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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> |
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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> |
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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> |
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| <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.gluon.parameter</h1><div class="highlight"><pre> |
| <span></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"># coding: utf-8</span> |
| <span class="c1"># pylint: disable=unnecessary-pass, too-many-lines</span> |
| <span class="sd">"""Neural network parameter."""</span> |
| |
| <span class="n">__all__</span> <span class="o">=</span> <span class="p">[</span><span class="s1">'DeferredInitializationError'</span><span class="p">,</span> <span class="s1">'Parameter'</span><span class="p">,</span> <span class="s1">'Constant'</span><span class="p">,</span> |
| <span class="s1">'tensor_types'</span><span class="p">]</span> |
| |
| |
| <span class="kn">import</span> <span class="nn">uuid</span> |
| <span class="kn">import</span> <span class="nn">warnings</span> |
| <span class="kn">import</span> <span class="nn">weakref</span> |
| <span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span> |
| |
| <span class="kn">from</span> <span class="nn">..base</span> <span class="kn">import</span> <span class="n">mx_real_t</span><span class="p">,</span> <span class="n">MXNetError</span> |
| <span class="kn">from</span> <span class="nn">..</span> <span class="kn">import</span> <span class="n">symbol</span><span class="p">,</span> <span class="n">ndarray</span><span class="p">,</span> <span class="n">initializer</span><span class="p">,</span> <span class="n">context</span><span class="p">,</span> <span class="n">_deferred_compute</span> <span class="k">as</span> <span class="n">dc</span> |
| <span class="kn">from</span> <span class="nn">..context</span> <span class="kn">import</span> <span class="n">Context</span><span class="p">,</span> <span class="n">cpu</span> |
| <span class="kn">from</span> <span class="nn">..</span> <span class="kn">import</span> <span class="n">autograd</span> |
| <span class="kn">from</span> <span class="nn">.utils</span> <span class="kn">import</span> <span class="n">shape_is_known</span> |
| <span class="kn">from</span> <span class="nn">..util</span> <span class="kn">import</span> <span class="n">is_np_shape</span><span class="p">,</span> <span class="n">is_np_array</span> |
| <span class="kn">from</span> <span class="nn">..</span> <span class="kn">import</span> <span class="n">numpy</span> <span class="k">as</span> <span class="n">_mx_np</span> <span class="c1"># pylint: disable=reimported</span> |
| |
| <span class="c1"># pylint: disable= invalid-name</span> |
| <span class="n">tensor_types</span> <span class="o">=</span> <span class="p">(</span><span class="n">symbol</span><span class="o">.</span><span class="n">Symbol</span><span class="p">,</span> <span class="n">ndarray</span><span class="o">.</span><span class="n">NDArray</span><span class="p">)</span> |
| <span class="c1"># pylint: enable= invalid-name</span> |
| |
| <span class="k">class</span> <span class="nc">DeferredInitializationError</span><span class="p">(</span><span class="n">MXNetError</span><span class="p">):</span> |
| <span class="sd">"""Error for unfinished deferred initialization."""</span> |
| <span class="k">pass</span> |
| |
| <div class="viewcode-block" id="Parameter"><a class="viewcode-back" href="../../../api/gluon/parameter.html#mxnet.gluon.Parameter">[docs]</a><span class="k">class</span> <span class="nc">Parameter</span><span class="p">(</span><span class="nb">object</span><span class="p">):</span> |
| <span class="sd">"""A Container holding parameters (weights) of Blocks.</span> |
| |
| <span class="sd"> :py:class:`Parameter` holds a copy of the parameter on each :py:class:`Context` after</span> |
| <span class="sd"> it is initialized with ``Parameter.initialize(...)``. If :py:attr:`grad_req` is</span> |
| <span class="sd"> not ``'null'``, it will also hold a gradient array on each :py:class:`Context`::</span> |
| |
| <span class="sd"> ctx = mx.gpu(0)</span> |
| <span class="sd"> x = mx.nd.zeros((16, 100), ctx=ctx)</span> |
| <span class="sd"> w = mx.gluon.Parameter('fc_weight', shape=(64, 100), init=mx.init.Xavier())</span> |
| <span class="sd"> b = mx.gluon.Parameter('fc_bias', shape=(64,), init=mx.init.Zero())</span> |
| <span class="sd"> w.initialize(ctx=ctx)</span> |
| <span class="sd"> b.initialize(ctx=ctx)</span> |
| <span class="sd"> out = mx.nd.FullyConnected(x, w.data(ctx), b.data(ctx), num_hidden=64)</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> name : str, default 'weight'</span> |
| <span class="sd"> Name of this parameter. It decides the corresponding default initializer.</span> |
| <span class="sd"> grad_req : {'write', 'add', 'null'}, default 'write'</span> |
| <span class="sd"> Specifies how to update gradient to grad arrays.</span> |
| |
| <span class="sd"> - ``'write'`` means everytime gradient is written to grad :py:class:`NDArray`.</span> |
| <span class="sd"> - ``'add'`` means everytime gradient is added to the grad :py:class:`NDArray`. You need</span> |
| <span class="sd"> to manually call ``zero_grad()`` to clear the gradient buffer before each</span> |
| <span class="sd"> iteration when using this option.</span> |
| <span class="sd"> - 'null' means gradient is not requested for this parameter. gradient arrays</span> |
| <span class="sd"> will not be allocated.</span> |
| <span class="sd"> shape : int or tuple of int, default None</span> |
| <span class="sd"> Shape of this parameter. By default shape is not specified. Parameter with</span> |
| <span class="sd"> unknown shape can be used for :py:class:`Symbol` API, but ``init`` will throw an error</span> |
| <span class="sd"> when using :py:class:`NDArray` API.</span> |
| <span class="sd"> dtype : numpy.dtype or str, default 'float32'</span> |
| <span class="sd"> Data type of this parameter. For example, ``numpy.float32`` or ``'float32'``.</span> |
| <span class="sd"> lr_mult : float, default 1.0</span> |
| <span class="sd"> Learning rate multiplier. Learning rate will be multiplied by lr_mult</span> |
| <span class="sd"> when updating this parameter with optimizer.</span> |
| <span class="sd"> wd_mult : float, default 1.0</span> |
| <span class="sd"> Weight decay multiplier (L2 regularizer coefficient). Works similar to lr_mult.</span> |
| <span class="sd"> init : Initializer, default None</span> |
| <span class="sd"> Initializer of this parameter. Will use the global initializer by default.</span> |
| <span class="sd"> stype: {'default', 'row_sparse', 'csr'}, defaults to 'default'.</span> |
| <span class="sd"> The storage type of the parameter.</span> |
| <span class="sd"> grad_stype: {'default', 'row_sparse', 'csr'}, defaults to 'default'.</span> |
| <span class="sd"> The storage type of the parameter's gradient.</span> |
| |
| <span class="sd"> Attributes</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> grad_req : {'write', 'add', 'null'}</span> |
| <span class="sd"> This can be set before or after initialization. Setting ``grad_req`` to ``'null'``</span> |
| <span class="sd"> with ``x.grad_req = 'null'`` saves memory and computation when you don't</span> |
| <span class="sd"> need gradient w.r.t x.</span> |
| <span class="sd"> lr_mult : float</span> |
| <span class="sd"> Local learning rate multiplier for this Parameter. The actual learning rate</span> |
| <span class="sd"> is calculated with ``learning_rate * lr_mult``. You can set it with</span> |
| <span class="sd"> ``param.lr_mult = 2.0``</span> |
| <span class="sd"> wd_mult : float</span> |
| <span class="sd"> Local weight decay multiplier for this Parameter.</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">name</span><span class="o">=</span><span class="s1">'weight'</span><span class="p">,</span> <span class="n">grad_req</span><span class="o">=</span><span class="s1">'write'</span><span class="p">,</span> <span class="n">shape</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">mx_real_t</span><span class="p">,</span> |
| <span class="n">lr_mult</span><span class="o">=</span><span class="mf">1.0</span><span class="p">,</span> <span class="n">wd_mult</span><span class="o">=</span><span class="mf">1.0</span><span class="p">,</span> <span class="n">init</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">allow_deferred_init</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> |
| <span class="n">differentiable</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">stype</span><span class="o">=</span><span class="s1">'default'</span><span class="p">,</span> <span class="n">grad_stype</span><span class="o">=</span><span class="s1">'default'</span><span class="p">):</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_var</span> <span class="o">=</span> <span class="kc">None</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_uuid</span> <span class="o">=</span> <span class="nb">str</span><span class="p">(</span><span class="n">uuid</span><span class="o">.</span><span class="n">uuid4</span><span class="p">())</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_var_name</span> <span class="o">=</span> <span class="kc">None</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_data</span> <span class="o">=</span> <span class="kc">None</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_grad</span> <span class="o">=</span> <span class="kc">None</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_ctx_list</span> <span class="o">=</span> <span class="kc">None</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_ctx_map</span> <span class="o">=</span> <span class="kc">None</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_trainer</span> <span class="o">=</span> <span class="kc">None</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_deferred_init</span> <span class="o">=</span> <span class="p">()</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_differentiable</span> <span class="o">=</span> <span class="n">differentiable</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_allow_deferred_init</span> <span class="o">=</span> <span class="n">allow_deferred_init</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_grad_req</span> <span class="o">=</span> <span class="kc">None</span> |
| <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">shape</span><span class="p">,</span> <span class="nb">int</span><span class="p">):</span> |
| <span class="n">shape</span> <span class="o">=</span> <span class="p">(</span><span class="n">shape</span><span class="p">,)</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_shape</span> <span class="o">=</span> <span class="n">shape</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_name</span> <span class="o">=</span> <span class="n">name</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_dtype</span> <span class="o">=</span> <span class="n">dtype</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">lr_mult</span> <span class="o">=</span> <span class="n">lr_mult</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">wd_mult</span> <span class="o">=</span> <span class="n">wd_mult</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">grad_req</span> <span class="o">=</span> <span class="n">grad_req</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">init</span> <span class="o">=</span> <span class="n">init</span> |
| <span class="c1"># sparse related storage type information</span> |
| <span class="n">valid_stypes</span> <span class="o">=</span> <span class="p">[</span><span class="s1">'default'</span><span class="p">,</span> <span class="s1">'row_sparse'</span><span class="p">,</span> <span class="s1">'csr'</span><span class="p">]</span> |
| <span class="k">assert</span> <span class="n">grad_stype</span> <span class="ow">in</span> <span class="n">valid_stypes</span><span class="p">,</span> <span class="s2">"grad_stype for Parameter must be "</span> \ |
| <span class="s2">"one of 'default', 'row_sparse', or 'csr', but got '</span><span class="si">%s</span><span class="s2">'"</span> <span class="o">%</span> <span class="p">(</span><span class="n">grad_stype</span><span class="p">)</span> |
| <span class="k">assert</span> <span class="n">stype</span> <span class="ow">in</span> <span class="n">valid_stypes</span><span class="p">,</span> <span class="s2">"stype for Parameter must be "</span> \ |
| <span class="s2">"one of 'default', 'row_sparse', or 'csr', but got '</span><span class="si">%s</span><span class="s2">'"</span> <span class="o">%</span> <span class="p">(</span><span class="n">stype</span><span class="p">)</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_grad_stype</span> <span class="o">=</span> <span class="n">grad_stype</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_stype</span> <span class="o">=</span> <span class="n">stype</span> |
| |
| <span class="k">def</span> <span class="fm">__repr__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span> |
| <span class="n">s</span> <span class="o">=</span> <span class="s1">'Parameter (shape=</span><span class="si">{shape}</span><span class="s1">, dtype=</span><span class="si">{dtype}</span><span class="s1">)'</span> |
| <span class="k">return</span> <span class="n">s</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">shape</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">dtype</span><span class="p">)</span> |
| |
| <span class="nd">@property</span> |
| <span class="k">def</span> <span class="nf">grad_req</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span> |
| <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_grad_req</span> |
| |
| <span class="nd">@property</span> |
| <span class="k">def</span> <span class="nf">name</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span> |
| <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_name</span> |
| |
| <span class="nd">@grad_req</span><span class="o">.</span><span class="n">setter</span> |
| <span class="k">def</span> <span class="nf">grad_req</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">req</span><span class="p">):</span> |
| <span class="k">assert</span> <span class="n">req</span> <span class="ow">in</span> <span class="p">[</span><span class="s1">'write'</span><span class="p">,</span> <span class="s1">'add'</span><span class="p">,</span> <span class="s1">'null'</span><span class="p">],</span> \ |
| <span class="s2">"grad_req must be one of 'write', 'add', or 'null', but got '</span><span class="si">%s</span><span class="s2">'"</span><span class="o">%</span><span class="n">req</span> |
| <span class="k">if</span> <span class="ow">not</span> <span class="bp">self</span><span class="o">.</span><span class="n">_differentiable</span><span class="p">:</span> |
| <span class="n">req</span> <span class="o">=</span> <span class="s1">'null'</span> |
| <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_grad_req</span> <span class="o">==</span> <span class="n">req</span><span class="p">:</span> |
| <span class="k">return</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_grad_req</span> <span class="o">=</span> <span class="n">req</span> |
| <span class="k">if</span> <span class="n">req</span> <span class="o">==</span> <span class="s1">'null'</span> <span class="ow">and</span> <span class="bp">self</span><span class="o">.</span><span class="n">_grad</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_grad</span> <span class="o">=</span> <span class="kc">None</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_data</span> <span class="o">=</span> <span class="p">[</span><span class="n">i</span><span class="o">.</span><span class="n">detach</span><span class="p">()</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">_data</span><span class="p">]</span> |
| <span class="k">elif</span> <span class="bp">self</span><span class="o">.</span><span class="n">_data</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_init_grad</span><span class="p">()</span> |
| |
| <span class="nd">@property</span> |
| <span class="k">def</span> <span class="nf">dtype</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span> |
| <span class="sd">"""The type of the parameter.</span> |
| |
| <span class="sd"> Setting the dtype value is equivalent to casting the value of the parameter</span> |
| <span class="sd"> """</span> |
| <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_dtype</span> |
| |
| <span class="nd">@dtype</span><span class="o">.</span><span class="n">setter</span> |
| <span class="k">def</span> <span class="nf">dtype</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">dtype</span><span class="p">):</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">cast</span><span class="p">(</span><span class="n">dtype</span><span class="p">)</span> |
| |
| <span class="nd">@property</span> |
| <span class="k">def</span> <span class="nf">shape</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span> |
| <span class="sd">"""The shape of the parameter.</span> |
| |
| <span class="sd"> By default, an unknown dimension size is 0. However, when the NumPy semantic</span> |
| <span class="sd"> is turned on, unknown dimension size is -1.</span> |
| <span class="sd"> """</span> |
| <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_shape</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span> |
| <span class="k">return</span> <span class="kc">None</span> |
| <span class="k">elif</span> <span class="n">is_np_shape</span><span class="p">():</span> |
| <span class="c1"># Parameters shouldn't be zero-size. If one of its dimension is 0,</span> |
| <span class="c1"># it means the parameter isn't initialized. In the NumPy semantics,</span> |
| <span class="c1"># the unknown dimension should be marked with -1.</span> |
| <span class="k">return</span> <span class="nb">tuple</span><span class="p">(</span><span class="n">i</span> <span class="k">if</span> <span class="n">i</span> <span class="o">!=</span> <span class="mi">0</span> <span class="k">else</span> <span class="o">-</span><span class="mi">1</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">_shape</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">_shape</span> |
| |
| <span class="nd">@shape</span><span class="o">.</span><span class="n">setter</span> |
| <span class="k">def</span> <span class="nf">shape</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">new_shape</span><span class="p">):</span> |
| <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_shape</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">_shape</span> <span class="o">=</span> <span class="n">new_shape</span> |
| <span class="k">return</span> |
| |
| <span class="k">assert</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_shape</span><span class="p">)</span> <span class="o">==</span> <span class="nb">len</span><span class="p">(</span><span class="n">new_shape</span><span class="p">)</span> <span class="ow">and</span> \ |
| <span class="nb">all</span><span class="p">(</span><span class="n">j</span> <span class="ow">in</span> <span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="n">i</span><span class="p">)</span> <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">j</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="n">new_shape</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">_shape</span><span class="p">)),</span> \ |
| <span class="s2">"Expected shape </span><span class="si">%s</span><span class="s2"> is incompatible with given shape </span><span class="si">%s</span><span class="s2"> for Parameter </span><span class="si">%s</span><span class="s2">."</span><span class="o">%</span><span class="p">(</span> |
| <span class="nb">str</span><span class="p">(</span><span class="n">new_shape</span><span class="p">),</span> <span class="nb">str</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_shape</span><span class="p">),</span> <span class="nb">str</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">name</span><span class="p">))</span> <span class="c1"># -1 means unknown dim size in np_shape mode</span> |
| |
| <span class="bp">self</span><span class="o">.</span><span class="n">_shape</span> <span class="o">=</span> <span class="n">new_shape</span> |
| |
| <span class="k">def</span> <span class="nf">_set_trainer</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">trainer</span><span class="p">):</span> |
| <span class="sd">""" Set the trainer this parameter is associated with. """</span> |
| <span class="c1"># trainer cannot be replaced for sparse params</span> |
| <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_stype</span> <span class="o">!=</span> <span class="s1">'default'</span> <span class="ow">and</span> <span class="bp">self</span><span class="o">.</span><span class="n">_trainer</span> <span class="ow">and</span> <span class="n">trainer</span> <span class="ow">and</span> <span class="bp">self</span><span class="o">.</span><span class="n">_trainer</span><span class="p">()</span> <span class="ow">is</span> <span class="ow">not</span> <span class="n">trainer</span><span class="p">:</span> |
| <span class="k">raise</span> <span class="ne">RuntimeError</span><span class="p">(</span> |
| <span class="s2">"Failed to set the trainer for Parameter '</span><span class="si">%s</span><span class="s2">' because it was already set. "</span> \ |
| <span class="s2">"More than one trainers for a </span><span class="si">%s</span><span class="s2"> Parameter is not supported."</span> \ |
| <span class="o">%</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">name</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">_stype</span><span class="p">))</span> |
| <span class="k">if</span> <span class="n">trainer</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_trainer</span> <span class="o">=</span> <span class="n">weakref</span><span class="o">.</span><span class="n">ref</span><span class="p">(</span><span class="n">trainer</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">_trainer</span> <span class="o">=</span> <span class="n">trainer</span> |
| |
| <span class="k">def</span> <span class="nf">_check_and_get</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">arr_list</span><span class="p">,</span> <span class="n">ctx</span><span class="p">):</span> |
| <span class="k">if</span> <span class="n">arr_list</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="n">ctx</span> <span class="ow">is</span> <span class="nb">list</span><span class="p">:</span> |
| <span class="k">return</span> <span class="n">arr_list</span> |
| <span class="k">if</span> <span class="n">ctx</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span> |
| <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">arr_list</span><span class="p">)</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span> |
| <span class="k">return</span> <span class="n">arr_list</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> |
| <span class="k">else</span><span class="p">:</span> |
| <span class="n">ctx</span> <span class="o">=</span> <span class="n">context</span><span class="o">.</span><span class="n">current_context</span><span class="p">()</span> |
| <span class="n">ctx_list</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_ctx_map</span><span class="p">[</span><span class="n">ctx</span><span class="o">.</span><span class="n">device_typeid</span><span class="o">&</span><span class="mi">1</span><span class="p">]</span> |
| <span class="k">if</span> <span class="n">ctx</span><span class="o">.</span><span class="n">device_id</span> <span class="o"><</span> <span class="nb">len</span><span class="p">(</span><span class="n">ctx_list</span><span class="p">):</span> |
| <span class="n">idx</span> <span class="o">=</span> <span class="n">ctx_list</span><span class="p">[</span><span class="n">ctx</span><span class="o">.</span><span class="n">device_id</span><span class="p">]</span> |
| <span class="k">if</span> <span class="n">idx</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="n">arr_list</span><span class="p">[</span><span class="n">idx</span><span class="p">]</span> |
| <span class="k">raise</span> <span class="ne">RuntimeError</span><span class="p">(</span> |
| <span class="s2">"Parameter '</span><span class="si">%s</span><span class="s2">' was not initialized on context </span><span class="si">%s</span><span class="s2">. "</span> |
| <span class="s2">"It was only initialized on </span><span class="si">%s</span><span class="s2">."</span><span class="o">%</span><span class="p">(</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">name</span><span class="p">,</span> <span class="nb">str</span><span class="p">(</span><span class="n">ctx</span><span class="p">),</span> <span class="nb">str</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_ctx_list</span><span class="p">)))</span> |
| <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_deferred_init</span><span class="p">:</span> |
| <span class="k">raise</span> <span class="n">DeferredInitializationError</span><span class="p">(</span> |
| <span class="s2">"Parameter '</span><span class="si">%s</span><span class="s2">' has not been initialized yet because initialization was "</span> \ |
| <span class="s2">"deferred. Actual initialization happens during the first forward pass. "</span> \ |
| <span class="s2">"Please pass one batch of data through the network before accessing Parameters. "</span> \ |
| <span class="s2">"You can also avoid deferred initialization by specifying in_units, "</span> \ |
| <span class="s2">"num_features, etc., for network layers."</span><span class="o">%</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">name</span><span class="p">))</span> |
| <span class="k">raise</span> <span class="ne">RuntimeError</span><span class="p">(</span> |
| <span class="s2">"Parameter '</span><span class="si">%s</span><span class="s2">' has not been initialized. Note that "</span> \ |
| <span class="s2">"you should initialize parameters and create Trainer "</span> \ |
| <span class="s2">"with Block.collect_params() instead of Block.params "</span> \ |
| <span class="s2">"because the later does not include Parameters of "</span> \ |
| <span class="s2">"nested child Blocks"</span><span class="o">%</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">name</span><span class="p">))</span> |
| |
| <span class="k">def</span> <span class="nf">_get_row_sparse</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">arr_list</span><span class="p">,</span> <span class="n">ctx</span><span class="p">,</span> <span class="n">row_id</span><span class="p">):</span> |
| <span class="sd">""" Get row_sparse data from row_sparse parameters based on row_id. """</span> |
| <span class="c1"># get row sparse params based on row ids</span> |
| <span class="k">if</span> <span class="ow">not</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">row_id</span><span class="p">,</span> <span class="n">ndarray</span><span class="o">.</span><span class="n">NDArray</span><span class="p">):</span> |
| <span class="k">raise</span> <span class="ne">TypeError</span><span class="p">(</span><span class="s2">"row_id must have NDArray type, but </span><span class="si">%s</span><span class="s2"> is given"</span><span class="o">%</span><span class="p">(</span><span class="nb">type</span><span class="p">(</span><span class="n">row_id</span><span class="p">)))</span> |
| <span class="n">trainer</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_trainer</span><span class="p">()</span> <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_trainer</span> <span class="k">else</span> <span class="kc">None</span> |
| <span class="k">if</span> <span class="ow">not</span> <span class="n">trainer</span><span class="p">:</span> |
| <span class="k">raise</span> <span class="ne">RuntimeError</span><span class="p">(</span><span class="s2">"Cannot get row_sparse data for Parameter '</span><span class="si">%s</span><span class="s2">' when no "</span> \ |
| <span class="s2">"Trainer is created with it."</span><span class="o">%</span><span class="bp">self</span><span class="o">.</span><span class="n">name</span><span class="p">)</span> |
| <span class="n">results</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_check_and_get</span><span class="p">(</span><span class="n">arr_list</span><span class="p">,</span> <span class="n">ctx</span><span class="p">)</span> |
| |
| <span class="c1"># fetch row sparse params from the trainer</span> |
| <span class="n">trainer</span><span class="o">.</span><span class="n">_row_sparse_pull</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">results</span><span class="p">,</span> <span class="n">row_id</span><span class="p">)</span> |
| <span class="k">return</span> <span class="n">results</span> |
| |
| <span class="k">def</span> <span class="nf">_load_init</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data</span><span class="p">,</span> <span class="n">ctx</span><span class="p">,</span> <span class="n">cast_dtype</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">dtype_source</span><span class="o">=</span><span class="s1">'current'</span><span class="p">):</span> |
| <span class="sd">"""</span> |
| <span class="sd"> (Re)initializes by loading from data.</span> |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> data : NDArray</span> |
| <span class="sd"> The data to load</span> |
| <span class="sd"> ctx : Context or list of Context</span> |
| <span class="sd"> Context(s) initialize loaded parameters on.</span> |
| <span class="sd"> cast_dtype : bool, default False</span> |
| <span class="sd"> Cast the data type of the parameter</span> |
| <span class="sd"> dtype_source : str, default 'current'</span> |
| <span class="sd"> must be in {'current', 'saved'}</span> |
| <span class="sd"> Only valid if cast_dtype=True, specify the source of the dtype for casting</span> |
| <span class="sd"> the parameters</span> |
| <span class="sd"> """</span> |
| <span class="k">if</span> <span class="n">cast_dtype</span><span class="p">:</span> |
| <span class="k">assert</span> <span class="n">dtype_source</span> <span class="ow">in</span> <span class="p">[</span><span class="s1">'current'</span><span class="p">,</span> <span class="s1">'saved'</span><span class="p">]</span> |
| <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">shape</span><span class="p">:</span> |
| <span class="n">unknown_dim_size</span> <span class="o">=</span> <span class="o">-</span><span class="mi">1</span> <span class="k">if</span> <span class="n">is_np_shape</span><span class="p">()</span> <span class="k">else</span> <span class="mi">0</span> |
| <span class="k">for</span> <span class="n">self_dim</span><span class="p">,</span> <span class="n">data_dim</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span> <span class="n">data</span><span class="o">.</span><span class="n">shape</span><span class="p">):</span> |
| <span class="k">assert</span> <span class="n">self_dim</span> <span class="ow">in</span> <span class="p">(</span><span class="n">unknown_dim_size</span><span class="p">,</span> <span class="n">data_dim</span><span class="p">),</span> \ |
| <span class="s2">"Failed loading Parameter '</span><span class="si">%s</span><span class="s2">' from saved params: "</span> \ |
| <span class="s2">"shape incompatible expected </span><span class="si">%s</span><span class="s2"> vs saved </span><span class="si">%s</span><span class="s2">"</span><span class="o">%</span><span class="p">(</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">name</span><span class="p">,</span> <span class="nb">str</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">shape</span><span class="p">),</span> <span class="nb">str</span><span class="p">(</span><span class="n">data</span><span class="o">.</span><span class="n">shape</span><span class="p">))</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">shape</span> <span class="o">=</span> <span class="nb">tuple</span><span class="p">(</span><span class="n">i</span> <span class="k">if</span> <span class="n">i</span> <span class="o">!=</span> <span class="n">unknown_dim_size</span> <span class="k">else</span> <span class="n">j</span> |
| <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">j</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span> <span class="n">data</span><span class="o">.</span><span class="n">shape</span><span class="p">))</span> |
| <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">dtype</span><span class="p">:</span> |
| <span class="k">if</span> <span class="n">cast_dtype</span> <span class="ow">and</span> <span class="n">np</span><span class="o">.</span><span class="n">dtype</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">dtype</span><span class="p">)</span><span class="o">.</span><span class="n">type</span> <span class="o">!=</span> <span class="n">data</span><span class="o">.</span><span class="n">dtype</span><span class="p">:</span> |
| <span class="k">if</span> <span class="n">dtype_source</span> <span class="o">==</span> <span class="s1">'current'</span><span class="p">:</span> |
| <span class="n">data</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">dtype</span><span class="p">,</span> <span class="n">copy</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span> |
| <span class="k">elif</span> <span class="n">dtype_source</span> <span class="o">==</span> <span class="s1">'saved'</span><span class="p">:</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">dtype</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">dtype</span> |
| <span class="k">else</span><span class="p">:</span> |
| <span class="k">if</span> <span class="n">data</span><span class="o">.</span><span class="n">dtype</span> <span class="o">==</span> <span class="n">np</span><span class="o">.</span><span class="n">dtype</span><span class="p">([(</span><span class="s1">'bfloat16'</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">uint16</span><span class="p">)]):</span> |
| <span class="k">assert</span> <span class="n">np</span><span class="o">.</span><span class="n">dtype</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">dtype</span><span class="p">)</span> <span class="o">==</span> <span class="n">data</span><span class="o">.</span><span class="n">dtype</span><span class="p">,</span> \ |
| <span class="s2">"Failed loading Parameter '</span><span class="si">%s</span><span class="s2">' from saved params: "</span> \ |
| <span class="s2">"dtype incompatible expected </span><span class="si">%s</span><span class="s2"> vs saved </span><span class="si">%s</span><span class="s2">. "</span> \ |
| <span class="s2">"Set cast_dtype=True to cast the dtype of saved params."</span><span class="o">%</span><span class="p">(</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">name</span><span class="p">,</span> <span class="nb">str</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">dtype</span><span class="p">),</span> <span class="nb">str</span><span class="p">(</span><span class="n">data</span><span class="o">.</span><span class="n">dtype</span><span class="p">))</span> |
| <span class="k">else</span><span class="p">:</span> |
| <span class="k">assert</span> <span class="n">np</span><span class="o">.</span><span class="n">dtype</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">dtype</span><span class="p">)</span><span class="o">.</span><span class="n">type</span> <span class="o">==</span> <span class="n">data</span><span class="o">.</span><span class="n">dtype</span><span class="p">,</span> \ |
| <span class="s2">"Failed loading Parameter '</span><span class="si">%s</span><span class="s2">' from saved params: "</span> \ |
| <span class="s2">"dtype incompatible expected </span><span class="si">%s</span><span class="s2"> vs saved </span><span class="si">%s</span><span class="s2">. "</span> \ |
| <span class="s2">"Set cast_dtype=True to cast the dtype of saved params."</span><span class="o">%</span><span class="p">(</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">name</span><span class="p">,</span> <span class="nb">str</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">dtype</span><span class="p">),</span> <span class="nb">str</span><span class="p">(</span><span class="n">data</span><span class="o">.</span><span class="n">dtype</span><span class="p">))</span> |
| <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_stype</span> <span class="o">!=</span> <span class="n">data</span><span class="o">.</span><span class="n">stype</span><span class="p">:</span> |
| <span class="n">data</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">tostype</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_stype</span><span class="p">)</span> |
| <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">ctx</span><span class="p">,</span> <span class="n">Context</span><span class="p">):</span> |
| <span class="n">ctx</span> <span class="o">=</span> <span class="p">[</span><span class="n">ctx</span><span class="p">]</span> |
| <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_data</span> <span class="ow">is</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">_deferred_init</span><span class="p">:</span> |
| <span class="k">assert</span> <span class="n">ctx</span> <span class="ow">is</span> <span class="kc">None</span> <span class="ow">or</span> <span class="nb">set</span><span class="p">(</span><span class="n">ctx</span><span class="p">)</span> <span class="o">==</span> <span class="nb">set</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_deferred_init</span><span class="p">[</span><span class="mi">1</span><span class="p">]),</span> \ |
| <span class="s2">"Failed to load Parameter '</span><span class="si">%s</span><span class="s2">' on </span><span class="si">%s</span><span class="s2"> because it was "</span> \ |
| <span class="s2">"previous initialized on </span><span class="si">%s</span><span class="s2">."</span><span class="o">%</span><span class="p">(</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">name</span><span class="p">,</span> <span class="nb">str</span><span class="p">(</span><span class="n">ctx</span><span class="p">),</span> <span class="nb">str</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">list_ctx</span><span class="p">()))</span> |
| <span class="n">ctx</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_deferred_init</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> |
| <span class="k">elif</span> <span class="n">ctx</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span> |
| <span class="n">ctx</span> <span class="o">=</span> <span class="p">[</span><span class="n">cpu</span><span class="p">()]</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_init_impl</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">ctx</span><span class="p">)</span> |
| <span class="k">else</span><span class="p">:</span> |
| <span class="k">assert</span> <span class="n">ctx</span> <span class="ow">is</span> <span class="kc">None</span> <span class="ow">or</span> <span class="nb">set</span><span class="p">(</span><span class="n">ctx</span><span class="p">)</span> <span class="o">==</span> <span class="nb">set</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">list_ctx</span><span class="p">()),</span> \ |
| <span class="s2">"Failed to load Parameter '</span><span class="si">%s</span><span class="s2">' on </span><span class="si">%s</span><span class="s2"> because it was "</span> \ |
| <span class="s2">"previous initialized on </span><span class="si">%s</span><span class="s2">."</span><span class="o">%</span><span class="p">(</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">name</span><span class="p">,</span> <span class="nb">str</span><span class="p">(</span><span class="n">ctx</span><span class="p">),</span> <span class="nb">str</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">list_ctx</span><span class="p">()))</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">set_data</span><span class="p">(</span><span class="n">data</span><span class="p">)</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_deferred_init</span> <span class="o">=</span> <span class="p">()</span> |
| |
| <span class="k">def</span> <span class="nf">_finish_deferred_init</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span> |
| <span class="sd">"""Finishes deferred initialization."""</span> |
| <span class="k">if</span> <span class="ow">not</span> <span class="bp">self</span><span class="o">.</span><span class="n">_deferred_init</span><span class="p">:</span> |
| <span class="k">return</span> |
| <span class="n">init</span><span class="p">,</span> <span class="n">ctx</span><span class="p">,</span> <span class="n">default_init</span><span class="p">,</span> <span class="n">data</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_deferred_init</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_deferred_init</span> <span class="o">=</span> <span class="p">()</span> |
| |
| <span class="k">assert</span> <span class="n">shape_is_known</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">shape</span><span class="p">),</span> \ |
| <span class="s2">"Cannot initialize Parameter '</span><span class="si">%s</span><span class="s2">' because it has "</span> \ |
| <span class="s2">"invalid shape: </span><span class="si">%s</span><span class="s2">. Please specify in_units, "</span> \ |
| <span class="s2">"in_channels, etc for `Block`s."</span><span class="o">%</span><span class="p">(</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">name</span><span class="p">,</span> <span class="nb">str</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">shape</span><span class="p">))</span> |
| |
| <span class="k">with</span> <span class="n">autograd</span><span class="o">.</span><span class="n">pause</span><span class="p">(),</span> <span class="n">dc</span><span class="o">.</span><span class="n">context</span><span class="p">(</span><span class="kc">False</span><span class="p">):</span> |
| <span class="k">if</span> <span class="n">data</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span> |
| <span class="n">kwargs</span> <span class="o">=</span> <span class="p">{</span><span class="s1">'shape'</span><span class="p">:</span> <span class="bp">self</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span> <span class="s1">'dtype'</span><span class="p">:</span> <span class="bp">self</span><span class="o">.</span><span class="n">dtype</span><span class="p">,</span> <span class="s1">'ctx'</span><span class="p">:</span> <span class="n">context</span><span class="o">.</span><span class="n">cpu</span><span class="p">()}</span> |
| <span class="k">if</span> <span class="n">is_np_array</span><span class="p">():</span> |
| <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_stype</span> <span class="o">!=</span> <span class="s1">'default'</span><span class="p">:</span> |
| <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">"mxnet.numpy.zeros does not support stype = </span><span class="si">{}</span><span class="s2">"</span> |
| <span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_stype</span><span class="p">))</span> |
| <span class="n">zeros_fn</span> <span class="o">=</span> <span class="n">_mx_np</span><span class="o">.</span><span class="n">zeros</span> |
| <span class="k">else</span><span class="p">:</span> |
| <span class="n">kwargs</span><span class="p">[</span><span class="s1">'stype'</span><span class="p">]</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_stype</span> |
| <span class="n">zeros_fn</span> <span class="o">=</span> <span class="n">ndarray</span><span class="o">.</span><span class="n">zeros</span> |
| <span class="n">data</span> <span class="o">=</span> <span class="n">zeros_fn</span><span class="p">(</span><span class="o">**</span><span class="n">kwargs</span><span class="p">)</span> |
| <span class="n">initializer</span><span class="o">.</span><span class="n">create</span><span class="p">(</span><span class="n">default_init</span><span class="p">)(</span> |
| <span class="n">initializer</span><span class="o">.</span><span class="n">InitDesc</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">name</span><span class="p">,</span> <span class="p">{</span><span class="s1">'__init__'</span><span class="p">:</span> <span class="n">init</span><span class="p">}),</span> <span class="n">data</span><span class="p">)</span> |
| |
| <span class="bp">self</span><span class="o">.</span><span class="n">_init_impl</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">ctx</span><span class="p">)</span> |
| |
| <span class="k">def</span> <span class="nf">_init_impl</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data</span><span class="p">,</span> <span class="n">ctx_list</span><span class="p">):</span> |
| <span class="sd">"""Sets data and grad."""</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_ctx_list</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="n">ctx_list</span><span class="p">)</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_ctx_map</span> <span class="o">=</span> <span class="p">[[],</span> <span class="p">[]]</span> |
| <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">ctx</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_ctx_list</span><span class="p">):</span> |
| <span class="n">dev_list</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_ctx_map</span><span class="p">[</span><span class="n">ctx</span><span class="o">.</span><span class="n">device_typeid</span><span class="o">&</span><span class="mi">1</span><span class="p">]</span> |
| <span class="k">while</span> <span class="nb">len</span><span class="p">(</span><span class="n">dev_list</span><span class="p">)</span> <span class="o"><=</span> <span class="n">ctx</span><span class="o">.</span><span class="n">device_id</span><span class="p">:</span> |
| <span class="n">dev_list</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="kc">None</span><span class="p">)</span> |
| <span class="n">dev_list</span><span class="p">[</span><span class="n">ctx</span><span class="o">.</span><span class="n">device_id</span><span class="p">]</span> <span class="o">=</span> <span class="n">i</span> |
| |
| <span class="bp">self</span><span class="o">.</span><span class="n">_data</span> <span class="o">=</span> <span class="p">[</span><span class="n">data</span><span class="o">.</span><span class="n">copyto</span><span class="p">(</span><span class="n">ctx</span><span class="p">)</span> <span class="k">for</span> <span class="n">ctx</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">_ctx_list</span><span class="p">]</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_init_grad</span><span class="p">()</span> |
| |
| <span class="k">def</span> <span class="nf">_init_grad</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span> |
| <span class="sd">"""Initialize grad buffers."""</span> |
| <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">grad_req</span> <span class="o">==</span> <span class="s1">'null'</span><span class="p">:</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_grad</span> <span class="o">=</span> <span class="kc">None</span> |
| <span class="k">return</span> |
| |
| <span class="k">if</span> <span class="n">is_np_array</span><span class="p">():</span> |
| <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_grad_stype</span> <span class="o">!=</span> <span class="s1">'default'</span><span class="p">:</span> |
| <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">"mxnet.numpy.zeros does not support stype = </span><span class="si">{}</span><span class="s2">"</span> |
| <span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_grad_stype</span><span class="p">))</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_grad</span> <span class="o">=</span> <span class="p">[</span><span class="n">_mx_np</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="n">shape</span><span class="o">=</span><span class="n">i</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">i</span><span class="o">.</span><span class="n">dtype</span><span class="p">,</span> <span class="n">ctx</span><span class="o">=</span><span class="n">i</span><span class="o">.</span><span class="n">ctx</span><span class="p">)</span> |
| <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">_data</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">_grad</span> <span class="o">=</span> <span class="p">[</span><span class="n">ndarray</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="n">shape</span><span class="o">=</span><span class="n">i</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">i</span><span class="o">.</span><span class="n">dtype</span><span class="p">,</span> <span class="n">ctx</span><span class="o">=</span><span class="n">i</span><span class="o">.</span><span class="n">ctx</span><span class="p">,</span> |
| <span class="n">stype</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">_grad_stype</span><span class="p">)</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">_data</span><span class="p">]</span> |
| |
| <span class="n">autograd</span><span class="o">.</span><span class="n">mark_variables</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_check_and_get</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_data</span><span class="p">,</span> <span class="nb">list</span><span class="p">),</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_grad</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">grad_req</span><span class="p">)</span> |
| |
| <span class="k">def</span> <span class="nf">_reduce</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span> |
| <span class="sd">"""Reduce data from multiple context to cpu."""</span> |
| <span class="n">ctx</span> <span class="o">=</span> <span class="n">context</span><span class="o">.</span><span class="n">cpu</span><span class="p">()</span> |
| <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_stype</span> <span class="o">==</span> <span class="s1">'default'</span><span class="p">:</span> |
| <span class="n">block</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">list_data</span><span class="p">()</span> |
| <span class="k">if</span> <span class="n">is_np_array</span><span class="p">():</span> |
| <span class="n">data</span> <span class="o">=</span> <span class="nb">sum</span><span class="p">([</span><span class="n">w</span><span class="o">.</span><span class="n">copyto</span><span class="p">(</span><span class="n">ctx</span><span class="p">)</span> <span class="k">for</span> <span class="n">w</span> <span class="ow">in</span> <span class="n">block</span><span class="p">])</span> <span class="o">/</span> <span class="nb">len</span><span class="p">(</span><span class="n">block</span><span class="p">)</span> |
| <span class="k">else</span><span class="p">:</span> |
| <span class="n">data</span> <span class="o">=</span> <span class="n">ndarray</span><span class="o">.</span><span class="n">add_n</span><span class="p">(</span><span class="o">*</span><span class="p">(</span><span class="n">w</span><span class="o">.</span><span class="n">copyto</span><span class="p">(</span><span class="n">ctx</span><span class="p">)</span> <span class="k">for</span> <span class="n">w</span> <span class="ow">in</span> <span class="n">block</span><span class="p">))</span> <span class="o">/</span> <span class="nb">len</span><span class="p">(</span><span class="n">block</span><span class="p">)</span> |
| <span class="k">else</span><span class="p">:</span> |
| <span class="c1"># fetch all rows for 'row_sparse' param</span> |
| <span class="n">all_row_ids</span> <span class="o">=</span> <span class="n">ndarray</span><span class="o">.</span><span class="n">arange</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">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">dtype</span><span class="o">=</span><span class="s1">'int64'</span><span class="p">,</span> <span class="n">ctx</span><span class="o">=</span><span class="n">ctx</span><span class="p">)</span> |
| <span class="n">data</span> <span class="o">=</span> <span class="n">ndarray</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span> <span class="n">stype</span><span class="o">=</span><span class="s1">'row_sparse'</span><span class="p">,</span> <span class="n">ctx</span><span class="o">=</span><span class="n">ctx</span><span class="p">)</span> |
| <span class="n">trainer</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_trainer</span><span class="p">()</span> <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_trainer</span> <span class="k">else</span> <span class="kc">None</span> |
| <span class="k">if</span> <span class="ow">not</span> <span class="n">trainer</span><span class="p">:</span> |
| <span class="k">raise</span> <span class="ne">RuntimeError</span><span class="p">(</span><span class="s2">"Cannot reduce row_sparse data for Parameter '</span><span class="si">%s</span><span class="s2">' when no "</span> \ |
| <span class="s2">"Trainer is created with it."</span><span class="o">%</span><span class="bp">self</span><span class="o">.</span><span class="n">name</span><span class="p">)</span> |
| <span class="n">trainer</span><span class="o">.</span><span class="n">_row_sparse_pull</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data</span><span class="p">,</span> <span class="n">all_row_ids</span><span class="p">,</span> <span class="n">full_idx</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span> |
| <span class="k">return</span> <span class="n">data</span> |
| |
| <div class="viewcode-block" id="Parameter.initialize"><a class="viewcode-back" href="../../../api/gluon/parameter.html#mxnet.gluon.Parameter.initialize">[docs]</a> <span class="k">def</span> <span class="nf">initialize</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">init</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">ctx</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">default_init</span><span class="o">=</span><span class="n">initializer</span><span class="o">.</span><span class="n">Uniform</span><span class="p">(),</span> |
| <span class="n">force_reinit</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span> |
| <span class="sd">"""Initializes parameter and gradient arrays. Only used for :py:class:`NDArray` API.</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> init : Initializer</span> |
| <span class="sd"> The initializer to use. Overrides :py:meth:`Parameter.init` and default_init.</span> |
| <span class="sd"> ctx : Context or list of Context, defaults to :py:meth:`context.current_context()`.</span> |
| <span class="sd"> Initialize Parameter on given context. If ctx is a list of Context, a</span> |
| <span class="sd"> copy will be made for each context.</span> |
| |
| <span class="sd"> .. note::</span> |
| <span class="sd"> Copies are independent arrays. User is responsible for keeping</span> |
| <span class="sd"> their values consistent when updating.</span> |
| <span class="sd"> Normally :py:class:`gluon.Trainer` does this for you.</span> |
| |
| <span class="sd"> default_init : Initializer</span> |
| <span class="sd"> Default initializer is used when both :py:func:`init`</span> |
| <span class="sd"> and :py:meth:`Parameter.init` are ``None``.</span> |
| <span class="sd"> force_reinit : bool, default False</span> |
| <span class="sd"> Whether to force re-initialization if parameter is already initialized.</span> |
| <span class="sd"> Examples</span> |
| <span class="sd"> --------</span> |
| <span class="sd"> >>> weight = mx.gluon.Parameter('weight', shape=(2, 2))</span> |
| <span class="sd"> >>> weight.initialize(ctx=mx.cpu(0))</span> |
| <span class="sd"> >>> weight.data()</span> |
| <span class="sd"> [[-0.01068833 0.01729892]</span> |
| <span class="sd"> [ 0.02042518 -0.01618656]]</span> |
| <span class="sd"> <NDArray 2x2 @cpu(0)></span> |
| <span class="sd"> >>> weight.grad()</span> |
| <span class="sd"> [[ 0. 0.]</span> |
| <span class="sd"> [ 0. 0.]]</span> |
| <span class="sd"> <NDArray 2x2 @cpu(0)></span> |
| <span class="sd"> >>> weight.initialize(ctx=[mx.gpu(0), mx.gpu(1)])</span> |
| <span class="sd"> >>> weight.data(mx.gpu(0))</span> |
| <span class="sd"> [[-0.00873779 -0.02834515]</span> |
| <span class="sd"> [ 0.05484822 -0.06206018]]</span> |
| <span class="sd"> <NDArray 2x2 @gpu(0)></span> |
| <span class="sd"> >>> weight.data(mx.gpu(1))</span> |
| <span class="sd"> [[-0.00873779 -0.02834515]</span> |
| <span class="sd"> [ 0.05484822 -0.06206018]]</span> |
| <span class="sd"> <NDArray 2x2 @gpu(1)></span> |
| <span class="sd"> """</span> |
| <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_data</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="ow">and</span> <span class="ow">not</span> <span class="n">force_reinit</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">"Parameter '</span><span class="si">%s</span><span class="s2">' is already initialized, ignoring. "</span> \ |
| <span class="s2">"Set force_reinit=True to re-initialize."</span><span class="o">%</span><span class="bp">self</span><span class="o">.</span><span class="n">name</span><span class="p">,</span> |
| <span class="n">stacklevel</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span> |
| <span class="k">return</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_data</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_grad</span> <span class="o">=</span> <span class="kc">None</span> |
| <span class="k">if</span> <span class="n">ctx</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span> |
| <span class="n">ctx</span> <span class="o">=</span> <span class="p">[</span><span class="n">context</span><span class="o">.</span><span class="n">current_context</span><span class="p">()]</span> |
| <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">ctx</span><span class="p">,</span> <span class="n">Context</span><span class="p">):</span> |
| <span class="n">ctx</span> <span class="o">=</span> <span class="p">[</span><span class="n">ctx</span><span class="p">]</span> |
| <span class="k">if</span> <span class="n">init</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span> |
| <span class="n">init</span> <span class="o">=</span> <span class="n">default_init</span> <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">init</span> <span class="ow">is</span> <span class="kc">None</span> <span class="k">else</span> <span class="bp">self</span><span class="o">.</span><span class="n">init</span> |
| <span class="k">if</span> <span class="ow">not</span> <span class="n">shape_is_known</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">shape</span><span class="p">):</span> |
| <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_allow_deferred_init</span><span class="p">:</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_deferred_init</span> <span class="o">=</span> <span class="p">(</span><span class="n">init</span><span class="p">,</span> <span class="n">ctx</span><span class="p">,</span> <span class="n">default_init</span><span class="p">,</span> <span class="kc">None</span><span class="p">)</span> |
| <span class="k">return</span> |
| <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">"Cannot initialize Parameter '</span><span class="si">%s</span><span class="s2">' because it has "</span> \ |
| <span class="s2">"invalid shape: </span><span class="si">%s</span><span class="s2">."</span><span class="o">%</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">name</span><span class="p">,</span> <span class="nb">str</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">shape</span><span class="p">)))</span> |
| |
| <span class="bp">self</span><span class="o">.</span><span class="n">_deferred_init</span> <span class="o">=</span> <span class="p">(</span><span class="n">init</span><span class="p">,</span> <span class="n">ctx</span><span class="p">,</span> <span class="n">default_init</span><span class="p">,</span> <span class="kc">None</span><span class="p">)</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_finish_deferred_init</span><span class="p">()</span></div> |
| |
| <div class="viewcode-block" id="Parameter.reset_ctx"><a class="viewcode-back" href="../../../api/gluon/parameter.html#mxnet.gluon.Parameter.reset_ctx">[docs]</a> <span class="k">def</span> <span class="nf">reset_ctx</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">ctx</span><span class="p">):</span> |
| <span class="sd">"""Re-assign Parameter to other contexts.</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> ctx : Context or list of Context, default ``context.current_context()``.</span> |
| <span class="sd"> Assign Parameter to given context. If ctx is a list of Context, a</span> |
| <span class="sd"> copy will be made for each context.</span> |
| <span class="sd"> """</span> |
| <span class="k">if</span> <span class="n">ctx</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span> |
| <span class="n">ctx</span> <span class="o">=</span> <span class="p">[</span><span class="n">context</span><span class="o">.</span><span class="n">current_context</span><span class="p">()]</span> |
| <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">ctx</span><span class="p">,</span> <span class="n">Context</span><span class="p">):</span> |
| <span class="n">ctx</span> <span class="o">=</span> <span class="p">[</span><span class="n">ctx</span><span class="p">]</span> |
| <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_data</span><span class="p">:</span> |
| <span class="n">data</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_reduce</span><span class="p">()</span> |
| <span class="k">with</span> <span class="n">autograd</span><span class="o">.</span><span class="n">pause</span><span class="p">():</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_init_impl</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">ctx</span><span class="p">)</span> |
| <span class="k">elif</span> <span class="bp">self</span><span class="o">.</span><span class="n">_deferred_init</span><span class="p">:</span> |
| <span class="n">init</span><span class="p">,</span> <span class="n">_</span><span class="p">,</span> <span class="n">default_init</span><span class="p">,</span> <span class="n">data</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_deferred_init</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_deferred_init</span> <span class="o">=</span> <span class="p">(</span><span class="n">init</span><span class="p">,</span> <span class="n">ctx</span><span class="p">,</span> <span class="n">default_init</span><span class="p">,</span> <span class="n">data</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="s2">"Cannot reset context for Parameter '</span><span class="si">%s</span><span class="s2">' because it "</span> |
| <span class="s2">"has not been initialized."</span><span class="o">%</span><span class="bp">self</span><span class="o">.</span><span class="n">name</span><span class="p">)</span></div> |
| |
| <div class="viewcode-block" id="Parameter.set_data"><a class="viewcode-back" href="../../../api/gluon/parameter.html#mxnet.gluon.Parameter.set_data">[docs]</a> <span class="k">def</span> <span class="nf">set_data</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data</span><span class="p">):</span> |
| <span class="sd">"""Sets this parameter's value on all contexts."""</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">shape</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">shape</span> |
| |
| <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_data</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span> |
| <span class="k">assert</span> <span class="bp">self</span><span class="o">.</span><span class="n">_deferred_init</span><span class="p">,</span> \ |
| <span class="s2">"Parameter '</span><span class="si">%s</span><span class="s2">' has not been initialized"</span><span class="o">%</span><span class="bp">self</span><span class="o">.</span><span class="n">name</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_deferred_init</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_deferred_init</span><span class="p">[:</span><span class="mi">3</span><span class="p">]</span> <span class="o">+</span> <span class="p">(</span><span class="n">data</span><span class="p">,)</span> |
| <span class="k">return</span> |
| |
| <span class="c1"># if update_on_kvstore, we need to make sure the copy stored in kvstore is in sync</span> |
| <span class="n">trainer</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_trainer</span><span class="p">()</span> <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_trainer</span> <span class="k">else</span> <span class="kc">None</span> |
| <span class="k">if</span> <span class="n">trainer</span> <span class="ow">and</span> <span class="n">trainer</span><span class="o">.</span><span class="n">_kv_initialized</span> <span class="ow">and</span> <span class="n">trainer</span><span class="o">.</span><span class="n">_update_on_kvstore</span><span class="p">:</span> |
| <span class="k">if</span> <span class="bp">self</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">trainer</span><span class="o">.</span><span class="n">_params_to_init</span><span class="p">:</span> |
| <span class="n">trainer</span><span class="o">.</span><span class="n">_reset_kvstore</span><span class="p">()</span> |
| |
| <span class="k">for</span> <span class="n">arr</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">_check_and_get</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_data</span><span class="p">,</span> <span class="nb">list</span><span class="p">):</span> |
| <span class="n">arr</span><span class="p">[:]</span> <span class="o">=</span> <span class="n">data</span></div> |
| |
| <div class="viewcode-block" id="Parameter.row_sparse_data"><a class="viewcode-back" href="../../../api/gluon/parameter.html#mxnet.gluon.Parameter.row_sparse_data">[docs]</a> <span class="k">def</span> <span class="nf">row_sparse_data</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">row_id</span><span class="p">):</span> |
| <span class="sd">"""Returns a copy of the 'row_sparse' parameter on the same context as row_id's.</span> |
| <span class="sd"> The copy only retains rows whose ids occur in provided row ids.</span> |
| <span class="sd"> The parameter must have been initialized on this context before.</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> row_id: NDArray</span> |
| <span class="sd"> Row ids to retain for the 'row_sparse' parameter.</span> |
| |
| <span class="sd"> Returns</span> |
| <span class="sd"> -------</span> |
| <span class="sd"> NDArray on row_id's context</span> |
| <span class="sd"> """</span> |
| <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_stype</span> <span class="o">!=</span> <span class="s1">'row_sparse'</span><span class="p">:</span> |
| <span class="k">raise</span> <span class="ne">RuntimeError</span><span class="p">(</span><span class="s2">"Cannot return a copy of Parameter </span><span class="si">%s</span><span class="s2"> via row_sparse_data() "</span> \ |
| <span class="s2">"because its storage type is </span><span class="si">%s</span><span class="s2">. Please use data() instead."</span> \ |
| <span class="o">%</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">name</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">_stype</span><span class="p">))</span> |
| <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_row_sparse</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_data</span><span class="p">,</span> <span class="n">row_id</span><span class="o">.</span><span class="n">ctx</span><span class="p">,</span> <span class="n">row_id</span><span class="p">)</span></div> |
| |
| <div class="viewcode-block" id="Parameter.list_row_sparse_data"><a class="viewcode-back" href="../../../api/gluon/parameter.html#mxnet.gluon.Parameter.list_row_sparse_data">[docs]</a> <span class="k">def</span> <span class="nf">list_row_sparse_data</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">row_id</span><span class="p">):</span> |
| <span class="sd">"""Returns copies of the 'row_sparse' parameter on all contexts, in the same order</span> |
| <span class="sd"> as creation. The copy only retains rows whose ids occur in provided row ids.</span> |
| <span class="sd"> The parameter must have been initialized before.</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> row_id: NDArray</span> |
| <span class="sd"> Row ids to retain for the 'row_sparse' parameter.</span> |
| |
| <span class="sd"> Returns</span> |
| <span class="sd"> -------</span> |
| <span class="sd"> list of NDArrays</span> |
| <span class="sd"> """</span> |
| <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_stype</span> <span class="o">!=</span> <span class="s1">'row_sparse'</span><span class="p">:</span> |
| <span class="k">raise</span> <span class="ne">RuntimeError</span><span class="p">(</span><span class="s2">"Cannot return copies of Parameter '</span><span class="si">%s</span><span class="s2">' on all contexts via "</span> \ |
| <span class="s2">"list_row_sparse_data() because its storage type is </span><span class="si">%s</span><span class="s2">. Please "</span> \ |
| <span class="s2">"use data() instead."</span> <span class="o">%</span> <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">name</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">_stype</span><span class="p">))</span> |
| <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_row_sparse</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_data</span><span class="p">,</span> <span class="nb">list</span><span class="p">,</span> <span class="n">row_id</span><span class="p">)</span></div> |
| |
| <div class="viewcode-block" id="Parameter.data"><a class="viewcode-back" href="../../../api/gluon/parameter.html#mxnet.gluon.Parameter.data">[docs]</a> <span class="k">def</span> <span class="nf">data</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">ctx</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> |
| <span class="sd">"""Returns a copy of this parameter on one context. Must have been</span> |
| <span class="sd"> initialized on this context before. For sparse parameters, use</span> |
| <span class="sd"> :py:meth:`Parameter.row_sparse_data` instead.</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> ctx : Context</span> |
| <span class="sd"> Desired context.</span> |
| |
| <span class="sd"> Returns</span> |
| <span class="sd"> -------</span> |
| <span class="sd"> NDArray on ctx</span> |
| <span class="sd"> """</span> |
| <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_stype</span> <span class="o">!=</span> <span class="s1">'default'</span><span class="p">:</span> |
| <span class="k">raise</span> <span class="ne">RuntimeError</span><span class="p">(</span><span class="s2">"Cannot return a copy of Parameter '</span><span class="si">%s</span><span class="s2">' on ctx </span><span class="si">%s</span><span class="s2"> via data() "</span> \ |
| <span class="s2">"because its storage type is </span><span class="si">%s</span><span class="s2">. Please use row_sparse_data() "</span> \ |
| <span class="s2">"instead."</span> <span class="o">%</span> <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">name</span><span class="p">,</span> <span class="nb">str</span><span class="p">(</span><span class="n">ctx</span><span class="p">),</span> <span class="bp">self</span><span class="o">.</span><span class="n">_stype</span><span class="p">))</span> |
| <span class="n">data</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_check_and_get</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_data</span><span class="p">,</span> <span class="n">ctx</span><span class="p">)</span> |
| <span class="n">dc</span><span class="o">.</span><span class="n">set_variable</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">var</span><span class="p">())</span> |
| <span class="k">return</span> <span class="n">data</span></div> |
| |
| <div class="viewcode-block" id="Parameter.list_data"><a class="viewcode-back" href="../../../api/gluon/parameter.html#mxnet.gluon.Parameter.list_data">[docs]</a> <span class="k">def</span> <span class="nf">list_data</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span> |
| <span class="sd">"""Returns copies of this parameter on all contexts, in the same order</span> |
| <span class="sd"> as creation. For sparse parameters, use :py:meth:`Parameter.list_row_sparse_data`</span> |
| <span class="sd"> instead.</span> |
| |
| <span class="sd"> Returns</span> |
| <span class="sd"> -------</span> |
| <span class="sd"> list of NDArrays</span> |
| <span class="sd"> """</span> |
| <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_stype</span> <span class="o">!=</span> <span class="s1">'default'</span><span class="p">:</span> |
| <span class="k">raise</span> <span class="ne">RuntimeError</span><span class="p">(</span><span class="s2">"Cannot return copies of Parameter '</span><span class="si">%s</span><span class="s2">' on all contexts via "</span> \ |
| <span class="s2">"list_data() because its storage type is </span><span class="si">%s</span><span class="s2">. Please use "</span> \ |
| <span class="s2">"row_sparse_data() instead."</span> <span class="o">%</span> <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">name</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">_stype</span><span class="p">))</span> |
| <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_check_and_get</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_data</span><span class="p">,</span> <span class="nb">list</span><span class="p">)</span></div> |
| |
| <div class="viewcode-block" id="Parameter.grad"><a class="viewcode-back" href="../../../api/gluon/parameter.html#mxnet.gluon.Parameter.grad">[docs]</a> <span class="k">def</span> <span class="nf">grad</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">ctx</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> |
| <span class="sd">"""Returns a gradient buffer for this parameter on one context.</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> ctx : Context</span> |
| <span class="sd"> Desired context.</span> |
| <span class="sd"> """</span> |
| <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_data</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="ow">and</span> <span class="bp">self</span><span class="o">.</span><span class="n">_grad</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span> |
| <span class="k">raise</span> <span class="ne">RuntimeError</span><span class="p">(</span> |
| <span class="s2">"Cannot get gradient array for Parameter '</span><span class="si">%s</span><span class="s2">' "</span> \ |
| <span class="s2">"because grad_req='null'"</span><span class="o">%</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">name</span><span class="p">))</span> |
| <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_check_and_get</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_grad</span><span class="p">,</span> <span class="n">ctx</span><span class="p">)</span></div> |
| |
| <div class="viewcode-block" id="Parameter.list_grad"><a class="viewcode-back" href="../../../api/gluon/parameter.html#mxnet.gluon.Parameter.list_grad">[docs]</a> <span class="k">def</span> <span class="nf">list_grad</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span> |
| <span class="sd">"""Returns gradient buffers on all contexts, in the same order</span> |
| <span class="sd"> as :py:meth:`values`."""</span> |
| <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_data</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="ow">and</span> <span class="bp">self</span><span class="o">.</span><span class="n">_grad</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span> |
| <span class="k">raise</span> <span class="ne">RuntimeError</span><span class="p">(</span> |
| <span class="s2">"Cannot get gradient array for Parameter '</span><span class="si">%s</span><span class="s2">' "</span> \ |
| <span class="s2">"because grad_req='null'"</span><span class="o">%</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">name</span><span class="p">))</span> |
| <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_check_and_get</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_grad</span><span class="p">,</span> <span class="nb">list</span><span class="p">)</span></div> |
| |
| <div class="viewcode-block" id="Parameter.list_ctx"><a class="viewcode-back" href="../../../api/gluon/parameter.html#mxnet.gluon.Parameter.list_ctx">[docs]</a> <span class="k">def</span> <span class="nf">list_ctx</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span> |
| <span class="sd">"""Returns a list of contexts this parameter is initialized on."""</span> |
| <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_data</span> <span class="ow">is</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">_deferred_init</span><span class="p">:</span> |
| <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_deferred_init</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> |
| <span class="k">raise</span> <span class="ne">RuntimeError</span><span class="p">(</span><span class="s2">"Parameter '</span><span class="si">%s</span><span class="s2">' has not been initialized"</span><span class="o">%</span><span class="bp">self</span><span class="o">.</span><span class="n">name</span><span class="p">)</span> |
| <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_ctx_list</span></div> |
| |
| <div class="viewcode-block" id="Parameter.zero_grad"><a class="viewcode-back" href="../../../api/gluon/parameter.html#mxnet.gluon.Parameter.zero_grad">[docs]</a> <span class="k">def</span> <span class="nf">zero_grad</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span> |
| <span class="sd">"""Sets gradient buffer on all contexts to 0. No action is taken if</span> |
| <span class="sd"> parameter is uninitialized or doesn't require gradient."""</span> |
| <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_grad</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span> |
| <span class="k">return</span> |
| <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">_grad</span><span class="p">:</span> |
| <span class="n">ndarray</span><span class="o">.</span><span class="n">zeros_like</span><span class="p">(</span><span class="n">i</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="n">i</span><span class="p">)</span></div> |
| |
| <div class="viewcode-block" id="Parameter.var"><a class="viewcode-back" href="../../../api/gluon/parameter.html#mxnet.gluon.Parameter.var">[docs]</a> <span class="k">def</span> <span class="nf">var</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span> |
| <span class="sd">"""Returns a symbol representing this parameter."""</span> |
| <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_var</span> <span class="ow">is</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">_var_name</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span> <span class="c1"># _var_name is set manually in SymbolBlock.import</span> |
| <span class="c1"># The variable name is required by the storage profiler.</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_var_name</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_uuid</span><span class="o">.</span><span class="n">replace</span><span class="p">(</span><span class="s1">'-'</span><span class="p">,</span> <span class="s1">'_'</span><span class="p">)</span> <span class="o">+</span> <span class="s1">'_'</span> <span class="o">+</span> <span class="bp">self</span><span class="o">.</span><span class="n">_name</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_var</span> <span class="o">=</span> <span class="n">symbol</span><span class="o">.</span><span class="n">var</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_var_name</span><span class="p">,</span> <span class="n">shape</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">dtype</span><span class="p">,</span> |
| <span class="n">lr_mult</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">wd_mult</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">init</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">init</span><span class="p">,</span> <span class="n">stype</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">_stype</span><span class="p">)</span> |
| <span class="k">if</span> <span class="n">is_np_array</span><span class="p">():</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_var</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_var</span><span class="o">.</span><span class="n">as_np_ndarray</span><span class="p">()</span> |
| <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_var</span></div> |
| |
| <div class="viewcode-block" id="Parameter.cast"><a class="viewcode-back" href="../../../api/gluon/parameter.html#mxnet.gluon.Parameter.cast">[docs]</a> <span class="k">def</span> <span class="nf">cast</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">dtype</span><span class="p">):</span> |
| <span class="sd">"""Cast data and gradient of this Parameter to a new data type.</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> dtype : str or numpy.dtype</span> |
| <span class="sd"> The new data type.</span> |
| <span class="sd"> """</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_dtype</span> <span class="o">=</span> <span class="n">dtype</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_var</span> <span class="o">=</span> <span class="kc">None</span> <span class="c1"># Clear Symbol Variable as it caches the dtype</span> |
| <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_data</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span> |
| <span class="k">return</span> |
| <span class="k">with</span> <span class="n">autograd</span><span class="o">.</span><span class="n">pause</span><span class="p">():</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_data</span> <span class="o">=</span> <span class="p">[</span><span class="n">i</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="n">dtype</span><span class="p">)</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">_data</span><span class="p">]</span> |
| <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_grad</span> <span class="ow">is</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">_grad</span> <span class="o">=</span> <span class="p">[</span><span class="n">i</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="n">dtype</span><span class="p">)</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">_grad</span><span class="p">]</span> |
| <span class="n">autograd</span><span class="o">.</span><span class="n">mark_variables</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_data</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">_grad</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">grad_req</span><span class="p">)</span></div> |
| |
| <span class="k">def</span> <span class="nf">_check_and_setattr</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="sd">"""check and set attributes for parameter"""</span> |
| <span class="k">for</span> <span class="n">k</span><span class="p">,</span> <span class="n">v</span> <span class="ow">in</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">items</span><span class="p">():</span> |
| <span class="k">if</span> <span class="nb">hasattr</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">k</span><span class="p">)</span> <span class="ow">and</span> <span class="nb">getattr</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">k</span><span class="p">)</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span> |
| <span class="n">existing</span> <span class="o">=</span> <span class="nb">getattr</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">k</span><span class="p">)</span> |
| <span class="k">if</span> <span class="n">k</span> <span class="o">==</span> <span class="s1">'shape'</span> <span class="ow">and</span> <span class="nb">len</span><span class="p">(</span><span class="n">v</span><span class="p">)</span> <span class="o">==</span> <span class="nb">len</span><span class="p">(</span><span class="n">existing</span><span class="p">):</span> |
| <span class="n">inferred_shape</span> <span class="o">=</span> <span class="p">[]</span> |
| <span class="n">matched</span> <span class="o">=</span> <span class="kc">True</span> |
| <span class="k">for</span> <span class="n">dim1</span><span class="p">,</span> <span class="n">dim2</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="n">v</span><span class="p">,</span> <span class="n">existing</span><span class="p">):</span> |
| <span class="k">if</span> <span class="n">dim1</span> <span class="o">!=</span> <span class="n">dim2</span> <span class="ow">and</span> <span class="n">dim1</span> <span class="o">></span> <span class="mi">0</span> <span class="ow">and</span> <span class="n">dim2</span> <span class="o">></span> <span class="mi">0</span><span class="p">:</span> |
| <span class="n">matched</span> <span class="o">=</span> <span class="kc">False</span> |
| <span class="k">break</span> |
| <span class="k">elif</span> <span class="n">dim1</span> <span class="o">==</span> <span class="n">dim2</span><span class="p">:</span> |
| <span class="n">inferred_shape</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">dim1</span><span class="p">)</span> |
| <span class="k">elif</span> <span class="n">dim1</span> <span class="ow">in</span> <span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">):</span> <span class="c1"># -1 means unknown dim size in np_shape mode</span> |
| <span class="n">inferred_shape</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">dim2</span><span class="p">)</span> |
| <span class="k">else</span><span class="p">:</span> |
| <span class="n">inferred_shape</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">dim1</span><span class="p">)</span> |
| |
| <span class="k">if</span> <span class="n">matched</span><span class="p">:</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_shape</span> <span class="o">=</span> <span class="nb">tuple</span><span class="p">(</span><span class="n">inferred_shape</span><span class="p">)</span> |
| <span class="k">continue</span> |
| <span class="k">elif</span> <span class="n">k</span> <span class="o">==</span> <span class="s1">'dtype'</span> <span class="ow">and</span> <span class="n">np</span><span class="o">.</span><span class="n">dtype</span><span class="p">(</span><span class="n">v</span><span class="p">)</span> <span class="o">==</span> <span class="n">np</span><span class="o">.</span><span class="n">dtype</span><span class="p">(</span><span class="n">existing</span><span class="p">):</span> |
| <span class="k">continue</span> |
| |
| <span class="k">assert</span> <span class="n">v</span> <span class="ow">is</span> <span class="kc">None</span> <span class="ow">or</span> <span class="n">v</span> <span class="o">==</span> <span class="n">existing</span><span class="p">,</span> \ |
| <span class="s2">"Cannot retrieve Parameter '</span><span class="si">%s</span><span class="s2">' because desired attribute "</span> \ |
| <span class="s2">"does not match with stored for attribute '</span><span class="si">%s</span><span class="s2">': "</span> \ |
| <span class="s2">"desired '</span><span class="si">%s</span><span class="s2">' vs stored '</span><span class="si">%s</span><span class="s2">'."</span><span class="o">%</span><span class="p">(</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">name</span><span class="p">,</span> <span class="n">k</span><span class="p">,</span> <span class="nb">str</span><span class="p">(</span><span class="n">v</span><span class="p">),</span> <span class="nb">str</span><span class="p">(</span><span class="nb">getattr</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">k</span><span class="p">)))</span> |
| <span class="k">else</span><span class="p">:</span> |
| <span class="nb">setattr</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">k</span><span class="p">,</span> <span class="n">v</span><span class="p">)</span></div> |
| |
| <span class="k">class</span> <span class="nc">Constant</span><span class="p">(</span><span class="n">Parameter</span><span class="p">):</span> |
| <span class="sd">"""A constant parameter for holding immutable tensors.</span> |
| <span class="sd"> `Constant`s are ignored by `autograd` and `Trainer`, thus their values</span> |
| <span class="sd"> will not change during training. But you can still update their values</span> |
| <span class="sd"> manually with the `set_data` method.</span> |
| |
| <span class="sd"> `Constant` s can be created with either::</span> |
| |
| <span class="sd"> const = mx.gluon.Constant([[1,2],[3,4]])</span> |
| |
| <span class="sd"> or::</span> |
| |
| <span class="sd"> class Block(gluon.Block):</span> |
| <span class="sd"> def __init__(self, **kwargs):</span> |
| <span class="sd"> super(Block, self).__init__(**kwargs)</span> |
| <span class="sd"> self.const = mx.gluon.Constant([[1,2],[3,4]])</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> value : array-like</span> |
| <span class="sd"> Initial value for the constant.</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">value</span><span class="p">):</span> |
| <span class="k">if</span> <span class="ow">not</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">value</span><span class="p">,</span> <span class="n">ndarray</span><span class="o">.</span><span class="n">NDArray</span><span class="p">):</span> |
| <span class="n">array_fn</span> <span class="o">=</span> <span class="n">_mx_np</span><span class="o">.</span><span class="n">array</span> <span class="k">if</span> <span class="n">is_np_array</span><span class="p">()</span> <span class="k">else</span> <span class="n">ndarray</span><span class="o">.</span><span class="n">array</span> |
| <span class="n">value</span> <span class="o">=</span> <span class="n">array_fn</span><span class="p">(</span><span class="n">value</span><span class="p">)</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">value</span> <span class="o">=</span> <span class="n">value</span> |
| |
| <span class="k">class</span> <span class="nc">Init</span><span class="p">(</span><span class="n">initializer</span><span class="o">.</span><span class="n">Initializer</span><span class="p">):</span> |
| <span class="k">def</span> <span class="nf">_init_weight</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">_</span><span class="p">,</span> <span class="n">arr</span><span class="p">):</span> |
| <span class="n">value</span><span class="o">.</span><span class="n">copyto</span><span class="p">(</span><span class="n">arr</span><span class="p">)</span> |
| <span class="n">init_name</span> <span class="o">=</span> <span class="s1">'Constant_</span><span class="si">{}</span><span class="s1">'</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="nb">id</span><span class="p">(</span><span class="bp">self</span><span class="p">))</span> |
| <span class="n">initializer</span><span class="o">.</span><span class="n">alias</span><span class="p">(</span><span class="n">init_name</span><span class="p">)(</span><span class="n">Init</span><span class="p">)</span> |
| |
| <span class="nb">super</span><span class="p">(</span><span class="n">Constant</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="n">name</span><span class="o">=</span><span class="s1">'const'</span><span class="p">,</span> <span class="n">grad_req</span><span class="o">=</span><span class="s1">'null'</span><span class="p">,</span> <span class="n">shape</span><span class="o">=</span><span class="n">value</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">value</span><span class="o">.</span><span class="n">dtype</span><span class="p">,</span> |
| <span class="n">init</span><span class="o">=</span><span class="n">init_name</span><span class="p">)</span> |
| |
| <span class="k">def</span> <span class="fm">__repr__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span> |
| <span class="n">s</span> <span class="o">=</span> <span class="s1">'Constant (shape=</span><span class="si">{shape}</span><span class="s1">, dtype=</span><span class="si">{dtype}</span><span class="s1">)'</span> |
| <span class="k">return</span> <span class="n">s</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">shape</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">dtype</span><span class="p">)</span> |
| |
| <span class="nd">@property</span> |
| <span class="k">def</span> <span class="nf">grad_req</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span> |
| <span class="k">return</span> <span class="s1">'null'</span> |
| |
| <span class="nd">@grad_req</span><span class="o">.</span><span class="n">setter</span> |
| <span class="k">def</span> <span class="nf">grad_req</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">req</span><span class="p">):</span> |
| <span class="k">if</span> <span class="n">req</span> <span class="o">!=</span> <span class="s1">'null'</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">'Constant parameter "</span><span class="si">{}</span><span class="s1">" does not support '</span> |
| <span class="s1">'grad_req other than "null", and new value "</span><span class="si">{}</span><span class="s1">" '</span> |
| <span class="s1">'is ignored.'</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">name</span><span class="p">,</span> <span class="n">req</span><span class="p">))</span> |
| </pre></div> |
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