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| <span class="mdl-layout-title toc">Table Of Contents</span> |
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| <li class="toctree-l1"><a class="reference internal" href="../../../tutorials/index.html">Python Tutorials</a><ul> |
| <li class="toctree-l2"><a class="reference internal" href="../../../tutorials/getting-started/index.html">Getting Started</a><ul> |
| <li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/index.html">Getting started with NP on MXNet</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/1-ndarray.html">Step 1: Manipulate data with NP on MXNet</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/2-nn.html">Step 2: Create a neural network</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/3-autograd.html">Step 3: Automatic differentiation with autograd</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/4-train.html">Step 4: Train the neural network</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-predict.html">Step 5: Predict with a pretrained model</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/6-use_gpus.html">Step 6: Use GPUs to increase efficiency</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/to-mxnet/index.html">Moving to MXNet from Other Frameworks</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/to-mxnet/pytorch.html">PyTorch vs Apache MXNet</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/gluon_from_experiment_to_deployment.html">Gluon: from experiment to deployment</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/logistic_regression_explained.html">Logistic regression explained</a></li> |
| <li class="toctree-l3"><a class="reference external" href="https://mxnet.apache.org/api/python/docs/tutorials/packages/gluon/image/mnist.html">MNIST</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l2"><a class="reference internal" href="../../../tutorials/packages/index.html">Packages</a><ul> |
| <li class="toctree-l3"><a class="reference internal" href="../../../tutorials/packages/autograd/index.html">Automatic Differentiation</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../tutorials/packages/gluon/index.html">Gluon</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/packages/gluon/blocks/index.html">Blocks</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/blocks/custom-layer.html">Custom Layers</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/blocks/custom_layer_beginners.html">Customer Layers (Beginners)</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/blocks/hybridize.html">Hybridize</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/blocks/init.html">Initialization</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/blocks/naming.html">Parameter and Block Naming</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/blocks/nn.html">Layers and Blocks</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/blocks/parameters.html">Parameter Management</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/blocks/save_load_params.html">Saving and Loading Gluon Models</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/blocks/activations/activations.html">Activation Blocks</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/packages/gluon/data/index.html">Data Tutorials</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/data/data_augmentation.html">Image Augmentation</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/data/data_augmentation.html#Spatial-Augmentation">Spatial Augmentation</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/data/data_augmentation.html#Color-Augmentation">Color Augmentation</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/data/data_augmentation.html#Composed-Augmentations">Composed Augmentations</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/data/datasets.html">Gluon <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/data/datasets.html#Using-own-data-with-included-Datasets">Using own data with included <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/data/datasets.html#Using-own-data-with-custom-Datasets">Using own data with custom <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/data/datasets.html#Appendix:-Upgrading-from-Module-DataIter-to-Gluon-DataLoader">Appendix: Upgrading from Module <code class="docutils literal notranslate"><span class="pre">DataIter</span></code> to Gluon <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li> |
| </ul> |
| </li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/packages/gluon/image/index.html">Image Tutorials</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/image/info_gan.html">Image similarity search with InfoGAN</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/image/mnist.html">Handwritten Digit Recognition</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/packages/gluon/loss/index.html">Losses</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/loss/custom-loss.html">Custom Loss Blocks</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/loss/kl_divergence.html">Kullback-Leibler (KL) Divergence</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/loss/loss.html">Loss functions</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/packages/gluon/text/index.html">Text Tutorials</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/text/gnmt.html">Google Neural Machine Translation</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/text/transformer.html">Machine Translation with Transformer</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/packages/gluon/training/index.html">Training</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/training/fit_api_tutorial.html">MXNet Gluon Fit API</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/training/trainer.html">Trainer</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/training/learning_rates/index.html">Learning Rates</a><ul> |
| <li class="toctree-l6"><a class="reference internal" href="../../../tutorials/packages/gluon/training/learning_rates/learning_rate_finder.html">Learning Rate Finder</a></li> |
| <li class="toctree-l6"><a class="reference internal" href="../../../tutorials/packages/gluon/training/learning_rates/learning_rate_schedules.html">Learning Rate Schedules</a></li> |
| <li class="toctree-l6"><a class="reference internal" href="../../../tutorials/packages/gluon/training/learning_rates/learning_rate_schedules_advanced.html">Advanced Learning Rate Schedules</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/training/normalization/index.html">Normalization Blocks</a></li> |
| </ul> |
| </li> |
| </ul> |
| </li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../tutorials/packages/kvstore/index.html">KVStore</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/packages/kvstore/kvstore.html">Distributed Key-Value Store</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../tutorials/packages/legacy/index.html">Legacy</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/packages/legacy/ndarray/index.html">NDArray</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/legacy/ndarray/01-ndarray-intro.html">An Intro: Manipulate Data the MXNet Way with NDArray</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/legacy/ndarray/02-ndarray-operations.html">NDArray Operations</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/legacy/ndarray/03-ndarray-contexts.html">NDArray Contexts</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/legacy/ndarray/gotchas_numpy_in_mxnet.html">Gotchas using NumPy in Apache MXNet</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/legacy/ndarray/sparse/index.html">Tutorials</a><ul> |
| <li class="toctree-l6"><a class="reference internal" href="../../../tutorials/packages/legacy/ndarray/sparse/csr.html">CSRNDArray - NDArray in Compressed Sparse Row Storage Format</a></li> |
| <li class="toctree-l6"><a class="reference internal" href="../../../tutorials/packages/legacy/ndarray/sparse/row_sparse.html">RowSparseNDArray - NDArray for Sparse Gradient Updates</a></li> |
| <li class="toctree-l6"><a class="reference internal" href="../../../tutorials/packages/legacy/ndarray/sparse/train_gluon.html">Sparse NDArrays with Gluon</a></li> |
| </ul> |
| </li> |
| </ul> |
| </li> |
| </ul> |
| </li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../tutorials/packages/np/index.html">What is NP on MXNet</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/packages/np/cheat-sheet.html">The NP on MXNet cheat sheet</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/packages/np/np-vs-numpy.html">Differences between NP on MXNet and NumPy</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../tutorials/packages/onnx/index.html">ONNX</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/packages/onnx/fine_tuning_gluon.html">Fine-tuning an ONNX model</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/packages/onnx/inference_on_onnx_model.html">Running inference on MXNet/Gluon from an ONNX model</a></li> |
| <li class="toctree-l4"><a class="reference external" href="https://mxnet.apache.org/api/python/docs/tutorials/deploy/export/onnx.html">Export ONNX Models</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../tutorials/packages/optimizer/index.html">Optimizers</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../tutorials/packages/viz/index.html">Visualization</a><ul> |
| <li class="toctree-l4"><a class="reference external" href="https://mxnet.apache.org/api/faq/visualize_graph">Visualize networks</a></li> |
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| <li class="toctree-l2"><a class="reference internal" href="../../../tutorials/performance/index.html">Performance</a><ul> |
| <li class="toctree-l3"><a class="reference internal" href="../../../tutorials/performance/compression/index.html">Compression</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/performance/compression/int8.html">Deploy with int-8</a></li> |
| <li class="toctree-l4"><a class="reference external" href="https://mxnet.apache.org/api/faq/float16">Float16</a></li> |
| <li class="toctree-l4"><a class="reference external" href="https://mxnet.apache.org/api/faq/gradient_compression">Gradient Compression</a></li> |
| <li class="toctree-l4"><a class="reference external" href="https://gluon-cv.mxnet.io/build/examples_deployment/int8_inference.html">GluonCV with Quantized Models</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../tutorials/performance/backend/index.html">Accelerated Backend Tools</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/performance/backend/mkldnn/index.html">Intel MKL-DNN</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/performance/backend/mkldnn/mkldnn_readme.html">Install MXNet with MKL-DNN</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/performance/backend/tensorrt/index.html">TensorRT</a><ul class="simple"> |
| </ul> |
| </li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/performance/backend/tvm.html">Use TVM</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/performance/backend/profiler.html">Profiling MXNet Models</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/performance/backend/amp.html">Using AMP: Automatic Mixed Precision</a></li> |
| </ul> |
| </li> |
| </ul> |
| </li> |
| <li class="toctree-l2"><a class="reference internal" href="../../../tutorials/deploy/index.html">Deployment</a><ul> |
| <li class="toctree-l3"><a class="reference internal" href="../../../tutorials/deploy/export/index.html">Export</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/deploy/export/onnx.html">Exporting to ONNX format</a></li> |
| <li class="toctree-l4"><a class="reference external" href="https://gluon-cv.mxnet.io/build/examples_deployment/export_network.html">Export Gluon CV Models</a></li> |
| <li class="toctree-l4"><a class="reference external" href="https://mxnet.apache.org/api/python/docs/tutorials/packages/gluon/blocks/save_load_params.html">Save / Load Parameters</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../tutorials/deploy/inference/index.html">Inference</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/deploy/inference/cpp.html">Deploy into C++</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/deploy/inference/image_classification_jetson.html">Image Classication using pretrained ResNet-50 model on Jetson module</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../tutorials/deploy/run-on-aws/index.html">Run on AWS</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/deploy/run-on-aws/use_ec2.html">Run on an EC2 Instance</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/deploy/run-on-aws/use_sagemaker.html">Run on Amazon SageMaker</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/deploy/run-on-aws/cloud.html">MXNet on the Cloud</a></li> |
| </ul> |
| </li> |
| </ul> |
| </li> |
| <li class="toctree-l2"><a class="reference internal" href="../../../tutorials/extend/index.html">Extend</a><ul> |
| <li class="toctree-l3"><a class="reference internal" href="../../../tutorials/extend/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> |
| </li> |
| </ul> |
| </li> |
| <li class="toctree-l1"><a class="reference internal" href="../../../api/index.html">Python API</a><ul> |
| <li class="toctree-l2"><a class="reference internal" href="../../../api/np/index.html">mxnet.np</a><ul> |
| <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><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.ndarray.html">mxnet.np.ndarray</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.ndarray.shape.html">mxnet.np.ndarray.shape</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.ndarray.ndim.html">mxnet.np.ndarray.ndim</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.ndarray.size.html">mxnet.np.ndarray.size</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.ndarray.dtype.html">mxnet.np.ndarray.dtype</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.ndarray.item.html">mxnet.np.ndarray.item</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.ndarray.copy.html">mxnet.np.ndarray.copy</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.ndarray.astype.html">mxnet.np.ndarray.astype</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.ndarray.reshape.html">mxnet.np.ndarray.reshape</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.ndarray.transpose.html">mxnet.np.ndarray.transpose</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.ndarray.swapaxes.html">mxnet.np.ndarray.swapaxes</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.ndarray.squeeze.html">mxnet.np.ndarray.squeeze</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.ndarray.nonzero.html">mxnet.np.ndarray.nonzero</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.ndarray.take.html">mxnet.np.ndarray.take</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.ndarray.repeat.html">mxnet.np.ndarray.repeat</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.ndarray.max.html">mxnet.np.ndarray.max</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.ndarray.argmax.html">mxnet.np.ndarray.argmax</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.ndarray.min.html">mxnet.np.ndarray.min</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.ndarray.argmin.html">mxnet.np.ndarray.argmin</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.ndarray.clip.html">mxnet.np.ndarray.clip</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.ndarray.sum.html">mxnet.np.ndarray.sum</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.ndarray.mean.html">mxnet.np.ndarray.mean</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.ndarray.prod.html">mxnet.np.ndarray.prod</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.ndarray.cumsum.html">mxnet.np.ndarray.cumsum</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.ndarray.var.html">mxnet.np.ndarray.var</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.ndarray.std.html">mxnet.np.ndarray.std</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.ndarray.__lt__.html">mxnet.np.ndarray.__lt__</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.ndarray.__le__.html">mxnet.np.ndarray.__le__</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.ndarray.__gt__.html">mxnet.np.ndarray.__gt__</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.ndarray.__ge__.html">mxnet.np.ndarray.__ge__</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.ndarray.__eq__.html">mxnet.np.ndarray.__eq__</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.ndarray.__ne__.html">mxnet.np.ndarray.__ne__</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.ndarray.__bool__.html">mxnet.np.ndarray.__bool__</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.ndarray.__neg__.html">mxnet.np.ndarray.__neg__</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.ndarray.__add__.html">mxnet.np.ndarray.__add__</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.ndarray.__sub__.html">mxnet.np.ndarray.__sub__</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.ndarray.__mul__.html">mxnet.np.ndarray.__mul__</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.ndarray.__truediv__.html">mxnet.np.ndarray.__truediv__</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.ndarray.__mod__.html">mxnet.np.ndarray.__mod__</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.ndarray.__pow__.html">mxnet.np.ndarray.__pow__</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.ndarray.__iadd__.html">mxnet.np.ndarray.__iadd__</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.ndarray.__isub__.html">mxnet.np.ndarray.__isub__</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.ndarray.__imul__.html">mxnet.np.ndarray.__imul__</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.ndarray.__itruediv__.html">mxnet.np.ndarray.__itruediv__</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.ndarray.__imod__.html">mxnet.np.ndarray.__imod__</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.ndarray.__reduce__.html">mxnet.np.ndarray.__reduce__</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.ndarray.__setstate__.html">mxnet.np.ndarray.__setstate__</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.ndarray.__len__.html">mxnet.np.ndarray.__len__</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.ndarray.__getitem__.html">mxnet.np.ndarray.__getitem__</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.ndarray.__setitem__.html">mxnet.np.ndarray.__setitem__</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.ndarray.__int__.html">mxnet.np.ndarray.__int__</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.ndarray.__float__.html">mxnet.np.ndarray.__float__</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.ndarray.__str__.html">mxnet.np.ndarray.__str__</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.ndarray.__repr__.html">mxnet.np.ndarray.__repr__</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../api/np/arrays.indexing.html">Indexing</a></li> |
| </ul> |
| </li> |
| <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> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.logspace.html">mxnet.np.logspace</a></li> |
| <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> |
| </ul> |
| </li> |
| <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.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> |
| <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.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.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.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> |
| </ul> |
| </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> |
| </ul> |
| </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.svd.html">mxnet.np.linalg.svd</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> |
| </ul> |
| </li> |
| <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> |
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| <li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/index.html">Getting started with NP on MXNet</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/1-ndarray.html">Step 1: Manipulate data with NP on MXNet</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/2-nn.html">Step 2: Create a neural network</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/3-autograd.html">Step 3: Automatic differentiation with autograd</a></li> |
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| <li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/to-mxnet/index.html">Moving to MXNet from Other Frameworks</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/to-mxnet/pytorch.html">PyTorch vs Apache MXNet</a></li> |
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| <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> |
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| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/blocks/custom_layer_beginners.html">Customer Layers (Beginners)</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/blocks/hybridize.html">Hybridize</a></li> |
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| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/data/data_augmentation.html#Spatial-Augmentation">Spatial Augmentation</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/data/data_augmentation.html#Color-Augmentation">Color Augmentation</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/data/data_augmentation.html#Composed-Augmentations">Composed Augmentations</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/data/datasets.html">Gluon <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/data/datasets.html#Using-own-data-with-included-Datasets">Using own data with included <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/data/datasets.html#Using-own-data-with-custom-Datasets">Using own data with custom <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/data/datasets.html#Appendix:-Upgrading-from-Module-DataIter-to-Gluon-DataLoader">Appendix: Upgrading from Module <code class="docutils literal notranslate"><span class="pre">DataIter</span></code> to Gluon <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li> |
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| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/image/info_gan.html">Image similarity search with InfoGAN</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/image/mnist.html">Handwritten Digit Recognition</a></li> |
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| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/packages/gluon/loss/index.html">Losses</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/loss/custom-loss.html">Custom Loss Blocks</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/loss/kl_divergence.html">Kullback-Leibler (KL) Divergence</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/loss/loss.html">Loss functions</a></li> |
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| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/packages/gluon/text/index.html">Text Tutorials</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/text/gnmt.html">Google Neural Machine Translation</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/text/transformer.html">Machine Translation with Transformer</a></li> |
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| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/packages/gluon/training/index.html">Training</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/training/fit_api_tutorial.html">MXNet Gluon Fit API</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/training/trainer.html">Trainer</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/training/learning_rates/index.html">Learning Rates</a><ul> |
| <li class="toctree-l6"><a class="reference internal" href="../../../tutorials/packages/gluon/training/learning_rates/learning_rate_finder.html">Learning Rate Finder</a></li> |
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| <li class="toctree-l6"><a class="reference internal" href="../../../tutorials/packages/gluon/training/learning_rates/learning_rate_schedules_advanced.html">Advanced Learning Rate Schedules</a></li> |
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| </li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/training/normalization/index.html">Normalization Blocks</a></li> |
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| </li> |
| </ul> |
| </li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../tutorials/packages/kvstore/index.html">KVStore</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/packages/kvstore/kvstore.html">Distributed Key-Value Store</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../tutorials/packages/legacy/index.html">Legacy</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/packages/legacy/ndarray/index.html">NDArray</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/legacy/ndarray/01-ndarray-intro.html">An Intro: Manipulate Data the MXNet Way with NDArray</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/legacy/ndarray/02-ndarray-operations.html">NDArray Operations</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/legacy/ndarray/03-ndarray-contexts.html">NDArray Contexts</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/legacy/ndarray/gotchas_numpy_in_mxnet.html">Gotchas using NumPy in Apache MXNet</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/legacy/ndarray/sparse/index.html">Tutorials</a><ul> |
| <li class="toctree-l6"><a class="reference internal" href="../../../tutorials/packages/legacy/ndarray/sparse/csr.html">CSRNDArray - NDArray in Compressed Sparse Row Storage Format</a></li> |
| <li class="toctree-l6"><a class="reference internal" href="../../../tutorials/packages/legacy/ndarray/sparse/row_sparse.html">RowSparseNDArray - NDArray for Sparse Gradient Updates</a></li> |
| <li class="toctree-l6"><a class="reference internal" href="../../../tutorials/packages/legacy/ndarray/sparse/train_gluon.html">Sparse NDArrays with Gluon</a></li> |
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| </li> |
| </ul> |
| </li> |
| </ul> |
| </li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../tutorials/packages/np/index.html">What is NP on MXNet</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/packages/np/cheat-sheet.html">The NP on MXNet cheat sheet</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/packages/np/np-vs-numpy.html">Differences between NP on MXNet and NumPy</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../tutorials/packages/onnx/index.html">ONNX</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/packages/onnx/fine_tuning_gluon.html">Fine-tuning an ONNX model</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/packages/onnx/inference_on_onnx_model.html">Running inference on MXNet/Gluon from an ONNX model</a></li> |
| <li class="toctree-l4"><a class="reference external" href="https://mxnet.apache.org/api/python/docs/tutorials/deploy/export/onnx.html">Export ONNX Models</a></li> |
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| <li class="toctree-l3"><a class="reference internal" href="../../../tutorials/packages/optimizer/index.html">Optimizers</a></li> |
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| <li class="toctree-l4"><a class="reference external" href="https://mxnet.apache.org/api/faq/visualize_graph">Visualize networks</a></li> |
| </ul> |
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| </ul> |
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| <li class="toctree-l2"><a class="reference internal" href="../../../tutorials/performance/index.html">Performance</a><ul> |
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| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/performance/compression/int8.html">Deploy with int-8</a></li> |
| <li class="toctree-l4"><a class="reference external" href="https://mxnet.apache.org/api/faq/float16">Float16</a></li> |
| <li class="toctree-l4"><a class="reference external" href="https://mxnet.apache.org/api/faq/gradient_compression">Gradient Compression</a></li> |
| <li class="toctree-l4"><a class="reference external" href="https://gluon-cv.mxnet.io/build/examples_deployment/int8_inference.html">GluonCV with Quantized Models</a></li> |
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| <li class="toctree-l3"><a class="reference internal" href="../../../tutorials/performance/backend/index.html">Accelerated Backend Tools</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/performance/backend/mkldnn/index.html">Intel MKL-DNN</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/performance/backend/mkldnn/mkldnn_readme.html">Install MXNet with MKL-DNN</a></li> |
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| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/performance/backend/tensorrt/index.html">TensorRT</a><ul class="simple"> |
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| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/performance/backend/tvm.html">Use TVM</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/performance/backend/profiler.html">Profiling MXNet Models</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/performance/backend/amp.html">Using AMP: Automatic Mixed Precision</a></li> |
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| <li class="toctree-l2"><a class="reference internal" href="../../../tutorials/deploy/index.html">Deployment</a><ul> |
| <li class="toctree-l3"><a class="reference internal" href="../../../tutorials/deploy/export/index.html">Export</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/deploy/export/onnx.html">Exporting to ONNX format</a></li> |
| <li class="toctree-l4"><a class="reference external" href="https://gluon-cv.mxnet.io/build/examples_deployment/export_network.html">Export Gluon CV Models</a></li> |
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| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/deploy/inference/image_classification_jetson.html">Image Classication using pretrained ResNet-50 model on Jetson module</a></li> |
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| <li class="toctree-l2"><a class="reference internal" href="../../../tutorials/extend/index.html">Extend</a><ul> |
| <li class="toctree-l3"><a class="reference internal" href="../../../tutorials/extend/customop.html">Custom Numpy Operators</a></li> |
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| <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> |
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| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.ndarray.html">mxnet.np.ndarray</a></li> |
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| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.ndarray.__eq__.html">mxnet.np.ndarray.__eq__</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.ndarray.__ne__.html">mxnet.np.ndarray.__ne__</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.ndarray.__bool__.html">mxnet.np.ndarray.__bool__</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.ndarray.__neg__.html">mxnet.np.ndarray.__neg__</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.ndarray.__add__.html">mxnet.np.ndarray.__add__</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.ndarray.__sub__.html">mxnet.np.ndarray.__sub__</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.ndarray.__mul__.html">mxnet.np.ndarray.__mul__</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.ndarray.__truediv__.html">mxnet.np.ndarray.__truediv__</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.ndarray.__mod__.html">mxnet.np.ndarray.__mod__</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.ndarray.__pow__.html">mxnet.np.ndarray.__pow__</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.ndarray.__iadd__.html">mxnet.np.ndarray.__iadd__</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.ndarray.__isub__.html">mxnet.np.ndarray.__isub__</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.ndarray.__imul__.html">mxnet.np.ndarray.__imul__</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.ndarray.__itruediv__.html">mxnet.np.ndarray.__itruediv__</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.ndarray.__imod__.html">mxnet.np.ndarray.__imod__</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.ndarray.__reduce__.html">mxnet.np.ndarray.__reduce__</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.ndarray.__setstate__.html">mxnet.np.ndarray.__setstate__</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.ndarray.__len__.html">mxnet.np.ndarray.__len__</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.ndarray.__getitem__.html">mxnet.np.ndarray.__getitem__</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.ndarray.__setitem__.html">mxnet.np.ndarray.__setitem__</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.ndarray.__int__.html">mxnet.np.ndarray.__int__</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.ndarray.__float__.html">mxnet.np.ndarray.__float__</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.ndarray.__str__.html">mxnet.np.ndarray.__str__</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.ndarray.__repr__.html">mxnet.np.ndarray.__repr__</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../api/np/arrays.indexing.html">Indexing</a></li> |
| </ul> |
| </li> |
| <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> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.logspace.html">mxnet.np.logspace</a></li> |
| <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> |
| </ul> |
| </li> |
| <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.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> |
| <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.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.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.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> |
| </ul> |
| </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> |
| </ul> |
| </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.svd.html">mxnet.np.linalg.svd</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> |
| </ul> |
| </li> |
| <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.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.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.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.ldexp.html">mxnet.np.ldexp</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.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> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.negative.html">mxnet.np.negative</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.divide.html">mxnet.np.divide</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.power.html">mxnet.np.power</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.subtract.html">mxnet.np.subtract</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.mod.html">mxnet.np.mod</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.multiply.html">mxnet.np.multiply</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.true_divide.html">mxnet.np.true_divide</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.remainder.html">mxnet.np.remainder</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.clip.html">mxnet.np.clip</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.sqrt.html">mxnet.np.sqrt</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.cbrt.html">mxnet.np.cbrt</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.square.html">mxnet.np.square</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.absolute.html">mxnet.np.absolute</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.sign.html">mxnet.np.sign</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.maximum.html">mxnet.np.maximum</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.minimum.html">mxnet.np.minimum</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../api/np/random/index.html">np.random</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../api/np/routines.sort.html">Sorting, searching, and counting</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.argmax.html">mxnet.np.argmax</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.argmin.html">mxnet.np.argmin</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../api/np/routines.statistics.html">Statistics</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.min.html">mxnet.np.min</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.max.html">mxnet.np.max</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.mean.html">mxnet.np.mean</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.std.html">mxnet.np.std</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.var.html">mxnet.np.var</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.histogram.html">mxnet.np.histogram</a></li> |
| </ul> |
| </li> |
| </ul> |
| </li> |
| </ul> |
| </li> |
| <li class="toctree-l2"><a class="reference internal" href="../../../api/npx/index.html">NPX: NumPy Neural Network Extension</a><ul> |
| <li class="toctree-l3"><a class="reference internal" href="../../../api/npx/generated/mxnet.npx.set_np.html">mxnet.npx.set_np</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../api/npx/generated/mxnet.npx.reset_np.html">mxnet.npx.reset_np</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../api/npx/generated/mxnet.npx.cpu.html">mxnet.npx.cpu</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../api/npx/generated/mxnet.npx.cpu_pinned.html">mxnet.npx.cpu_pinned</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../api/npx/generated/mxnet.npx.gpu.html">mxnet.npx.gpu</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../api/npx/generated/mxnet.npx.gpu_memory_info.html">mxnet.npx.gpu_memory_info</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../api/npx/generated/mxnet.npx.current_context.html">mxnet.npx.current_context</a></li> |
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| <h1>Source code for mxnet.optimizer.optimizer</h1><div class="highlight"><pre> |
| <span></span><span class="c1"># coding: utf-8</span> |
| <span class="c1"># Licensed to the Apache Software Foundation (ASF) under one</span> |
| <span class="c1"># or more contributor license agreements. See the NOTICE file</span> |
| <span class="c1"># distributed with this work for additional information</span> |
| <span class="c1"># regarding copyright ownership. The ASF licenses this file</span> |
| <span class="c1"># to you under the Apache License, Version 2.0 (the</span> |
| <span class="c1"># "License"); you may not use this file except in compliance</span> |
| <span class="c1"># with the License. You may obtain a copy of the License at</span> |
| <span class="c1">#</span> |
| <span class="c1"># http://www.apache.org/licenses/LICENSE-2.0</span> |
| <span class="c1">#</span> |
| <span class="c1"># Unless required by applicable law or agreed to in writing,</span> |
| <span class="c1"># software distributed under the License is distributed on an</span> |
| <span class="c1"># "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY</span> |
| <span class="c1"># KIND, either express or implied. See the License for the</span> |
| <span class="c1"># specific language governing permissions and limitations</span> |
| <span class="c1"># under the License.</span> |
| |
| <span class="c1"># pylint: disable=too-many-lines</span> |
| <span class="sd">"""Base Optimizer class."""</span> |
| <span class="kn">import</span> <span class="nn">warnings</span> |
| <span class="kn">import</span> <span class="nn">numpy</span> |
| <span class="kn">from</span> <span class="nn">..ndarray</span> <span class="kn">import</span> <span class="p">(</span><span class="n">NDArray</span><span class="p">,</span> <span class="n">zeros</span><span class="p">,</span> <span class="n">cast</span><span class="p">)</span> |
| <span class="kn">from</span> <span class="nn">..util</span> <span class="kn">import</span> <span class="n">is_np_array</span> |
| |
| <span class="n">__all__</span> <span class="o">=</span> <span class="p">[</span><span class="s1">'Optimizer'</span><span class="p">,</span> <span class="s1">'Test'</span><span class="p">,</span> <span class="s1">'create'</span><span class="p">,</span> <span class="s1">'register'</span><span class="p">]</span> |
| |
| |
| <div class="viewcode-block" id="Optimizer"><a class="viewcode-back" href="../../../api/optimizer/index.html#mxnet.optimizer.Optimizer">[docs]</a><span class="k">class</span> <span class="nc">Optimizer</span><span class="p">(</span><span class="nb">object</span><span class="p">):</span> |
| <span class="sd">"""The base class inherited by all optimizers.</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> rescale_grad : float, optional, default 1.0</span> |
| <span class="sd"> Multiply the gradient with `rescale_grad` before updating. Often</span> |
| <span class="sd"> choose to be ``1.0/batch_size``.</span> |
| |
| <span class="sd"> param_idx2name : dict from int to string, optional, default None</span> |
| <span class="sd"> A dictionary that maps int index to string name.</span> |
| |
| <span class="sd"> clip_gradient : float, optional, default None</span> |
| <span class="sd"> Clip the gradient by projecting onto the box ``[-clip_gradient, clip_gradient]``.</span> |
| |
| <span class="sd"> learning_rate : float, optional, default None</span> |
| <span class="sd"> The initial learning rate. If None, the optimization will use the</span> |
| <span class="sd"> learning rate from ``lr_scheduler``. If not None, it will overwrite</span> |
| <span class="sd"> the learning rate in ``lr_scheduler``. If None and ``lr_scheduler``</span> |
| <span class="sd"> is also None, then it will be set to 0.01 by default.</span> |
| |
| <span class="sd"> lr_scheduler : LRScheduler, optional, default None</span> |
| <span class="sd"> The learning rate scheduler.</span> |
| |
| <span class="sd"> wd : float, optional, default 0.0</span> |
| <span class="sd"> The weight decay (or L2 regularization) coefficient. Modifies objective</span> |
| <span class="sd"> by adding a penalty for having large weights.</span> |
| |
| <span class="sd"> sym: Symbol, optional, default None</span> |
| <span class="sd"> The Symbol this optimizer is applying to.</span> |
| |
| <span class="sd"> begin_num_update : int, optional, default 0</span> |
| <span class="sd"> The initial number of updates.</span> |
| |
| <span class="sd"> multi_precision : bool, optional, default False</span> |
| <span class="sd"> Flag to control the internal precision of the optimizer.</span> |
| <span class="sd"> False: results in using the same precision as the weights (default),</span> |
| <span class="sd"> True: makes internal 32-bit copy of the weights and applies gradients</span> |
| <span class="sd"> in 32-bit precision even if actual weights used in the model have lower precision.</span> |
| <span class="sd"> Turning this on can improve convergence and accuracy when training with float16.</span> |
| |
| <span class="sd"> param_dict : dict of int -> gluon.Parameter, default None</span> |
| <span class="sd"> Dictionary of parameter index to gluon.Parameter, used to lookup parameter attributes</span> |
| <span class="sd"> such as lr_mult, wd_mult, etc. param_dict shall not be deep copied.</span> |
| |
| <span class="sd"> aggregate_num : int, optional, default None</span> |
| <span class="sd"> Number of weights to be aggregated in a list.</span> |
| <span class="sd"> They are passed to the optimizer for a single optimization step.</span> |
| <span class="sd"> In default, only one weight is aggregated.</span> |
| <span class="sd"> When `aggregate_num` is set to numpy.inf, all the weights are aggregated.</span> |
| |
| <span class="sd"> use_fused_step : bool, optional, default None</span> |
| <span class="sd"> Whether or not to use fused kernels for optimizer.</span> |
| <span class="sd"> When use_fused_step=False, step is called,</span> |
| <span class="sd"> otherwise, fused_step is called.</span> |
| |
| <span class="sd"> Properties</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> learning_rate : float</span> |
| <span class="sd"> The current learning rate of the optimizer. Given an Optimizer object</span> |
| <span class="sd"> optimizer, its learning rate can be accessed as optimizer.learning_rate.</span> |
| <span class="sd"> """</span> |
| <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">rescale_grad</span><span class="o">=</span><span class="mf">1.</span><span class="p">,</span> <span class="n">param_idx2name</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">wd</span><span class="o">=</span><span class="mf">0.</span><span class="p">,</span> |
| <span class="n">clip_gradient</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">learning_rate</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> |
| <span class="n">lr_scheduler</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">sym</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">begin_num_update</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> |
| <span class="n">multi_precision</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">param_dict</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">aggregate_num</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> |
| <span class="n">use_fused_step</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span> |
| <span class="nb">super</span><span class="p">(</span><span class="n">Optimizer</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="o">**</span><span class="n">kwargs</span><span class="p">)</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">rescale_grad</span> <span class="o">=</span> <span class="n">rescale_grad</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">lr_scheduler</span> <span class="o">=</span> <span class="n">lr_scheduler</span> |
| <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">lr_scheduler</span> <span class="ow">is</span> <span class="kc">None</span> <span class="ow">and</span> <span class="n">learning_rate</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span> |
| <span class="n">learning_rate</span> <span class="o">=</span> <span class="mf">0.01</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">lr</span> <span class="o">=</span> <span class="n">learning_rate</span> |
| <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">lr_scheduler</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="ow">and</span> <span class="n">learning_rate</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span> |
| <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">lr_scheduler</span><span class="o">.</span><span class="n">base_lr</span> <span class="o">!=</span> <span class="n">learning_rate</span><span class="p">:</span> |
| <span class="nb">print</span><span class="p">(</span><span class="ne">UserWarning</span><span class="p">(</span><span class="s2">"learning rate from ``lr_scheduler`` has been "</span> |
| <span class="s2">"overwritten by ``learning_rate`` in optimizer."</span><span class="p">))</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">lr_scheduler</span><span class="o">.</span><span class="n">base_lr</span> <span class="o">=</span> <span class="n">learning_rate</span> |
| |
| <span class="bp">self</span><span class="o">.</span><span class="n">wd</span> <span class="o">=</span> <span class="n">wd</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">lr_mult</span> <span class="o">=</span> <span class="p">{}</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">wd_mult</span> <span class="o">=</span> <span class="p">{}</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">begin_num_update</span> <span class="o">=</span> <span class="n">begin_num_update</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">num_update</span> <span class="o">=</span> <span class="n">begin_num_update</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_all_index_update_counts</span> <span class="o">=</span> <span class="p">{</span><span class="mi">0</span> <span class="p">:</span> <span class="p">{}}</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_index_update_count</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_all_index_update_counts</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">clip_gradient</span> <span class="o">=</span> <span class="n">clip_gradient</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">multi_precision</span> <span class="o">=</span> <span class="n">multi_precision</span> |
| |
| <span class="k">if</span> <span class="n">aggregate_num</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">aggregate_num</span> <span class="o">=</span> <span class="mi">1</span> |
| <span class="k">else</span><span class="p">:</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">aggregate_num</span> <span class="o">=</span> <span class="n">aggregate_num</span> |
| |
| <span class="k">if</span> <span class="n">param_idx2name</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span> |
| <span class="n">param_idx2name</span> <span class="o">=</span> <span class="p">{}</span> |
| <span class="k">assert</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">param_idx2name</span><span class="p">,</span> <span class="nb">dict</span><span class="p">),</span> \ |
| <span class="s1">'param_idx2name should be a dict of param indexes to names.'</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">idx2name</span> <span class="o">=</span> <span class="n">param_idx2name</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">sym_info</span> <span class="o">=</span> <span class="p">(</span><span class="n">sym</span><span class="o">.</span><span class="n">attr_dict</span><span class="p">(),</span> <span class="n">sym</span><span class="o">.</span><span class="n">list_arguments</span><span class="p">())</span> <span class="k">if</span> <span class="n">sym</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="k">else</span> <span class="p">()</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">param_dict</span> <span class="o">=</span> <span class="n">param_dict</span> <span class="k">if</span> <span class="n">param_dict</span> <span class="k">else</span> <span class="p">{}</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">allow_np_array</span> <span class="o">=</span> <span class="n">is_np_array</span><span class="p">()</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">use_fused_step</span> <span class="o">=</span> <span class="n">use_fused_step</span> \ |
| <span class="k">if</span> <span class="n">use_fused_step</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="k">else</span> <span class="kc">False</span> |
| |
| <span class="bp">self</span><span class="o">.</span><span class="n">set_lr_mult</span><span class="p">({})</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">set_wd_mult</span><span class="p">({})</span> |
| |
| <span class="n">opt_registry</span> <span class="o">=</span> <span class="p">{}</span> |
| |
| <div class="viewcode-block" id="Optimizer.register"><a class="viewcode-back" href="../../../api/optimizer/index.html#mxnet.optimizer.Optimizer.register">[docs]</a> <span class="nd">@staticmethod</span> |
| <span class="k">def</span> <span class="nf">register</span><span class="p">(</span><span class="n">klass</span><span class="p">):</span> |
| <span class="sd">"""Registers a new optimizer.</span> |
| |
| <span class="sd"> Once an optimizer is registered, we can create an instance of this</span> |
| <span class="sd"> optimizer with `create_optimizer` later.</span> |
| |
| <span class="sd"> Examples</span> |
| <span class="sd"> --------</span> |
| |
| <span class="sd"> >>> @mx.optimizer.Optimizer.register</span> |
| <span class="sd"> ... class MyOptimizer(mx.optimizer.Optimizer):</span> |
| <span class="sd"> ... pass</span> |
| <span class="sd"> >>> optim = mx.optimizer.Optimizer.create_optimizer('MyOptimizer')</span> |
| <span class="sd"> >>> print(type(optim))</span> |
| <span class="sd"> <class '__main__.MyOptimizer'></span> |
| <span class="sd"> """</span> |
| <span class="k">assert</span><span class="p">(</span><span class="nb">isinstance</span><span class="p">(</span><span class="n">klass</span><span class="p">,</span> <span class="nb">type</span><span class="p">))</span> |
| <span class="n">name</span> <span class="o">=</span> <span class="n">klass</span><span class="o">.</span><span class="vm">__name__</span><span class="o">.</span><span class="n">lower</span><span class="p">()</span> |
| <span class="k">if</span> <span class="n">name</span> <span class="ow">in</span> <span class="n">Optimizer</span><span class="o">.</span><span class="n">opt_registry</span><span class="p">:</span> |
| <span class="n">warnings</span><span class="o">.</span><span class="n">warn</span><span class="p">(</span><span class="s1">'WARNING: New optimizer </span><span class="si">%s</span><span class="s1">.</span><span class="si">%s</span><span class="s1"> is overriding '</span> |
| <span class="s1">'existing optimizer </span><span class="si">%s</span><span class="s1">.</span><span class="si">%s</span><span class="s1">'</span> <span class="o">%</span> |
| <span class="p">(</span><span class="n">klass</span><span class="o">.</span><span class="vm">__module__</span><span class="p">,</span> <span class="n">klass</span><span class="o">.</span><span class="vm">__name__</span><span class="p">,</span> |
| <span class="n">Optimizer</span><span class="o">.</span><span class="n">opt_registry</span><span class="p">[</span><span class="n">name</span><span class="p">]</span><span class="o">.</span><span class="vm">__module__</span><span class="p">,</span> |
| <span class="n">Optimizer</span><span class="o">.</span><span class="n">opt_registry</span><span class="p">[</span><span class="n">name</span><span class="p">]</span><span class="o">.</span><span class="vm">__name__</span><span class="p">))</span> |
| <span class="n">Optimizer</span><span class="o">.</span><span class="n">opt_registry</span><span class="p">[</span><span class="n">name</span><span class="p">]</span> <span class="o">=</span> <span class="n">klass</span> |
| <span class="k">return</span> <span class="n">klass</span></div> |
| |
| <div class="viewcode-block" id="Optimizer.create_optimizer"><a class="viewcode-back" href="../../../api/optimizer/index.html#mxnet.optimizer.Optimizer.create_optimizer">[docs]</a> <span class="nd">@staticmethod</span> |
| <span class="k">def</span> <span class="nf">create_optimizer</span><span class="p">(</span><span class="n">name</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span> |
| <span class="sd">"""Instantiates an optimizer with a given name and kwargs.</span> |
| |
| <span class="sd"> .. note:: We can use the alias `create` for ``Optimizer.create_optimizer``.</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> name: str</span> |
| <span class="sd"> Name of the optimizer. Should be the name</span> |
| <span class="sd"> of a subclass of Optimizer. Case insensitive.</span> |
| |
| <span class="sd"> kwargs: dict</span> |
| <span class="sd"> Parameters for the optimizer.</span> |
| |
| <span class="sd"> Returns</span> |
| <span class="sd"> -------</span> |
| <span class="sd"> Optimizer</span> |
| <span class="sd"> An instantiated optimizer.</span> |
| |
| <span class="sd"> Examples</span> |
| <span class="sd"> --------</span> |
| <span class="sd"> >>> sgd = mx.optimizer.Optimizer.create_optimizer('sgd')</span> |
| <span class="sd"> >>> type(sgd)</span> |
| <span class="sd"> <class 'mxnet.optimizer.SGD'></span> |
| <span class="sd"> >>> adam = mx.optimizer.create('adam', learning_rate=.1)</span> |
| <span class="sd"> >>> type(adam)</span> |
| <span class="sd"> <class 'mxnet.optimizer.Adam'></span> |
| <span class="sd"> """</span> |
| <span class="k">if</span> <span class="n">name</span><span class="o">.</span><span class="n">lower</span><span class="p">()</span> <span class="ow">in</span> <span class="n">Optimizer</span><span class="o">.</span><span class="n">opt_registry</span><span class="p">:</span> |
| <span class="k">return</span> <span class="n">Optimizer</span><span class="o">.</span><span class="n">opt_registry</span><span class="p">[</span><span class="n">name</span><span class="o">.</span><span class="n">lower</span><span class="p">()](</span><span class="o">**</span><span class="n">kwargs</span><span class="p">)</span> |
| <span class="k">else</span><span class="p">:</span> |
| <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">'Cannot find optimizer </span><span class="si">%s</span><span class="s1">'</span> <span class="o">%</span> <span class="n">name</span><span class="p">)</span></div> |
| |
| <span class="nd">@property</span> |
| <span class="k">def</span> <span class="nf">learning_rate</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span> |
| <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">lr_scheduler</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span> |
| <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">lr_scheduler</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">num_update</span><span class="p">)</span> |
| <span class="k">else</span><span class="p">:</span> |
| <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">lr</span> |
| |
| <div class="viewcode-block" id="Optimizer.create_state"><a class="viewcode-back" href="../../../api/optimizer/index.html#mxnet.optimizer.Optimizer.create_state">[docs]</a> <span class="k">def</span> <span class="nf">create_state</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">index</span><span class="p">,</span> <span class="n">weight</span><span class="p">):</span> |
| <span class="sd">"""Creates auxiliary state for a given weight.</span> |
| |
| <span class="sd"> Some optimizers require additional states, e.g. as momentum, in addition</span> |
| <span class="sd"> to gradients in order to update weights. This function creates state</span> |
| <span class="sd"> for a given weight which will be used in `update`. This function is</span> |
| <span class="sd"> called only once for each weight.</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> index : int</span> |
| <span class="sd"> An unique index to identify the weight.</span> |
| <span class="sd"> weight : NDArray</span> |
| <span class="sd"> The weight.</span> |
| |
| <span class="sd"> Returns</span> |
| <span class="sd"> -------</span> |
| <span class="sd"> state : any obj</span> |
| <span class="sd"> The state associated with the weight.</span> |
| <span class="sd"> """</span></div> |
| |
| <div class="viewcode-block" id="Optimizer.create_state_multi_precision"><a class="viewcode-back" href="../../../api/optimizer/index.html#mxnet.optimizer.Optimizer.create_state_multi_precision">[docs]</a> <span class="k">def</span> <span class="nf">create_state_multi_precision</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">index</span><span class="p">,</span> <span class="n">weight</span><span class="p">):</span> |
| <span class="sd">"""Creates auxiliary state for a given weight, including FP32 high</span> |
| <span class="sd"> precision copy if original weight is FP16.</span> |
| |
| <span class="sd"> This method is provided to perform automatic mixed precision training</span> |
| <span class="sd"> for optimizers that do not support it themselves.</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> index : int</span> |
| <span class="sd"> An unique index to identify the weight.</span> |
| <span class="sd"> weight : NDArray</span> |
| <span class="sd"> The weight.</span> |
| |
| <span class="sd"> Returns</span> |
| <span class="sd"> -------</span> |
| <span class="sd"> state : any obj</span> |
| <span class="sd"> The state associated with the weight.</span> |
| <span class="sd"> """</span> |
| <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">multi_precision</span> <span class="ow">and</span> <span class="n">weight</span><span class="o">.</span><span class="n">dtype</span> <span class="o">==</span> <span class="n">numpy</span><span class="o">.</span><span class="n">float16</span><span class="p">:</span> |
| <span class="n">weight_master_copy</span> <span class="o">=</span> <span class="n">weight</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="n">numpy</span><span class="o">.</span><span class="n">float32</span><span class="p">)</span> |
| <span class="k">return</span> <span class="p">(</span><span class="n">weight_master_copy</span><span class="p">,)</span> <span class="o">+</span> <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">create_state</span><span class="p">(</span><span class="n">index</span><span class="p">,</span> <span class="n">weight_master_copy</span><span class="p">),)</span> |
| <span class="k">if</span> <span class="n">weight</span><span class="o">.</span><span class="n">dtype</span> <span class="o">==</span> <span class="n">numpy</span><span class="o">.</span><span class="n">float16</span> <span class="ow">and</span> <span class="ow">not</span> <span class="bp">self</span><span class="o">.</span><span class="n">multi_precision</span><span class="p">:</span> |
| <span class="n">warnings</span><span class="o">.</span><span class="n">warn</span><span class="p">(</span><span class="s2">"Accumulating with float16 in optimizer can lead to "</span> |
| <span class="s2">"poor accuracy or slow convergence. "</span> |
| <span class="s2">"Consider using multi_precision=True option of the "</span> |
| <span class="s2">"optimizer"</span><span class="p">)</span> |
| <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">create_state</span><span class="p">(</span><span class="n">index</span><span class="p">,</span> <span class="n">weight</span><span class="p">)</span></div> |
| |
| <div class="viewcode-block" id="Optimizer.step"><a class="viewcode-back" href="../../../api/optimizer/index.html#mxnet.optimizer.Optimizer.step">[docs]</a> <span class="k">def</span> <span class="nf">step</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">indices</span><span class="p">,</span> <span class="n">weights</span><span class="p">,</span> <span class="n">grads</span><span class="p">,</span> <span class="n">states</span><span class="p">):</span> |
| <span class="sd">"""Perform an optimization step using gradients and states.</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> indices : list of int</span> |
| <span class="sd"> List of unique indices of the parameters into the individual learning rates</span> |
| <span class="sd"> and weight decays. Learning rates and weight decay may be set via `set_lr_mult()`</span> |
| <span class="sd"> and `set_wd_mult()`, respectively.</span> |
| <span class="sd"> weights : list of NDArray</span> |
| <span class="sd"> List of parameters to be updated.</span> |
| <span class="sd"> grads : list of NDArray</span> |
| <span class="sd"> List of gradients of the objective with respect to this parameter.</span> |
| <span class="sd"> states : List of any obj</span> |
| <span class="sd"> List of state returned by `create_state()`.</span> |
| <span class="sd"> """</span> |
| <span class="k">raise</span> <span class="ne">NotImplementedError</span></div> |
| |
| <div class="viewcode-block" id="Optimizer.fused_step"><a class="viewcode-back" href="../../../api/optimizer/index.html#mxnet.optimizer.Optimizer.fused_step">[docs]</a> <span class="k">def</span> <span class="nf">fused_step</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">indices</span><span class="p">,</span> <span class="n">weights</span><span class="p">,</span> <span class="n">grads</span><span class="p">,</span> <span class="n">states</span><span class="p">):</span> |
| <span class="sd">"""Perform a fused optimization step using gradients and states.</span> |
| <span class="sd"> New operators that fuses optimizer's update should be put in this function.</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> indices : list of int</span> |
| <span class="sd"> List of unique indices of the parameters into the individual learning rates</span> |
| <span class="sd"> and weight decays. Learning rates and weight decay may be set via `set_lr_mult()`</span> |
| <span class="sd"> and `set_wd_mult()`, respectively.</span> |
| <span class="sd"> weights : list of NDArray</span> |
| <span class="sd"> List of parameters to be updated.</span> |
| <span class="sd"> grads : list of NDArray</span> |
| <span class="sd"> List of gradients of the objective with respect to this parameter.</span> |
| <span class="sd"> states : List of any obj</span> |
| <span class="sd"> List of state returned by `create_state()`.</span> |
| <span class="sd"> """</span> |
| <span class="k">raise</span> <span class="ne">NotImplementedError</span></div> |
| |
| <div class="viewcode-block" id="Optimizer.update"><a class="viewcode-back" href="../../../api/optimizer/index.html#mxnet.optimizer.Optimizer.update">[docs]</a> <span class="k">def</span> <span class="nf">update</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">indices</span><span class="p">,</span> <span class="n">weights</span><span class="p">,</span> <span class="n">grads</span><span class="p">,</span> <span class="n">states</span><span class="p">):</span> |
| <span class="sd">"""Call step to perform a single optimization update if use_fused_step is False,</span> |
| <span class="sd"> otherwise fused_step is called.</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> indices : list of int</span> |
| <span class="sd"> List of unique indices of the parameters into the individual learning rates</span> |
| <span class="sd"> and weight decays. Learning rates and weight decay may be set via `set_lr_mult()`</span> |
| <span class="sd"> and `set_wd_mult()`, respectively.</span> |
| <span class="sd"> weights : list of NDArray</span> |
| <span class="sd"> List of parameters to be updated.</span> |
| <span class="sd"> grads : list of NDArray</span> |
| <span class="sd"> List of gradients of the objective with respect to this parameter.</span> |
| <span class="sd"> states : List of any obj</span> |
| <span class="sd"> List of state returned by `create_state()`.</span> |
| <span class="sd"> """</span> |
| <span class="k">for</span> <span class="n">weight</span><span class="p">,</span> <span class="n">grad</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="n">weights</span><span class="p">,</span> <span class="n">grads</span><span class="p">):</span> |
| <span class="k">assert</span><span class="p">(</span><span class="nb">isinstance</span><span class="p">(</span><span class="n">weight</span><span class="p">,</span> <span class="n">NDArray</span><span class="p">))</span> |
| <span class="k">assert</span><span class="p">(</span><span class="nb">isinstance</span><span class="p">(</span><span class="n">grad</span><span class="p">,</span> <span class="n">NDArray</span><span class="p">))</span> |
| <span class="k">if</span> <span class="ow">not</span> <span class="bp">self</span><span class="o">.</span><span class="n">use_fused_step</span><span class="p">:</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">step</span><span class="p">(</span><span class="n">indices</span><span class="p">,</span> <span class="n">weights</span><span class="p">,</span> <span class="n">grads</span><span class="p">,</span> <span class="n">states</span><span class="p">)</span> |
| <span class="k">else</span><span class="p">:</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">fused_step</span><span class="p">(</span><span class="n">indices</span><span class="p">,</span> <span class="n">weights</span><span class="p">,</span> <span class="n">grads</span><span class="p">,</span> <span class="n">states</span><span class="p">)</span></div> |
| |
| <div class="viewcode-block" id="Optimizer.update_multi_precision"><a class="viewcode-back" href="../../../api/optimizer/index.html#mxnet.optimizer.Optimizer.update_multi_precision">[docs]</a> <span class="k">def</span> <span class="nf">update_multi_precision</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">indices</span><span class="p">,</span> <span class="n">weights</span><span class="p">,</span> <span class="n">grads</span><span class="p">,</span> <span class="n">states</span><span class="p">):</span> |
| <span class="sd">"""Call step to perform a single optimization update if use_fused_step is False,</span> |
| <span class="sd"> otherwise fused_step is called. Mixed precision version.</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> indices : list of int</span> |
| <span class="sd"> List of unique indices of the parameters into the individual learning rates</span> |
| <span class="sd"> and weight decays. Learning rates and weight decay may be set via `set_lr_mult()`</span> |
| <span class="sd"> and `set_wd_mult()`, respectively.</span> |
| <span class="sd"> weights : list of NDArray</span> |
| <span class="sd"> List of parameters to be updated.</span> |
| <span class="sd"> grads : list of NDArray</span> |
| <span class="sd"> List of gradients of the objective with respect to this parameter.</span> |
| <span class="sd"> states : List of any obj</span> |
| <span class="sd"> List of state returned by `create_state()`.</span> |
| <span class="sd"> """</span> |
| <span class="n">weights_master_copy</span> <span class="o">=</span> <span class="p">[]</span> |
| <span class="n">original_states</span> <span class="o">=</span> <span class="p">[]</span> |
| <span class="n">grads32</span> <span class="o">=</span> <span class="p">[]</span> |
| <span class="k">for</span> <span class="n">weight</span><span class="p">,</span> <span class="n">grad</span><span class="p">,</span> <span class="n">state</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="n">weights</span><span class="p">,</span> <span class="n">grads</span><span class="p">,</span> <span class="n">states</span><span class="p">):</span> |
| <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">multi_precision</span> <span class="ow">and</span> <span class="n">weight</span><span class="o">.</span><span class="n">dtype</span> <span class="o">==</span> <span class="n">numpy</span><span class="o">.</span><span class="n">float16</span><span class="p">:</span> |
| <span class="n">weights_master_copy</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">state</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span> |
| <span class="n">original_states</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">state</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span> |
| <span class="n">grads32</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">grad</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="n">numpy</span><span class="o">.</span><span class="n">float32</span><span class="p">))</span> |
| <span class="k">else</span><span class="p">:</span> |
| <span class="n">weights_master_copy</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">weight</span><span class="p">)</span> |
| <span class="n">original_states</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">state</span><span class="p">)</span> |
| <span class="n">grads32</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">grad</span><span class="p">)</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">update</span><span class="p">(</span><span class="n">indices</span><span class="p">,</span> <span class="n">weights_master_copy</span><span class="p">,</span> <span class="n">grads32</span><span class="p">,</span> <span class="n">original_states</span><span class="p">)</span> |
| <span class="k">for</span> <span class="n">weight_master_copy</span><span class="p">,</span> <span class="n">weight</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="n">weights_master_copy</span><span class="p">,</span> <span class="n">weights</span><span class="p">):</span> |
| <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">multi_precision</span> <span class="ow">and</span> <span class="n">weight</span><span class="o">.</span><span class="n">dtype</span> <span class="o">==</span> <span class="n">numpy</span><span class="o">.</span><span class="n">float16</span><span class="p">:</span> |
| <span class="n">cast</span><span class="p">(</span><span class="n">weight_master_copy</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">weight</span><span class="o">.</span><span class="n">dtype</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="n">weight</span><span class="p">)</span></div> |
| |
| <div class="viewcode-block" id="Optimizer.set_learning_rate"><a class="viewcode-back" href="../../../api/optimizer/index.html#mxnet.optimizer.Optimizer.set_learning_rate">[docs]</a> <span class="k">def</span> <span class="nf">set_learning_rate</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">lr</span><span class="p">):</span> |
| <span class="sd">"""Sets a new learning rate of the optimizer.</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> lr : float</span> |
| <span class="sd"> The new learning rate of the optimizer.</span> |
| <span class="sd"> """</span> |
| <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">lr_scheduler</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span> <span class="c1"># pylint: disable=no-else-raise</span> |
| <span class="k">raise</span> <span class="ne">UserWarning</span><span class="p">(</span><span class="s2">"LRScheduler of the optimizer has already been "</span> |
| <span class="s2">"defined. Note that set_learning_rate can mutate "</span> |
| <span class="s2">"the value of the learning rate of the optimizer "</span> |
| <span class="s2">"only when the LRScheduler of the optimizer is "</span> |
| <span class="s2">"undefined."</span><span class="p">)</span> |
| <span class="k">else</span><span class="p">:</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">lr</span> <span class="o">=</span> <span class="n">lr</span></div> |
| |
| <div class="viewcode-block" id="Optimizer.set_lr_mult"><a class="viewcode-back" href="../../../api/optimizer/index.html#mxnet.optimizer.Optimizer.set_lr_mult">[docs]</a> <span class="k">def</span> <span class="nf">set_lr_mult</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">args_lr_mult</span><span class="p">):</span> |
| <span class="sd">"""Sets an individual learning rate multiplier for each parameter.</span> |
| |
| <span class="sd"> If you specify a learning rate multiplier for a parameter, then</span> |
| <span class="sd"> the learning rate for the parameter will be set as the product of</span> |
| <span class="sd"> the global learning rate `self.lr` and its multiplier.</span> |
| |
| <span class="sd"> .. note:: The default learning rate multiplier of a `Variable`</span> |
| <span class="sd"> can be set with `lr_mult` argument in the constructor.</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> args_lr_mult : dict of str/int to float</span> |
| <span class="sd"> For each of its key-value entries, the learning rate multipler for the</span> |
| <span class="sd"> parameter specified in the key will be set as the given value.</span> |
| |
| <span class="sd"> You can specify the parameter with either its name or its index.</span> |
| <span class="sd"> If you use the name, you should pass `sym` in the constructor,</span> |
| <span class="sd"> and the name you specified in the key of `args_lr_mult` should match</span> |
| <span class="sd"> the name of the parameter in `sym`. If you use the index, it should</span> |
| <span class="sd"> correspond to the index of the parameter used in the `update` method.</span> |
| |
| <span class="sd"> Specifying a parameter by its index is only supported for backward</span> |
| <span class="sd"> compatibility, and we recommend to use the name instead.</span> |
| <span class="sd"> """</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">lr_mult</span> <span class="o">=</span> <span class="p">{}</span> |
| <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">sym_info</span><span class="p">:</span> |
| <span class="n">attr</span><span class="p">,</span> <span class="n">arg_names</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">sym_info</span> |
| <span class="k">for</span> <span class="n">name</span> <span class="ow">in</span> <span class="n">arg_names</span><span class="p">:</span> |
| <span class="k">if</span> <span class="n">name</span> <span class="ow">in</span> <span class="n">attr</span> <span class="ow">and</span> <span class="s1">'__lr_mult__'</span> <span class="ow">in</span> <span class="n">attr</span><span class="p">[</span><span class="n">name</span><span class="p">]:</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">lr_mult</span><span class="p">[</span><span class="n">name</span><span class="p">]</span> <span class="o">=</span> <span class="nb">float</span><span class="p">(</span><span class="n">attr</span><span class="p">[</span><span class="n">name</span><span class="p">][</span><span class="s1">'__lr_mult__'</span><span class="p">])</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">lr_mult</span><span class="o">.</span><span class="n">update</span><span class="p">(</span><span class="n">args_lr_mult</span><span class="p">)</span></div> |
| |
| <div class="viewcode-block" id="Optimizer.set_wd_mult"><a class="viewcode-back" href="../../../api/optimizer/index.html#mxnet.optimizer.Optimizer.set_wd_mult">[docs]</a> <span class="k">def</span> <span class="nf">set_wd_mult</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">args_wd_mult</span><span class="p">):</span> |
| <span class="sd">"""Sets an individual weight decay multiplier for each parameter.</span> |
| |
| <span class="sd"> .. note:: The default weight decay multiplier for a `Variable`</span> |
| <span class="sd"> can be set with its `wd_mult` argument in the constructor.</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> args_wd_mult : dict of string/int to float</span> |
| <span class="sd"> For each of its key-value entries, the weight decay multipler for the</span> |
| <span class="sd"> parameter specified in the key will be set as the given value.</span> |
| |
| <span class="sd"> You can specify the parameter with either its name or its index.</span> |
| <span class="sd"> If you use the name, you should pass `sym` in the constructor,</span> |
| <span class="sd"> and the name you specified in the key of `args_lr_mult` should match</span> |
| <span class="sd"> the name of the parameter in `sym`. If you use the index, it should</span> |
| <span class="sd"> correspond to the index of the parameter used in the `update` method.</span> |
| |
| <span class="sd"> Specifying a parameter by its index is only supported for backward</span> |
| <span class="sd"> compatibility, and we recommend to use the name instead.</span> |
| <span class="sd"> """</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">wd_mult</span> <span class="o">=</span> <span class="p">{}</span> |
| <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">sym_info</span><span class="p">:</span> |
| <span class="n">attr</span><span class="p">,</span> <span class="n">arg_names</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">sym_info</span> |
| <span class="k">for</span> <span class="n">name</span> <span class="ow">in</span> <span class="n">arg_names</span><span class="p">:</span> |
| <span class="k">if</span> <span class="n">name</span> <span class="ow">in</span> <span class="n">attr</span> <span class="ow">and</span> <span class="s1">'__wd_mult__'</span> <span class="ow">in</span> <span class="n">attr</span><span class="p">[</span><span class="n">name</span><span class="p">]:</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">wd_mult</span><span class="p">[</span><span class="n">name</span><span class="p">]</span> <span class="o">=</span> <span class="nb">float</span><span class="p">(</span><span class="n">attr</span><span class="p">[</span><span class="n">name</span><span class="p">][</span><span class="s1">'__wd_mult__'</span><span class="p">])</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">wd_mult</span><span class="o">.</span><span class="n">update</span><span class="p">(</span><span class="n">args_wd_mult</span><span class="p">)</span></div> |
| |
| <span class="k">def</span> <span class="nf">_set_current_context</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">device_id</span><span class="p">):</span> |
| <span class="sd">"""Sets the number of the currently handled device.</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> device_id : int</span> |
| <span class="sd"> The number of current device.</span> |
| <span class="sd"> """</span> |
| <span class="k">if</span> <span class="n">device_id</span> <span class="ow">not</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">_all_index_update_counts</span><span class="p">:</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_all_index_update_counts</span><span class="p">[</span><span class="n">device_id</span><span class="p">]</span> <span class="o">=</span> <span class="p">{}</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_index_update_count</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_all_index_update_counts</span><span class="p">[</span><span class="n">device_id</span><span class="p">]</span> |
| |
| <span class="k">def</span> <span class="nf">_update_count</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">index</span><span class="p">):</span> |
| <span class="sd">"""Updates num_update.</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> index : int or list of int</span> |
| <span class="sd"> The index to be updated.</span> |
| <span class="sd"> """</span> |
| <span class="k">if</span> <span class="ow">not</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">index</span><span class="p">,</span> <span class="p">(</span><span class="nb">list</span><span class="p">,</span> <span class="nb">tuple</span><span class="p">)):</span> |
| <span class="n">index</span> <span class="o">=</span> <span class="p">[</span><span class="n">index</span><span class="p">]</span> |
| <span class="k">for</span> <span class="n">idx</span> <span class="ow">in</span> <span class="n">index</span><span class="p">:</span> |
| <span class="k">if</span> <span class="n">idx</span> <span class="ow">not</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">_index_update_count</span><span class="p">:</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_index_update_count</span><span class="p">[</span><span class="n">idx</span><span class="p">]</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">begin_num_update</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_index_update_count</span><span class="p">[</span><span class="n">idx</span><span class="p">]</span> <span class="o">+=</span> <span class="mi">1</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">num_update</span> <span class="o">=</span> <span class="nb">max</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_index_update_count</span><span class="p">[</span><span class="n">idx</span><span class="p">],</span> <span class="bp">self</span><span class="o">.</span><span class="n">num_update</span><span class="p">)</span> |
| |
| <span class="k">def</span> <span class="nf">_get_lrs</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">indices</span><span class="p">):</span> |
| <span class="sd">"""Gets the learning rates given the indices of the weights.</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> indices : list of int</span> |
| <span class="sd"> Indices corresponding to weights.</span> |
| |
| <span class="sd"> Returns</span> |
| <span class="sd"> -------</span> |
| <span class="sd"> lrs : list of float</span> |
| <span class="sd"> Learning rates for those indices.</span> |
| <span class="sd"> """</span> |
| <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">lr_scheduler</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span> |
| <span class="n">lr</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">lr_scheduler</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">num_update</span><span class="p">)</span> |
| <span class="k">else</span><span class="p">:</span> |
| <span class="n">lr</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">lr</span> |
| |
| <span class="n">lrs</span> <span class="o">=</span> <span class="p">[</span><span class="n">lr</span> <span class="k">for</span> <span class="n">_</span> <span class="ow">in</span> <span class="n">indices</span><span class="p">]</span> |
| <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">index</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">indices</span><span class="p">):</span> |
| <span class="k">if</span> <span class="n">index</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">param_dict</span><span class="p">:</span> |
| <span class="n">lrs</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">*=</span> <span class="bp">self</span><span class="o">.</span><span class="n">param_dict</span><span class="p">[</span><span class="n">index</span><span class="p">]</span><span class="o">.</span><span class="n">lr_mult</span> |
| <span class="k">elif</span> <span class="n">index</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">lr_mult</span><span class="p">:</span> |
| <span class="n">lrs</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">*=</span> <span class="bp">self</span><span class="o">.</span><span class="n">lr_mult</span><span class="p">[</span><span class="n">index</span><span class="p">]</span> |
| <span class="k">elif</span> <span class="n">index</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">idx2name</span><span class="p">:</span> |
| <span class="n">lrs</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">*=</span> <span class="bp">self</span><span class="o">.</span><span class="n">lr_mult</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">idx2name</span><span class="p">[</span><span class="n">index</span><span class="p">],</span> <span class="mf">1.0</span><span class="p">)</span> |
| <span class="k">return</span> <span class="n">lrs</span> |
| |
| <span class="k">def</span> <span class="nf">_get_lr</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">index</span><span class="p">):</span> |
| <span class="sd">"""Gets the learning rate given the index of the weight.</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> index : int</span> |
| <span class="sd"> The index corresponding to the weight.</span> |
| |
| <span class="sd"> Returns</span> |
| <span class="sd"> -------</span> |
| <span class="sd"> lr : float</span> |
| <span class="sd"> Learning rate for this index.</span> |
| <span class="sd"> """</span> |
| <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_lrs</span><span class="p">([</span><span class="n">index</span><span class="p">])[</span><span class="mi">0</span><span class="p">]</span> |
| |
| <span class="k">def</span> <span class="nf">_get_wds</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">indices</span><span class="p">):</span> |
| <span class="sd">"""Gets weight decays for indices.</span> |
| <span class="sd"> Returns 0 for non-weights if the name of weights are provided for `__init__`.</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> indices : list of int</span> |
| <span class="sd"> Indices of weights.</span> |
| |
| <span class="sd"> Returns</span> |
| <span class="sd"> -------</span> |
| <span class="sd"> wds : list of float</span> |
| <span class="sd"> Weight decays for those indices.</span> |
| <span class="sd"> """</span> |
| <span class="n">wds</span> <span class="o">=</span> <span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">wd</span> <span class="k">for</span> <span class="n">_</span> <span class="ow">in</span> <span class="n">indices</span><span class="p">]</span> |
| <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">index</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">indices</span><span class="p">):</span> |
| <span class="k">if</span> <span class="n">index</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">param_dict</span><span class="p">:</span> |
| <span class="n">wds</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">*=</span> <span class="bp">self</span><span class="o">.</span><span class="n">param_dict</span><span class="p">[</span><span class="n">index</span><span class="p">]</span><span class="o">.</span><span class="n">wd_mult</span> |
| <span class="k">elif</span> <span class="n">index</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">wd_mult</span><span class="p">:</span> |
| <span class="n">wds</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">*=</span> <span class="bp">self</span><span class="o">.</span><span class="n">wd_mult</span><span class="p">[</span><span class="n">index</span><span class="p">]</span> |
| <span class="k">elif</span> <span class="n">index</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">idx2name</span><span class="p">:</span> |
| <span class="n">wds</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">*=</span> <span class="bp">self</span><span class="o">.</span><span class="n">wd_mult</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">idx2name</span><span class="p">[</span><span class="n">index</span><span class="p">],</span> <span class="mf">1.0</span><span class="p">)</span> |
| <span class="k">return</span> <span class="n">wds</span> |
| |
| <span class="k">def</span> <span class="nf">_get_wd</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">index</span><span class="p">):</span> |
| <span class="sd">"""Gets weight decay for index.</span> |
| <span class="sd"> Returns 0 for non-weights if the name of weights are provided for `__init__`.</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> index : int</span> |
| <span class="sd"> The index of weight.</span> |
| |
| <span class="sd"> Returns</span> |
| <span class="sd"> -------</span> |
| <span class="sd"> wd : float</span> |
| <span class="sd"> Weight decay for this index.</span> |
| <span class="sd"> """</span> |
| <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_wds</span><span class="p">([</span><span class="n">index</span><span class="p">])[</span><span class="mi">0</span><span class="p">]</span> |
| |
| <span class="k">def</span> <span class="nf">__getstate__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span> |
| <span class="n">ret</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="vm">__dict__</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span> |
| <span class="c1"># do not include param_dict in the state</span> |
| <span class="k">del</span> <span class="n">ret</span><span class="p">[</span><span class="s1">'param_dict'</span><span class="p">]</span> |
| <span class="k">return</span> <span class="n">ret</span> |
| |
| <span class="k">def</span> <span class="nf">__setstate__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">state</span><span class="p">):</span> |
| <span class="bp">self</span><span class="o">.</span><span class="vm">__dict__</span> <span class="o">=</span> <span class="n">state</span> |
| <span class="c1"># param_dict needs to be explicitly set by the trainer</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">param_dict</span> <span class="o">=</span> <span class="p">{}</span></div> |
| |
| |
| <span class="c1"># convenience wrapper for Optimizer.Register</span> |
| <span class="n">register</span> <span class="o">=</span> <span class="n">Optimizer</span><span class="o">.</span><span class="n">register</span> <span class="c1"># pylint: disable=invalid-name</span> |
| |
| <span class="c1"># pylint: disable=W0223</span> |
| <div class="viewcode-block" id="Test"><a class="viewcode-back" href="../../../api/optimizer/index.html#mxnet.optimizer.Test">[docs]</a><span class="nd">@register</span> |
| <span class="k">class</span> <span class="nc">Test</span><span class="p">(</span><span class="n">Optimizer</span><span class="p">):</span> |
| <span class="sd">"""The Test optimizer"""</span> |
| <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span> |
| <span class="nb">super</span><span class="p">(</span><span class="n">Test</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="o">**</span><span class="n">kwargs</span><span class="p">)</span> |
| |
| <div class="viewcode-block" id="Test.create_state"><a class="viewcode-back" href="../../../api/optimizer/index.html#mxnet.optimizer.Test.create_state">[docs]</a> <span class="k">def</span> <span class="nf">create_state</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">index</span><span class="p">,</span> <span class="n">weight</span><span class="p">):</span> |
| <span class="sd">"""Creates a state to duplicate weight."""</span> |
| <span class="k">return</span> <span class="n">zeros</span><span class="p">(</span><span class="n">weight</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span> <span class="n">weight</span><span class="o">.</span><span class="n">context</span><span class="p">)</span></div> |
| |
| <div class="viewcode-block" id="Test.step"><a class="viewcode-back" href="../../../api/optimizer/index.html#mxnet.optimizer.Test.step">[docs]</a> <span class="k">def</span> <span class="nf">step</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">indices</span><span class="p">,</span> <span class="n">weights</span><span class="p">,</span> <span class="n">grads</span><span class="p">,</span> <span class="n">states</span><span class="p">):</span> |
| <span class="sd">"""Performs w += rescale_grad * grad."""</span> |
| <span class="k">for</span> <span class="n">index</span><span class="p">,</span> <span class="n">weight</span><span class="p">,</span> <span class="n">grad</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="n">indices</span><span class="p">,</span> <span class="n">weights</span><span class="p">,</span> <span class="n">grads</span><span class="p">):</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_update_count</span><span class="p">(</span><span class="n">index</span><span class="p">)</span> |
| <span class="n">lr</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_lr</span><span class="p">(</span><span class="n">index</span><span class="p">)</span> |
| <span class="n">wd</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_wd</span><span class="p">(</span><span class="n">index</span><span class="p">)</span> |
| <span class="n">grad</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">rescale_grad</span> <span class="o">*</span> <span class="n">grad</span> |
| <span class="n">weight</span><span class="p">[:]</span> <span class="o">-=</span> <span class="n">lr</span> <span class="o">*</span> <span class="p">(</span><span class="n">grad</span> <span class="o">+</span> <span class="n">wd</span> <span class="o">*</span> <span class="n">weight</span><span class="p">)</span></div></div> |
| |
| |
| <span class="n">create</span> <span class="o">=</span> <span class="n">Optimizer</span><span class="o">.</span><span class="n">create_optimizer</span> <span class="c1"># pylint: disable=invalid-name</span> |
| </pre></div> |
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