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| <span class="mdl-layout-title toc">Table Of Contents</span> |
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| <ul class="current"> |
| <li class="toctree-l1"><a class="reference internal" href="../../tutorials/index.html">Python Tutorials</a><ul> |
| <li class="toctree-l2"><a class="reference internal" href="../../tutorials/getting-started/index.html">Getting Started</a><ul> |
| <li class="toctree-l3"><a class="reference internal" href="../../tutorials/getting-started/crash-course/index.html">Crash Course</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/0-introduction.html">Introduction</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/1-nparray.html">Step 1: Manipulate data with NP on MXNet</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/2-create-nn.html">Step 2: Create a neural network</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/3-autograd.html">Step 3: Automatic differentiation with autograd</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/4-components.html">Step 4: Necessary components that are not in the network</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/5-datasets.html">Step 5: <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/5-datasets.html#Using-own-data-with-included-Datasets">Using own data with included <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/5-datasets.html#Using-your-own-data-with-custom-Datasets">Using your own data with custom <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/5-datasets.html#New-in-MXNet-2.0:-faster-C++-backend-dataloaders">New in MXNet 2.0: faster C++ backend dataloaders</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/6-train-nn.html">Step 6: Train a Neural Network</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/7-use-gpus.html">Step 7: Load and Run a NN using GPU</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l3"><a class="reference internal" href="../../tutorials/getting-started/to-mxnet/index.html">Moving to MXNet from Other Frameworks</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/to-mxnet/pytorch.html">PyTorch vs Apache MXNet</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l3"><a class="reference internal" href="../../tutorials/getting-started/gluon_from_experiment_to_deployment.html">Gluon: from experiment to deployment</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../tutorials/getting-started/gluon_migration_guide.html">Gluon2.0: Migration Guide</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../tutorials/getting-started/logistic_regression_explained.html">Logistic regression explained</a></li> |
| <li class="toctree-l3"><a class="reference external" href="https://mxnet.apache.org/api/python/docs/tutorials/packages/gluon/image/mnist.html">MNIST</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l2"><a class="reference internal" href="../../tutorials/packages/index.html">Packages</a><ul> |
| <li class="toctree-l3"><a class="reference internal" href="../../tutorials/packages/autograd/index.html">Automatic Differentiation</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../tutorials/packages/gluon/index.html">Gluon</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../tutorials/packages/gluon/blocks/index.html">Blocks</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../tutorials/packages/gluon/blocks/custom-layer.html">Custom Layers</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../tutorials/packages/gluon/blocks/hybridize.html">Hybridize</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../tutorials/packages/gluon/blocks/init.html">Initialization</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../tutorials/packages/gluon/blocks/naming.html">Parameter and Block Naming</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../tutorials/packages/gluon/blocks/nn.html">Layers and Blocks</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../tutorials/packages/gluon/blocks/parameters.html">Parameter Management</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../tutorials/packages/gluon/blocks/save_load_params.html">Saving and Loading Gluon Models</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../tutorials/packages/gluon/blocks/activations/activations.html">Activation Blocks</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l4"><a class="reference internal" href="../../tutorials/packages/gluon/data/index.html">Data Tutorials</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../tutorials/packages/gluon/data/data_augmentation.html">Image Augmentation</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../tutorials/packages/gluon/data/datasets.html">Gluon <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../tutorials/packages/gluon/data/datasets.html#Using-own-data-with-included-Datasets">Using own data with included <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../tutorials/packages/gluon/data/datasets.html#Using-own-data-with-custom-Datasets">Using own data with custom <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../tutorials/packages/gluon/data/datasets.html#Appendix:-Upgrading-from-Module-DataIter-to-Gluon-DataLoader">Appendix: Upgrading from Module <code class="docutils literal notranslate"><span class="pre">DataIter</span></code> to Gluon <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li> |
| </ul> |
| </li> |
| <li class="toctree-l4"><a class="reference internal" href="../../tutorials/packages/gluon/image/index.html">Image Tutorials</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../tutorials/packages/gluon/image/info_gan.html">Image similarity search with InfoGAN</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../tutorials/packages/gluon/image/mnist.html">Handwritten Digit Recognition</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l4"><a class="reference internal" href="../../tutorials/packages/gluon/loss/index.html">Losses</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../tutorials/packages/gluon/loss/custom-loss.html">Custom Loss Blocks</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../tutorials/packages/gluon/loss/kl_divergence.html">Kullback-Leibler (KL) Divergence</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../tutorials/packages/gluon/loss/loss.html">Loss functions</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l4"><a class="reference internal" href="../../tutorials/packages/gluon/text/index.html">Text Tutorials</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../tutorials/packages/gluon/text/gnmt.html">Google Neural Machine Translation</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../tutorials/packages/gluon/text/transformer.html">Machine Translation with Transformer</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l4"><a class="reference internal" href="../../tutorials/packages/gluon/training/index.html">Training</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../tutorials/packages/gluon/training/fit_api_tutorial.html">MXNet Gluon Fit API</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../tutorials/packages/gluon/training/trainer.html">Trainer</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../tutorials/packages/gluon/training/learning_rates/index.html">Learning Rates</a><ul> |
| <li class="toctree-l6"><a class="reference internal" href="../../tutorials/packages/gluon/training/learning_rates/learning_rate_finder.html">Learning Rate Finder</a></li> |
| <li class="toctree-l6"><a class="reference internal" href="../../tutorials/packages/gluon/training/learning_rates/learning_rate_schedules.html">Learning Rate Schedules</a></li> |
| <li class="toctree-l6"><a class="reference internal" href="../../tutorials/packages/gluon/training/learning_rates/learning_rate_schedules_advanced.html">Advanced Learning Rate Schedules</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l5"><a class="reference internal" href="../../tutorials/packages/gluon/training/normalization/index.html">Normalization Blocks</a></li> |
| </ul> |
| </li> |
| </ul> |
| </li> |
| <li class="toctree-l3"><a class="reference internal" href="../../tutorials/packages/kvstore/index.html">KVStore</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../tutorials/packages/kvstore/kvstore.html">Distributed Key-Value Store</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l3"><a class="reference internal" href="../../tutorials/packages/legacy/index.html">Legacy</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../tutorials/packages/legacy/ndarray/index.html">NDArray</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../tutorials/packages/legacy/ndarray/01-ndarray-intro.html">An Intro: Manipulate Data the MXNet Way with NDArray</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../tutorials/packages/legacy/ndarray/02-ndarray-operations.html">NDArray Operations</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../tutorials/packages/legacy/ndarray/03-ndarray-contexts.html">NDArray Contexts</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../tutorials/packages/legacy/ndarray/gotchas_numpy_in_mxnet.html">Gotchas using NumPy in Apache MXNet</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../tutorials/packages/legacy/ndarray/sparse/index.html">Tutorials</a><ul> |
| <li class="toctree-l6"><a class="reference internal" href="../../tutorials/packages/legacy/ndarray/sparse/csr.html">CSRNDArray - NDArray in Compressed Sparse Row Storage Format</a></li> |
| <li class="toctree-l6"><a class="reference internal" href="../../tutorials/packages/legacy/ndarray/sparse/row_sparse.html">RowSparseNDArray - NDArray for Sparse Gradient Updates</a></li> |
| </ul> |
| </li> |
| </ul> |
| </li> |
| </ul> |
| </li> |
| <li class="toctree-l3"><a class="reference internal" href="../../tutorials/packages/np/index.html">What is NP on MXNet</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../tutorials/packages/np/cheat-sheet.html">The NP on MXNet cheat sheet</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../tutorials/packages/np/np-vs-numpy.html">Differences between NP on MXNet and NumPy</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l3"><a class="reference internal" href="../../tutorials/packages/onnx/index.html">ONNX</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../tutorials/packages/onnx/fine_tuning_gluon.html">Fine-tuning an ONNX model</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../tutorials/packages/onnx/inference_on_onnx_model.html">Running inference on MXNet/Gluon from an ONNX model</a></li> |
| <li class="toctree-l4"><a class="reference external" href="https://mxnet.apache.org/api/python/docs/tutorials/deploy/export/onnx.html">Export ONNX Models</a></li> |
| </ul> |
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| <li class="toctree-l2"><a class="reference internal" href="../npx/index.html">NPX: NumPy Neural Network Extension</a><ul> |
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| <span class="mdl-layout-title toc">Table Of Contents</span> |
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| <nav class="mdl-navigation"> |
| <ul class="current"> |
| <li class="toctree-l1"><a class="reference internal" href="../../tutorials/index.html">Python Tutorials</a><ul> |
| <li class="toctree-l2"><a class="reference internal" href="../../tutorials/getting-started/index.html">Getting Started</a><ul> |
| <li class="toctree-l3"><a class="reference internal" href="../../tutorials/getting-started/crash-course/index.html">Crash Course</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/0-introduction.html">Introduction</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/1-nparray.html">Step 1: Manipulate data with NP on MXNet</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/2-create-nn.html">Step 2: Create a neural network</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/3-autograd.html">Step 3: Automatic differentiation with autograd</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/4-components.html">Step 4: Necessary components that are not in the network</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/5-datasets.html">Step 5: <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/5-datasets.html#Using-own-data-with-included-Datasets">Using own data with included <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/5-datasets.html#Using-your-own-data-with-custom-Datasets">Using your own data with custom <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/5-datasets.html#New-in-MXNet-2.0:-faster-C++-backend-dataloaders">New in MXNet 2.0: faster C++ backend dataloaders</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/6-train-nn.html">Step 6: Train a Neural Network</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/7-use-gpus.html">Step 7: Load and Run a NN using GPU</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l3"><a class="reference internal" href="../../tutorials/getting-started/to-mxnet/index.html">Moving to MXNet from Other Frameworks</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/to-mxnet/pytorch.html">PyTorch vs Apache MXNet</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l3"><a class="reference internal" href="../../tutorials/getting-started/gluon_from_experiment_to_deployment.html">Gluon: from experiment to deployment</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../tutorials/getting-started/gluon_migration_guide.html">Gluon2.0: Migration Guide</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../tutorials/getting-started/logistic_regression_explained.html">Logistic regression explained</a></li> |
| <li class="toctree-l3"><a class="reference external" href="https://mxnet.apache.org/api/python/docs/tutorials/packages/gluon/image/mnist.html">MNIST</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l2"><a class="reference internal" href="../../tutorials/packages/index.html">Packages</a><ul> |
| <li class="toctree-l3"><a class="reference internal" href="../../tutorials/packages/autograd/index.html">Automatic Differentiation</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../tutorials/packages/gluon/index.html">Gluon</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../tutorials/packages/gluon/blocks/index.html">Blocks</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../tutorials/packages/gluon/blocks/custom-layer.html">Custom Layers</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../tutorials/packages/gluon/blocks/hybridize.html">Hybridize</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../tutorials/packages/gluon/blocks/init.html">Initialization</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../tutorials/packages/gluon/blocks/naming.html">Parameter and Block Naming</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../tutorials/packages/gluon/blocks/nn.html">Layers and Blocks</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../tutorials/packages/gluon/blocks/parameters.html">Parameter Management</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../tutorials/packages/gluon/blocks/save_load_params.html">Saving and Loading Gluon Models</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../tutorials/packages/gluon/blocks/activations/activations.html">Activation Blocks</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l4"><a class="reference internal" href="../../tutorials/packages/gluon/data/index.html">Data Tutorials</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../tutorials/packages/gluon/data/data_augmentation.html">Image Augmentation</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../tutorials/packages/gluon/data/datasets.html">Gluon <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../tutorials/packages/gluon/data/datasets.html#Using-own-data-with-included-Datasets">Using own data with included <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../tutorials/packages/gluon/data/datasets.html#Using-own-data-with-custom-Datasets">Using own data with custom <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../tutorials/packages/gluon/data/datasets.html#Appendix:-Upgrading-from-Module-DataIter-to-Gluon-DataLoader">Appendix: Upgrading from Module <code class="docutils literal notranslate"><span class="pre">DataIter</span></code> to Gluon <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li> |
| </ul> |
| </li> |
| <li class="toctree-l4"><a class="reference internal" href="../../tutorials/packages/gluon/image/index.html">Image Tutorials</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../tutorials/packages/gluon/image/info_gan.html">Image similarity search with InfoGAN</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../tutorials/packages/gluon/image/mnist.html">Handwritten Digit Recognition</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l4"><a class="reference internal" href="../../tutorials/packages/gluon/loss/index.html">Losses</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../tutorials/packages/gluon/loss/custom-loss.html">Custom Loss Blocks</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../tutorials/packages/gluon/loss/kl_divergence.html">Kullback-Leibler (KL) Divergence</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../tutorials/packages/gluon/loss/loss.html">Loss functions</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l4"><a class="reference internal" href="../../tutorials/packages/gluon/text/index.html">Text Tutorials</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../tutorials/packages/gluon/text/gnmt.html">Google Neural Machine Translation</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../tutorials/packages/gluon/text/transformer.html">Machine Translation with Transformer</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l4"><a class="reference internal" href="../../tutorials/packages/gluon/training/index.html">Training</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../tutorials/packages/gluon/training/fit_api_tutorial.html">MXNet Gluon Fit API</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../tutorials/packages/gluon/training/trainer.html">Trainer</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../tutorials/packages/gluon/training/learning_rates/index.html">Learning Rates</a><ul> |
| <li class="toctree-l6"><a class="reference internal" href="../../tutorials/packages/gluon/training/learning_rates/learning_rate_finder.html">Learning Rate Finder</a></li> |
| <li class="toctree-l6"><a class="reference internal" href="../../tutorials/packages/gluon/training/learning_rates/learning_rate_schedules.html">Learning Rate Schedules</a></li> |
| <li class="toctree-l6"><a class="reference internal" href="../../tutorials/packages/gluon/training/learning_rates/learning_rate_schedules_advanced.html">Advanced Learning Rate Schedules</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l5"><a class="reference internal" href="../../tutorials/packages/gluon/training/normalization/index.html">Normalization Blocks</a></li> |
| </ul> |
| </li> |
| </ul> |
| </li> |
| <li class="toctree-l3"><a class="reference internal" href="../../tutorials/packages/kvstore/index.html">KVStore</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../tutorials/packages/kvstore/kvstore.html">Distributed Key-Value Store</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l3"><a class="reference internal" href="../../tutorials/packages/legacy/index.html">Legacy</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../tutorials/packages/legacy/ndarray/index.html">NDArray</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../tutorials/packages/legacy/ndarray/01-ndarray-intro.html">An Intro: Manipulate Data the MXNet Way with NDArray</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../tutorials/packages/legacy/ndarray/02-ndarray-operations.html">NDArray Operations</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../tutorials/packages/legacy/ndarray/03-ndarray-contexts.html">NDArray Contexts</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../tutorials/packages/legacy/ndarray/gotchas_numpy_in_mxnet.html">Gotchas using NumPy in Apache MXNet</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../tutorials/packages/legacy/ndarray/sparse/index.html">Tutorials</a><ul> |
| <li class="toctree-l6"><a class="reference internal" href="../../tutorials/packages/legacy/ndarray/sparse/csr.html">CSRNDArray - NDArray in Compressed Sparse Row Storage Format</a></li> |
| <li class="toctree-l6"><a class="reference internal" href="../../tutorials/packages/legacy/ndarray/sparse/row_sparse.html">RowSparseNDArray - NDArray for Sparse Gradient Updates</a></li> |
| </ul> |
| </li> |
| </ul> |
| </li> |
| </ul> |
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| <li class="toctree-l3"><a class="reference internal" href="../../tutorials/packages/np/index.html">What is NP on MXNet</a><ul> |
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| <li class="toctree-l4"><a class="reference external" href="https://mxnet.apache.org/api/faq/visualize_graph">Visualize networks</a></li> |
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| <li class="toctree-l4"><a class="reference internal" href="../np/routines.linalg.html">Linear algebra (<code class="xref py py-mod docutils literal notranslate"><span class="pre">numpy.linalg</span></code>)</a><ul> |
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| <li class="toctree-l2"><a class="reference internal" href="../npx/index.html">NPX: NumPy Neural Network Extension</a><ul> |
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| |
| <div class="section" id="module-mxnet.test_utils"> |
| <span id="mxnet-test-utils"></span><h1>mxnet.test_utils<a class="headerlink" href="#module-mxnet.test_utils" title="Permalink to this headline">¶</a></h1> |
| <p>Tools for testing.</p> |
| <p><strong>Classes</strong></p> |
| <table class="longtable docutils align-default"> |
| <colgroup> |
| <col style="width: 10%" /> |
| <col style="width: 90%" /> |
| </colgroup> |
| <tbody> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.test_utils.DummyIter" title="mxnet.test_utils.DummyIter"><code class="xref py py-obj docutils literal notranslate"><span class="pre">DummyIter</span></code></a>(real_iter)</p></td> |
| <td><p>A dummy iterator that always returns the same batch of data (the first data batch of the real data iter).</p></td> |
| </tr> |
| </tbody> |
| </table> |
| <p><strong>Functions</strong></p> |
| <table class="longtable docutils align-default"> |
| <colgroup> |
| <col style="width: 10%" /> |
| <col style="width: 90%" /> |
| </colgroup> |
| <tbody> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.test_utils.almost_equal" title="mxnet.test_utils.almost_equal"><code class="xref py py-obj docutils literal notranslate"><span class="pre">almost_equal</span></code></a>(a, b[, rtol, atol, equal_nan, …])</p></td> |
| <td><p>Test if two numpy arrays are almost equal.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.test_utils.assert_almost_equal" title="mxnet.test_utils.assert_almost_equal"><code class="xref py py-obj docutils literal notranslate"><span class="pre">assert_almost_equal</span></code></a>(a, b[, rtol, atol, …])</p></td> |
| <td><p>Test that two numpy arrays are almost equal.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.test_utils.assert_almost_equal_ignore_nan" title="mxnet.test_utils.assert_almost_equal_ignore_nan"><code class="xref py py-obj docutils literal notranslate"><span class="pre">assert_almost_equal_ignore_nan</span></code></a>(a, b[, rtol, …])</p></td> |
| <td><p>Test that two NumPy arrays are almost equal (ignoring NaN in either array).</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.test_utils.assert_almost_equal_with_err" title="mxnet.test_utils.assert_almost_equal_with_err"><code class="xref py py-obj docutils literal notranslate"><span class="pre">assert_almost_equal_with_err</span></code></a>(a, b[, rtol, …])</p></td> |
| <td><p>Test that two numpy arrays are almost equal within given error rate.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.test_utils.assert_exception" title="mxnet.test_utils.assert_exception"><code class="xref py py-obj docutils literal notranslate"><span class="pre">assert_exception</span></code></a>(f, exception_type, *args, …)</p></td> |
| <td><p>Test that function f will throw an exception of type given by <cite>exception_type</cite></p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.test_utils.assign_each" title="mxnet.test_utils.assign_each"><code class="xref py py-obj docutils literal notranslate"><span class="pre">assign_each</span></code></a>(the_input, function)</p></td> |
| <td><p>Return ndarray composed of passing each array value through some function</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.test_utils.assign_each2" title="mxnet.test_utils.assign_each2"><code class="xref py py-obj docutils literal notranslate"><span class="pre">assign_each2</span></code></a>(input1, input2, function)</p></td> |
| <td><p>Return ndarray composed of passing two array values through some function</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.test_utils.check_consistency" title="mxnet.test_utils.check_consistency"><code class="xref py py-obj docutils literal notranslate"><span class="pre">check_consistency</span></code></a>(sym, ctx_list[, scale, …])</p></td> |
| <td><p>Check symbol gives the same output for different running context</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.test_utils.check_gluon_hybridize_consistency" title="mxnet.test_utils.check_gluon_hybridize_consistency"><code class="xref py py-obj docutils literal notranslate"><span class="pre">check_gluon_hybridize_consistency</span></code></a>(…[, …])</p></td> |
| <td><p>Check whether a HybridBlock has consistent output when hybridized or not hybridized</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.test_utils.check_numeric_gradient" title="mxnet.test_utils.check_numeric_gradient"><code class="xref py py-obj docutils literal notranslate"><span class="pre">check_numeric_gradient</span></code></a>(sym, location[, …])</p></td> |
| <td><p>Verify an operation by checking backward pass via finite difference method.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.test_utils.check_speed" title="mxnet.test_utils.check_speed"><code class="xref py py-obj docutils literal notranslate"><span class="pre">check_speed</span></code></a>(sym[, location, ctx, N, …])</p></td> |
| <td><p>Check the running speed of a symbol.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.test_utils.check_symbolic_backward" title="mxnet.test_utils.check_symbolic_backward"><code class="xref py py-obj docutils literal notranslate"><span class="pre">check_symbolic_backward</span></code></a>(sym, location, …)</p></td> |
| <td><p>Compares a symbol’s backward results with the expected ones.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.test_utils.check_symbolic_forward" title="mxnet.test_utils.check_symbolic_forward"><code class="xref py py-obj docutils literal notranslate"><span class="pre">check_symbolic_forward</span></code></a>(sym, location, expected)</p></td> |
| <td><p>Compares a symbol’s forward results with the expected ones.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.test_utils.chi_square_check" title="mxnet.test_utils.chi_square_check"><code class="xref py py-obj docutils literal notranslate"><span class="pre">chi_square_check</span></code></a>(generator, buckets, probs)</p></td> |
| <td><p>Run the chi-square test for the generator.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.test_utils.collapse_sum_like" title="mxnet.test_utils.collapse_sum_like"><code class="xref py py-obj docutils literal notranslate"><span class="pre">collapse_sum_like</span></code></a>(a, shape)</p></td> |
| <td><p>Given <cite>a</cite> as a numpy ndarray, perform reduce_sum on <cite>a</cite> over the axes that do not exist in <cite>shape</cite>.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.test_utils.compare_ndarray_tuple" title="mxnet.test_utils.compare_ndarray_tuple"><code class="xref py py-obj docutils literal notranslate"><span class="pre">compare_ndarray_tuple</span></code></a>(t1, t2[, rtol, atol])</p></td> |
| <td><p>Compare ndarray tuple.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.test_utils.compare_optimizer" title="mxnet.test_utils.compare_optimizer"><code class="xref py py-obj docutils literal notranslate"><span class="pre">compare_optimizer</span></code></a>(opt1, opt2, shapes, dtype)</p></td> |
| <td><p>Compare opt1 and opt2.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.test_utils.compare_optimizer_noise_seeded" title="mxnet.test_utils.compare_optimizer_noise_seeded"><code class="xref py py-obj docutils literal notranslate"><span class="pre">compare_optimizer_noise_seeded</span></code></a>(opt1, opt2, …)</p></td> |
| <td><p>Compare opt1 and opt2 with the added functionality that the seed for generating random noise in the SGLD optimizer update is set so that the same noise is used in opt1 and opt2.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.test_utils.create_sparse_array" title="mxnet.test_utils.create_sparse_array"><code class="xref py py-obj docutils literal notranslate"><span class="pre">create_sparse_array</span></code></a>(shape, stype[, …])</p></td> |
| <td><p>Create a sparse array, For Rsp, assure indices are in a canonical format</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.test_utils.create_sparse_array_zd" title="mxnet.test_utils.create_sparse_array_zd"><code class="xref py py-obj docutils literal notranslate"><span class="pre">create_sparse_array_zd</span></code></a>(shape, stype, density)</p></td> |
| <td><p>Create sparse array, using only rsp_indices to determine density</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.test_utils.default_atols" title="mxnet.test_utils.default_atols"><code class="xref py py-obj docutils literal notranslate"><span class="pre">default_atols</span></code></a>()</p></td> |
| <td><p>Get default absolute tolerances for data comparisons involving each data type.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.test_utils.default_device" title="mxnet.test_utils.default_device"><code class="xref py py-obj docutils literal notranslate"><span class="pre">default_device</span></code></a>()</p></td> |
| <td><p>Get default device for regression test.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.test_utils.default_dtype" title="mxnet.test_utils.default_dtype"><code class="xref py py-obj docutils literal notranslate"><span class="pre">default_dtype</span></code></a>()</p></td> |
| <td><p>Get default data type for regression test.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.test_utils.default_numeric_eps" title="mxnet.test_utils.default_numeric_eps"><code class="xref py py-obj docutils literal notranslate"><span class="pre">default_numeric_eps</span></code></a>()</p></td> |
| <td><p>Get default epsilon for finite difference gradient calculations with data type.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.test_utils.default_rtols" title="mxnet.test_utils.default_rtols"><code class="xref py py-obj docutils literal notranslate"><span class="pre">default_rtols</span></code></a>()</p></td> |
| <td><p>Get default relative tolerances for data comparisons involving each data type.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.test_utils.discard_stderr" title="mxnet.test_utils.discard_stderr"><code class="xref py py-obj docutils literal notranslate"><span class="pre">discard_stderr</span></code></a>()</p></td> |
| <td><p>Discards error output of a routine if invoked as:</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.test_utils.download" title="mxnet.test_utils.download"><code class="xref py py-obj docutils literal notranslate"><span class="pre">download</span></code></a>(url[, fname, dirname, overwrite, …])</p></td> |
| <td><p>Download an given URL</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.test_utils.effective_dtype" title="mxnet.test_utils.effective_dtype"><code class="xref py py-obj docutils literal notranslate"><span class="pre">effective_dtype</span></code></a>(dat)</p></td> |
| <td><p>Return the most appropriate dtype for determining the tolerance used in dat comparisons</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.test_utils.environment" title="mxnet.test_utils.environment"><code class="xref py py-obj docutils literal notranslate"><span class="pre">environment</span></code></a>(*args)</p></td> |
| <td><p>Environment variable setter and unsetter via <cite>with</cite> idiom.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.test_utils.gen_buckets_probs_with_ppf" title="mxnet.test_utils.gen_buckets_probs_with_ppf"><code class="xref py py-obj docutils literal notranslate"><span class="pre">gen_buckets_probs_with_ppf</span></code></a>(ppf, nbuckets)</p></td> |
| <td><p>Generate the buckets and probabilities for chi_square test when the ppf (Quantile function) is specified.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.test_utils.get_atol" title="mxnet.test_utils.get_atol"><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_atol</span></code></a>([atol, dtype])</p></td> |
| <td><p>Get default numerical threshold for regression test.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.test_utils.get_bz2_data" title="mxnet.test_utils.get_bz2_data"><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_bz2_data</span></code></a>(data_dir, data_name, url, …)</p></td> |
| <td><p>Download and extract bz2 data.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.test_utils.get_cifar10" title="mxnet.test_utils.get_cifar10"><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_cifar10</span></code></a>([path])</p></td> |
| <td><p>Downloads CIFAR10 dataset into a directory in the current directory with the name <cite>data</cite>, and then extracts all files into the directory <cite>data/cifar</cite>.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.test_utils.get_etol" title="mxnet.test_utils.get_etol"><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_etol</span></code></a>([etol])</p></td> |
| <td><p>Get default numerical threshold for regression test.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.test_utils.get_im2rec_path" title="mxnet.test_utils.get_im2rec_path"><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_im2rec_path</span></code></a>([home_env])</p></td> |
| <td><p>Get path to the im2rec.py tool</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.test_utils.get_mnist" title="mxnet.test_utils.get_mnist"><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_mnist</span></code></a>([path])</p></td> |
| <td><p>Download and load the MNIST dataset</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.test_utils.get_mnist_iterator" title="mxnet.test_utils.get_mnist_iterator"><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_mnist_iterator</span></code></a>(batch_size, input_shape)</p></td> |
| <td><p>Returns training and validation iterators for MNIST dataset</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.test_utils.get_mnist_ubyte" title="mxnet.test_utils.get_mnist_ubyte"><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_mnist_ubyte</span></code></a>([path])</p></td> |
| <td><p>Downloads ubyte version of the MNIST dataset into a directory in the current directory with the name <cite>data</cite> and extracts all files in the zip archive to this directory.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.test_utils.get_rtol" title="mxnet.test_utils.get_rtol"><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_rtol</span></code></a>([rtol, dtype])</p></td> |
| <td><p>Get default numerical threshold for regression test.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.test_utils.get_tolerance" title="mxnet.test_utils.get_tolerance"><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_tolerance</span></code></a>(dat, tol, default_tol)</p></td> |
| <td><p>Return the tolerance to be used for dat comparisons based on the given tol, datatype and device.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.test_utils.get_tols" title="mxnet.test_utils.get_tols"><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_tols</span></code></a>(x, y, rtol, atol)</p></td> |
| <td><p>For comparing two datasets ‘x’ and ‘y’, what relative and absolute tolerances should be used.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.test_utils.has_tvm_ops" title="mxnet.test_utils.has_tvm_ops"><code class="xref py py-obj docutils literal notranslate"><span class="pre">has_tvm_ops</span></code></a>()</p></td> |
| <td><p>Returns True if MXNet is compiled with TVM generated operators.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.test_utils.is_cd_run" title="mxnet.test_utils.is_cd_run"><code class="xref py py-obj docutils literal notranslate"><span class="pre">is_cd_run</span></code></a>()</p></td> |
| <td><p>Checks if the test is running as part of a Continuous Delivery run</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.test_utils.is_op_runnable" title="mxnet.test_utils.is_op_runnable"><code class="xref py py-obj docutils literal notranslate"><span class="pre">is_op_runnable</span></code></a>()</p></td> |
| <td><p>Returns True for all CPU tests.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.test_utils.list_gpus" title="mxnet.test_utils.list_gpus"><code class="xref py py-obj docutils literal notranslate"><span class="pre">list_gpus</span></code></a>()</p></td> |
| <td><p>Return a list of GPUs</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.test_utils.locationError" title="mxnet.test_utils.locationError"><code class="xref py py-obj docutils literal notranslate"><span class="pre">locationError</span></code></a>(a, b, index, names[, maxError])</p></td> |
| <td><p>Create element mismatch comment</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.test_utils.mean_check" title="mxnet.test_utils.mean_check"><code class="xref py py-obj docutils literal notranslate"><span class="pre">mean_check</span></code></a>(generator, mu, sigma[, nsamples])</p></td> |
| <td><p>Test the generator by matching the mean.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.test_utils.new_matrix_with_real_eigvals_2d" title="mxnet.test_utils.new_matrix_with_real_eigvals_2d"><code class="xref py py-obj docutils literal notranslate"><span class="pre">new_matrix_with_real_eigvals_2d</span></code></a>(n)</p></td> |
| <td><p>Generate a well-conditioned matrix with small real eigenvalues.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.test_utils.new_matrix_with_real_eigvals_nd" title="mxnet.test_utils.new_matrix_with_real_eigvals_nd"><code class="xref py py-obj docutils literal notranslate"><span class="pre">new_matrix_with_real_eigvals_nd</span></code></a>(shape)</p></td> |
| <td><p>Generate well-conditioned matrices with small real eigenvalues.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.test_utils.new_orthonormal_matrix_2d" title="mxnet.test_utils.new_orthonormal_matrix_2d"><code class="xref py py-obj docutils literal notranslate"><span class="pre">new_orthonormal_matrix_2d</span></code></a>(n)</p></td> |
| <td><p>Generate a orthonormal matrix.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.test_utils.new_sym_matrix_with_real_eigvals_2d" title="mxnet.test_utils.new_sym_matrix_with_real_eigvals_2d"><code class="xref py py-obj docutils literal notranslate"><span class="pre">new_sym_matrix_with_real_eigvals_2d</span></code></a>(n)</p></td> |
| <td><p>Generate a sym matrix with real eigenvalues.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.test_utils.new_sym_matrix_with_real_eigvals_nd" title="mxnet.test_utils.new_sym_matrix_with_real_eigvals_nd"><code class="xref py py-obj docutils literal notranslate"><span class="pre">new_sym_matrix_with_real_eigvals_nd</span></code></a>(shape)</p></td> |
| <td><p>Generate sym matrices with real eigenvalues.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.test_utils.np_reduce" title="mxnet.test_utils.np_reduce"><code class="xref py py-obj docutils literal notranslate"><span class="pre">np_reduce</span></code></a>(dat, axis, keepdims, numpy_reduce_func)</p></td> |
| <td><p>Compatible reduce for old version of NumPy.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.test_utils.numeric_grad" title="mxnet.test_utils.numeric_grad"><code class="xref py py-obj docutils literal notranslate"><span class="pre">numeric_grad</span></code></a>(executor, location[, …])</p></td> |
| <td><p>Calculates a numeric gradient via finite difference method.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.test_utils.rand_ndarray" title="mxnet.test_utils.rand_ndarray"><code class="xref py py-obj docutils literal notranslate"><span class="pre">rand_ndarray</span></code></a>(shape[, stype, density, dtype, …])</p></td> |
| <td><p>Generate a random sparse ndarray.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.test_utils.rand_sparse_ndarray" title="mxnet.test_utils.rand_sparse_ndarray"><code class="xref py py-obj docutils literal notranslate"><span class="pre">rand_sparse_ndarray</span></code></a>(shape, stype[, density, …])</p></td> |
| <td><p>Generate a random sparse ndarray.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.test_utils.random_arrays" title="mxnet.test_utils.random_arrays"><code class="xref py py-obj docutils literal notranslate"><span class="pre">random_arrays</span></code></a>(*shapes)</p></td> |
| <td><p>Generate some random numpy arrays.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.test_utils.random_sample" title="mxnet.test_utils.random_sample"><code class="xref py py-obj docutils literal notranslate"><span class="pre">random_sample</span></code></a>(population, k)</p></td> |
| <td><p>Return a k length list of the elements chosen from the population sequence.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.test_utils.random_uniform_arrays" title="mxnet.test_utils.random_uniform_arrays"><code class="xref py py-obj docutils literal notranslate"><span class="pre">random_uniform_arrays</span></code></a>(*shapes, **kwargs)</p></td> |
| <td><p>Generate some random numpy arrays.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.test_utils.same" title="mxnet.test_utils.same"><code class="xref py py-obj docutils literal notranslate"><span class="pre">same</span></code></a>(a, b)</p></td> |
| <td><p>Test if two NumPy arrays are the same.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.test_utils.same_array" title="mxnet.test_utils.same_array"><code class="xref py py-obj docutils literal notranslate"><span class="pre">same_array</span></code></a>(array1, array2)</p></td> |
| <td><p>Check whether two NDArrays sharing the same memory block</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.test_utils.same_symbol_structure" title="mxnet.test_utils.same_symbol_structure"><code class="xref py py-obj docutils literal notranslate"><span class="pre">same_symbol_structure</span></code></a>(sym1, sym2)</p></td> |
| <td><p>Compare two symbols to check if they have the same computation graph structure.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.test_utils.set_default_device" title="mxnet.test_utils.set_default_device"><code class="xref py py-obj docutils literal notranslate"><span class="pre">set_default_device</span></code></a>(device)</p></td> |
| <td><p>Set default device.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.test_utils.shuffle_csr_column_indices" title="mxnet.test_utils.shuffle_csr_column_indices"><code class="xref py py-obj docutils literal notranslate"><span class="pre">shuffle_csr_column_indices</span></code></a>(csr)</p></td> |
| <td><p>Shuffle CSR column indices per row</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.test_utils.var_check" title="mxnet.test_utils.var_check"><code class="xref py py-obj docutils literal notranslate"><span class="pre">var_check</span></code></a>(generator, sigma[, nsamples])</p></td> |
| <td><p>Test the generator by matching the variance.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.test_utils.verify_generator" title="mxnet.test_utils.verify_generator"><code class="xref py py-obj docutils literal notranslate"><span class="pre">verify_generator</span></code></a>(generator, buckets, probs)</p></td> |
| <td><p>Verify whether the generator is correct using chi-square testing.</p></td> |
| </tr> |
| </tbody> |
| </table> |
| <dl class="class"> |
| <dt id="mxnet.test_utils.DummyIter"> |
| <em class="property">class </em><code class="sig-name descname">DummyIter</code><span class="sig-paren">(</span><em class="sig-param">real_iter</em><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/mxnet/test_utils.html#DummyIter"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.test_utils.DummyIter" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">mxnet.io.io.DataIter</span></code></p> |
| <p>A dummy iterator that always returns the same batch of data |
| (the first data batch of the real data iter). This is usually used for speed testing.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><p><strong>real_iter</strong> (<em>mx.io.DataIter</em>) – The real data iterator where the first batch of data comes from</p> |
| </dd> |
| </dl> |
| <p><strong>Methods</strong></p> |
| <table class="longtable docutils align-default"> |
| <colgroup> |
| <col style="width: 10%" /> |
| <col style="width: 90%" /> |
| </colgroup> |
| <tbody> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.test_utils.DummyIter.next" title="mxnet.test_utils.DummyIter.next"><code class="xref py py-obj docutils literal notranslate"><span class="pre">next</span></code></a>()</p></td> |
| <td><p>Get a data batch from iterator.</p></td> |
| </tr> |
| </tbody> |
| </table> |
| <dl class="method"> |
| <dt id="mxnet.test_utils.DummyIter.next"> |
| <code class="sig-name descname">next</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/mxnet/test_utils.html#DummyIter.next"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.test_utils.DummyIter.next" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Get a data batch from iterator. The first data batch of real iter is always returned. |
| StopIteration will never be raised.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Returns</dt> |
| <dd class="field-odd"><p>The data of next batch.</p> |
| </dd> |
| <dt class="field-even">Return type</dt> |
| <dd class="field-even"><p><a class="reference internal" href="../legacy/io/index.html#mxnet.io.DataBatch" title="mxnet.io.DataBatch">DataBatch</a></p> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| </dd></dl> |
| |
| <dl class="function"> |
| <dt id="mxnet.test_utils.almost_equal"> |
| <code class="sig-name descname">almost_equal</code><span class="sig-paren">(</span><em class="sig-param">a</em>, <em class="sig-param">b</em>, <em class="sig-param">rtol=None</em>, <em class="sig-param">atol=None</em>, <em class="sig-param">equal_nan=False</em>, <em class="sig-param">use_broadcast=True</em><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/mxnet/test_utils.html#almost_equal"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.test_utils.almost_equal" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Test if two numpy arrays are almost equal.</p> |
| </dd></dl> |
| |
| <dl class="function"> |
| <dt id="mxnet.test_utils.assert_almost_equal"> |
| <code class="sig-name descname">assert_almost_equal</code><span class="sig-paren">(</span><em class="sig-param">a</em>, <em class="sig-param">b</em>, <em class="sig-param">rtol=None</em>, <em class="sig-param">atol=None</em>, <em class="sig-param">names=('a'</em>, <em class="sig-param">'b')</em>, <em class="sig-param">equal_nan=False</em>, <em class="sig-param">use_broadcast=True</em>, <em class="sig-param">mismatches=(10</em>, <em class="sig-param">10)</em><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/mxnet/test_utils.html#assert_almost_equal"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.test_utils.assert_almost_equal" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Test that two numpy arrays are almost equal. Raise exception message if not.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>a</strong> (<em>np.ndarray</em><em> or </em><em>mx.nd.array</em>) – </p></li> |
| <li><p><strong>b</strong> (<em>np.ndarray</em><em> or </em><em>mx.nd.array</em>) – </p></li> |
| <li><p><strong>rtol</strong> (<em>None</em><em> or </em><em>float</em><em> or </em><em>dict of dtype -> float</em>) – The relative threshold. Default threshold will be used if set to <code class="docutils literal notranslate"><span class="pre">None</span></code>.</p></li> |
| <li><p><strong>atol</strong> (<em>None</em><em> or </em><em>float</em><em> or </em><em>dict of dtype -> float</em>) – The absolute threshold. Default threshold will be used if set to <code class="docutils literal notranslate"><span class="pre">None</span></code>.</p></li> |
| <li><p><strong>names</strong> (<em>tuple of names</em><em>, </em><em>optional</em>) – The names used in error message when an exception occurs</p></li> |
| <li><p><strong>equal_nan</strong> (<em>boolean</em><em>, </em><em>optional</em>) – The flag determining how to treat NAN values in comparison</p></li> |
| <li><p><strong>mismatches</strong> (<em>tuple of mismatches</em>) – Maximum number of mismatches to be printed (mismatches[0]) and determine (mismatches[1])</p></li> |
| </ul> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="function"> |
| <dt id="mxnet.test_utils.assert_almost_equal_ignore_nan"> |
| <code class="sig-name descname">assert_almost_equal_ignore_nan</code><span class="sig-paren">(</span><em class="sig-param">a</em>, <em class="sig-param">b</em>, <em class="sig-param">rtol=None</em>, <em class="sig-param">atol=None</em>, <em class="sig-param">names=('a'</em>, <em class="sig-param">'b')</em><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/mxnet/test_utils.html#assert_almost_equal_ignore_nan"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.test_utils.assert_almost_equal_ignore_nan" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Test that two NumPy arrays are almost equal (ignoring NaN in either array). |
| Combines a relative and absolute measure of approximate eqality. |
| If either the relative or absolute check passes, the arrays are considered equal. |
| Including an absolute check resolves issues with the relative check where all |
| array values are close to zero.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>a</strong> (<em>np.ndarray</em>) – </p></li> |
| <li><p><strong>b</strong> (<em>np.ndarray</em>) – </p></li> |
| <li><p><strong>rtol</strong> (<em>None</em><em> or </em><em>float</em>) – The relative threshold. Default threshold will be used if set to <code class="docutils literal notranslate"><span class="pre">None</span></code>.</p></li> |
| <li><p><strong>atol</strong> (<em>None</em><em> or </em><em>float</em>) – The absolute threshold. Default threshold will be used if set to <code class="docutils literal notranslate"><span class="pre">None</span></code>.</p></li> |
| </ul> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="function"> |
| <dt id="mxnet.test_utils.assert_almost_equal_with_err"> |
| <code class="sig-name descname">assert_almost_equal_with_err</code><span class="sig-paren">(</span><em class="sig-param">a</em>, <em class="sig-param">b</em>, <em class="sig-param">rtol=None</em>, <em class="sig-param">atol=None</em>, <em class="sig-param">etol=None</em>, <em class="sig-param">names=('a'</em>, <em class="sig-param">'b')</em>, <em class="sig-param">equal_nan=False</em>, <em class="sig-param">mismatches=(10</em>, <em class="sig-param">10)</em><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/mxnet/test_utils.html#assert_almost_equal_with_err"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.test_utils.assert_almost_equal_with_err" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Test that two numpy arrays are almost equal within given error rate. Raise exception message if not.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>a</strong> (<em>np.ndarray</em>) – </p></li> |
| <li><p><strong>b</strong> (<em>np.ndarray</em>) – </p></li> |
| <li><p><strong>rtol</strong> (<em>None</em><em> or </em><em>float</em><em> or </em><em>dict of dtype -> float</em>) – The relative threshold. Default threshold will be used if set to <code class="docutils literal notranslate"><span class="pre">None</span></code>.</p></li> |
| <li><p><strong>atol</strong> (<em>None</em><em> or </em><em>float</em><em> or </em><em>dict of dtype -> float</em>) – The absolute threshold. Default threshold will be used if set to <code class="docutils literal notranslate"><span class="pre">None</span></code>.</p></li> |
| <li><p><strong>etol</strong> (<em>None</em><em> or </em><em>float</em>) – The error rate threshold. If etol is float, return true if error_rate < etol even if |
| any error is found.</p></li> |
| <li><p><strong>names</strong> (<em>tuple of names</em><em>, </em><em>optional</em>) – The names used in error message when an exception occurs</p></li> |
| <li><p><strong>equal_nan</strong> (<em>boolean</em><em>, </em><em>optional</em>) – The flag determining how to treat NAN values in comparison</p></li> |
| <li><p><strong>mismatches</strong> (<em>tuple of mismatches</em>) – Maximum number of mismatches to be printed (mismatches[0]) and determine (mismatches[1])</p></li> |
| </ul> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="function"> |
| <dt id="mxnet.test_utils.assert_exception"> |
| <code class="sig-name descname">assert_exception</code><span class="sig-paren">(</span><em class="sig-param">f</em>, <em class="sig-param">exception_type</em>, <em class="sig-param">*args</em>, <em class="sig-param">**kwargs</em><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/mxnet/test_utils.html#assert_exception"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.test_utils.assert_exception" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Test that function f will throw an exception of type given by <cite>exception_type</cite></p> |
| </dd></dl> |
| |
| <dl class="function"> |
| <dt id="mxnet.test_utils.assign_each"> |
| <code class="sig-name descname">assign_each</code><span class="sig-paren">(</span><em class="sig-param">the_input</em>, <em class="sig-param">function</em><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/mxnet/test_utils.html#assign_each"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.test_utils.assign_each" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Return ndarray composed of passing each array value through some function</p> |
| </dd></dl> |
| |
| <dl class="function"> |
| <dt id="mxnet.test_utils.assign_each2"> |
| <code class="sig-name descname">assign_each2</code><span class="sig-paren">(</span><em class="sig-param">input1</em>, <em class="sig-param">input2</em>, <em class="sig-param">function</em><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/mxnet/test_utils.html#assign_each2"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.test_utils.assign_each2" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Return ndarray composed of passing two array values through some function</p> |
| </dd></dl> |
| |
| <dl class="function"> |
| <dt id="mxnet.test_utils.check_consistency"> |
| <code class="sig-name descname">check_consistency</code><span class="sig-paren">(</span><em class="sig-param">sym</em>, <em class="sig-param">ctx_list</em>, <em class="sig-param">scale=1.0</em>, <em class="sig-param">grad_req='write'</em>, <em class="sig-param">arg_params=None</em>, <em class="sig-param">aux_params=None</em>, <em class="sig-param">rtol=None</em>, <em class="sig-param">atol=None</em>, <em class="sig-param">raise_on_err=True</em>, <em class="sig-param">ground_truth=None</em>, <em class="sig-param">equal_nan=False</em>, <em class="sig-param">use_uniform=False</em>, <em class="sig-param">rand_type=<class 'numpy.float64'></em><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/mxnet/test_utils.html#check_consistency"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.test_utils.check_consistency" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Check symbol gives the same output for different running context</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>sym</strong> (<a class="reference internal" href="../legacy/symbol/symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a><em> or </em><em>list of Symbols</em>) – Symbol(s) to run the consistency test.</p></li> |
| <li><p><strong>ctx_list</strong> (<em>list</em>) – Running context. See example for more detail.</p></li> |
| <li><p><strong>scale</strong> (<em>float</em><em>, </em><em>optional</em>) – Standard deviation of the inner normal distribution. Used in initialization.</p></li> |
| <li><p><strong>grad_req</strong> (<em>str</em><em> or </em><em>list of str</em><em> or </em><em>dict of str to str</em>) – Gradient requirement.</p></li> |
| <li><p><strong>arg_params</strong> (<em>dict of input name -> input data</em>) – data to use for non-aux inputs</p></li> |
| <li><p><strong>aux_params</strong> (<em>dict of input name -> input data</em>) – data to use for aux inputs</p></li> |
| <li><p><strong>rtol</strong> (<em>float</em><em> or </em><em>dictionary dtype->float</em><em>, </em><em>optional</em>) – The relative error tolerance.</p></li> |
| <li><p><strong>atol</strong> (<em>float</em><em> or </em><em>dictionary dtype->float</em><em>, </em><em>optional</em>) – The absolute error tolerance.</p></li> |
| <li><p><strong>raise_on_err</strong> (<em>bool</em><em>, </em><em>optional</em><em>, </em><em>defaults to True</em>) – Should an error raise an exception (or just output exception message)</p></li> |
| <li><p><strong>ground_truth</strong> (<em>dict of output name -> data</em><em>, </em><em>optional</em>) – Provided ideal result to be compared against</p></li> |
| <li><p><strong>equal_nan</strong> (<em>bool</em><em>, </em><em>optional</em><em>, </em><em>defaults to False</em>) – Should nans be treated as equal in the comparison</p></li> |
| <li><p><strong>use_uniform</strong> (<em>bool</em>) – Optional, When flag set to true, |
| random input data generated follows uniform distribution, |
| not normal distribution</p></li> |
| <li><p><strong>rand_type</strong> (<em>np.dtype</em>) – casts the randomly generated data to this type |
| Optional, when input data is passed via arg_params, |
| defaults to np.float64 (numpy float default)</p></li> |
| </ul> |
| </dd> |
| </dl> |
| <p class="rubric">Examples</p> |
| <div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="c1"># create the symbol</span> |
| <span class="gp">>>> </span><span class="n">sym</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">sym</span><span class="o">.</span><span class="n">Convolution</span><span class="p">(</span><span class="n">num_filter</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span> <span class="n">kernel</span><span class="o">=</span><span class="p">(</span><span class="mi">3</span><span class="p">,</span><span class="mi">3</span><span class="p">),</span> <span class="n">name</span><span class="o">=</span><span class="s1">'conv'</span><span class="p">)</span> |
| <span class="gp">>>> </span><span class="c1"># initialize the running context</span> |
| <span class="gp">>>> </span><span class="n">ctx_list</span> <span class="o">=</span><span class="p">[{</span><span class="s1">'ctx'</span><span class="p">:</span> <span class="n">mx</span><span class="o">.</span><span class="n">gpu</span><span class="p">(</span><span class="mi">0</span><span class="p">),</span> <span class="s1">'conv_data'</span><span class="p">:</span> <span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">10</span><span class="p">,</span> <span class="mi">10</span><span class="p">),</span> <span class="s1">'type_dict'</span><span class="p">:</span> <span class="p">{</span><span class="s1">'conv_data'</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">float64</span><span class="p">}},</span> <span class="p">{</span><span class="s1">'ctx'</span><span class="p">:</span> <span class="n">mx</span><span class="o">.</span><span class="n">gpu</span><span class="p">(</span><span class="mi">0</span><span class="p">),</span> <span class="s1">'conv_data'</span><span class="p">:</span> <span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">10</span><span class="p">,</span> <span class="mi">10</span><span class="p">),</span> <span class="s1">'type_dict'</span><span class="p">:</span> <span class="p">{</span><span class="s1">'conv_data'</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">float32</span><span class="p">}},</span> <span class="p">{</span><span class="s1">'ctx'</span><span class="p">:</span> <span class="n">mx</span><span class="o">.</span><span class="n">gpu</span><span class="p">(</span><span class="mi">0</span><span class="p">),</span> <span class="s1">'conv_data'</span><span class="p">:</span> <span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">10</span><span class="p">,</span> <span class="mi">10</span><span class="p">),</span> <span class="s1">'type_dict'</span><span class="p">:</span> <span class="p">{</span><span class="s1">'conv_data'</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">float16</span><span class="p">}},</span> <span class="p">{</span><span class="s1">'ctx'</span><span class="p">:</span> <span class="n">mx</span><span class="o">.</span><span class="n">cpu</span><span class="p">(</span><span class="mi">0</span><span class="p">),</span> <span class="s1">'conv_data'</span><span class="p">:</span> <span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">10</span><span class="p">,</span> <span class="mi">10</span><span class="p">),</span> <span class="s1">'type_dict'</span><span class="p">:</span> <span class="p">{</span><span class="s1">'conv_data'</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">float64</span><span class="p">}},</span> <span class="p">{</span><span class="s1">'ctx'</span><span class="p">:</span> <span class="n">mx</span><span class="o">.</span><span class="n">cpu</span><span class="p">(</span><span class="mi">0</span><span class="p">),</span> <span class="s1">'conv_data'</span><span class="p">:</span> <span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">10</span><span class="p">,</span> <span class="mi">10</span><span class="p">),</span> <span class="s1">'type_dict'</span><span class="p">:</span> <span class="p">{</span><span class="s1">'conv_data'</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">float32</span><span class="p">}}]</span> |
| <span class="gp">>>> </span><span class="n">check_consistency</span><span class="p">(</span><span class="n">sym</span><span class="p">,</span> <span class="n">ctx_list</span><span class="p">)</span> |
| <span class="gp">>>> </span><span class="n">sym</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">sym</span><span class="o">.</span><span class="n">Concat</span><span class="p">(</span><span class="n">name</span><span class="o">=</span><span class="s1">'concat'</span><span class="p">,</span> <span class="n">num_args</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span> |
| <span class="gp">>>> </span><span class="n">ctx_list</span> <span class="o">=</span> <span class="p">[{</span><span class="s1">'ctx'</span><span class="p">:</span> <span class="n">mx</span><span class="o">.</span><span class="n">gpu</span><span class="p">(</span><span class="mi">0</span><span class="p">),</span> <span class="s1">'concat_arg1'</span><span class="p">:</span> <span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">10</span><span class="p">),</span> <span class="s1">'concat_arg0'</span><span class="p">:</span> <span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">10</span><span class="p">),</span> <span class="s1">'type_dict'</span><span class="p">:</span> <span class="p">{</span><span class="s1">'concat_arg0'</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">float64</span><span class="p">,</span> <span class="s1">'concat_arg1'</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">float64</span><span class="p">}},</span> <span class="p">{</span><span class="s1">'ctx'</span><span class="p">:</span> <span class="n">mx</span><span class="o">.</span><span class="n">gpu</span><span class="p">(</span><span class="mi">0</span><span class="p">),</span> <span class="s1">'concat_arg1'</span><span class="p">:</span> <span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">10</span><span class="p">),</span> <span class="s1">'concat_arg0'</span><span class="p">:</span> <span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">10</span><span class="p">),</span> <span class="s1">'type_dict'</span><span class="p">:</span> <span class="p">{</span><span class="s1">'concat_arg0'</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">float32</span><span class="p">,</span> <span class="s1">'concat_arg1'</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">float32</span><span class="p">}},</span> <span class="p">{</span><span class="s1">'ctx'</span><span class="p">:</span> <span class="n">mx</span><span class="o">.</span><span class="n">gpu</span><span class="p">(</span><span class="mi">0</span><span class="p">),</span> <span class="s1">'concat_arg1'</span><span class="p">:</span> <span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">10</span><span class="p">),</span> <span class="s1">'concat_arg0'</span><span class="p">:</span> <span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">10</span><span class="p">),</span> <span class="s1">'type_dict'</span><span class="p">:</span> <span class="p">{</span><span class="s1">'concat_arg0'</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">float16</span><span class="p">,</span> <span class="s1">'concat_arg1'</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">float16</span><span class="p">}},</span> <span class="p">{</span><span class="s1">'ctx'</span><span class="p">:</span> <span class="n">mx</span><span class="o">.</span><span class="n">cpu</span><span class="p">(</span><span class="mi">0</span><span class="p">),</span> <span class="s1">'concat_arg1'</span><span class="p">:</span> <span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">10</span><span class="p">),</span> <span class="s1">'concat_arg0'</span><span class="p">:</span> <span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">10</span><span class="p">),</span> <span class="s1">'type_dict'</span><span class="p">:</span> <span class="p">{</span><span class="s1">'concat_arg0'</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">float64</span><span class="p">,</span> <span class="s1">'concat_arg1'</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">float64</span><span class="p">}},</span> <span class="p">{</span><span class="s1">'ctx'</span><span class="p">:</span> <span class="n">mx</span><span class="o">.</span><span class="n">cpu</span><span class="p">(</span><span class="mi">0</span><span class="p">),</span> <span class="s1">'concat_arg1'</span><span class="p">:</span> <span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">10</span><span class="p">),</span> <span class="s1">'concat_arg0'</span><span class="p">:</span> <span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">10</span><span class="p">),</span> <span class="s1">'type_dict'</span><span class="p">:</span> <span class="p">{</span><span class="s1">'concat_arg0'</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">float32</span><span class="p">,</span> <span class="s1">'concat_arg1'</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">float32</span><span class="p">}}]</span> |
| <span class="gp">>>> </span><span class="n">check_consistency</span><span class="p">(</span><span class="n">sym</span><span class="p">,</span> <span class="n">ctx_list</span><span class="p">)</span> |
| </pre></div> |
| </div> |
| </dd></dl> |
| |
| <dl class="function"> |
| <dt id="mxnet.test_utils.check_gluon_hybridize_consistency"> |
| <code class="sig-name descname">check_gluon_hybridize_consistency</code><span class="sig-paren">(</span><em class="sig-param">net_builder</em>, <em class="sig-param">data_l</em>, <em class="sig-param">numpy_func=None</em>, <em class="sig-param">test_grad=True</em>, <em class="sig-param">rtol=0.0001</em>, <em class="sig-param">atol=0.0001</em><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/mxnet/test_utils.html#check_gluon_hybridize_consistency"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.test_utils.check_gluon_hybridize_consistency" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Check whether a HybridBlock has consistent output when hybridized or not hybridized</p> |
| <p>The network should not contain any random number generators.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>net_builder</strong> (<em>function</em>) – The builder of the HybridBlock that we are going to check the consistency. |
| Inside the implementation, we will call net_builder() to construct the hybrid block. |
| Also, the net_builder will need to support specifying the params</p></li> |
| <li><p><strong>data_l</strong> (<em>list of mx.np.ndarray</em>) – List of input ndarrays.</p></li> |
| <li><p><strong>numpy_func</strong> (<em>function</em><em>, </em><em>optional</em>) – The ground truth numpy function that has the same functionality as net_builder(). |
| Default None.</p></li> |
| <li><p><strong>test_grad</strong> (<em>bool</em><em>, </em><em>optional</em>) – Whether to test the consistency of the gradient. Default True.</p></li> |
| <li><p><strong>rtol</strong> (<em>float</em><em>, </em><em>optional</em>) – The relative error tolerance, default 1E-4. Default 1E-4.</p></li> |
| <li><p><strong>atol</strong> (<em>float</em><em>, </em><em>optional</em>) – The absolute error tolerance, default 1E-4. Default 1E-4.</p></li> |
| </ul> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="function"> |
| <dt id="mxnet.test_utils.check_numeric_gradient"> |
| <code class="sig-name descname">check_numeric_gradient</code><span class="sig-paren">(</span><em class="sig-param">sym</em>, <em class="sig-param">location</em>, <em class="sig-param">aux_states=None</em>, <em class="sig-param">numeric_eps=None</em>, <em class="sig-param">rtol=None</em>, <em class="sig-param">atol=None</em>, <em class="sig-param">grad_nodes=None</em>, <em class="sig-param">use_forward_train=True</em>, <em class="sig-param">ctx=None</em>, <em class="sig-param">grad_stype_dict=None</em>, <em class="sig-param">dtype=<class 'numpy.float32'></em><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/mxnet/test_utils.html#check_numeric_gradient"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.test_utils.check_numeric_gradient" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Verify an operation by checking backward pass via finite difference method.</p> |
| <p>Based on Theano’s <cite>theano.gradient.verify_grad</cite> [1]</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>sym</strong> (<a class="reference internal" href="../legacy/symbol/symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – Symbol containing op to test</p></li> |
| <li><p><strong>location</strong> (<em>list</em><em> or </em><em>tuple</em><em> or </em><em>dict</em>) – <p>Argument values used as location to compute gradient</p> |
| <ul> |
| <li><p>if type is list of numpy.ndarray, inner elements should have the same order as mxnet.sym.list_arguments().</p></li> |
| <li><p>if type is dict of str -> numpy.ndarray, maps the name of arguments to the corresponding numpy.ndarray.</p></li> |
| </ul> |
| <p><em>In either case, value of all the arguments must be provided.</em></p> |
| </p></li> |
| <li><p><strong>aux_states</strong> (<em>list</em><em> or </em><em>tuple</em><em> or </em><em>dict</em><em>, </em><em>optional</em>) – The auxiliary states required when generating the executor for the symbol.</p></li> |
| <li><p><strong>numeric_eps</strong> (<em>float</em><em>, </em><em>optional</em>) – Delta for the finite difference method that approximates the gradient.</p></li> |
| <li><p><strong>rtol</strong> (<em>None</em><em> or </em><em>float</em>) – The relative threshold. Default threshold will be used if set to <code class="docutils literal notranslate"><span class="pre">None</span></code>.</p></li> |
| <li><p><strong>atol</strong> (<em>None</em><em> or </em><em>float</em>) – The absolute threshold. Default threshold will be used if set to <code class="docutils literal notranslate"><span class="pre">None</span></code>.</p></li> |
| <li><p><strong>grad_nodes</strong> (<em>None</em><em> or </em><em>list</em><em> or </em><em>tuple</em><em> or </em><em>dict</em><em>, </em><em>optional</em>) – Names of the nodes to check gradient on</p></li> |
| <li><p><strong>use_forward_train</strong> (<em>bool</em>) – Whether to use is_train=True when computing the finite-difference.</p></li> |
| <li><p><strong>ctx</strong> (<em>Context</em><em>, </em><em>optional</em>) – Check the gradient computation on the specified device.</p></li> |
| <li><p><strong>grad_stype_dict</strong> (<em>dict of str->str</em><em>, </em><em>optional</em>) – Storage type dictionary for gradient ndarrays.</p></li> |
| <li><p><strong>dtype</strong> (<em>np.float16</em><em> or </em><em>np.float32</em><em> or </em><em>np.float64</em>) – Datatype for mx.nd.array.</p></li> |
| </ul> |
| </dd> |
| </dl> |
| <p class="rubric">References</p> |
| <p>[1] <a class="reference external" href="https://github.com/Theano/Theano/blob/master/theano/gradient.py">https://github.com/Theano/Theano/blob/master/theano/gradient.py</a></p> |
| </dd></dl> |
| |
| <dl class="function"> |
| <dt id="mxnet.test_utils.check_speed"> |
| <code class="sig-name descname">check_speed</code><span class="sig-paren">(</span><em class="sig-param">sym</em>, <em class="sig-param">location=None</em>, <em class="sig-param">ctx=None</em>, <em class="sig-param">N=20</em>, <em class="sig-param">grad_req=None</em>, <em class="sig-param">typ='whole'</em>, <em class="sig-param">**kwargs</em><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/mxnet/test_utils.html#check_speed"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.test_utils.check_speed" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Check the running speed of a symbol.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>sym</strong> (<a class="reference internal" href="../legacy/symbol/symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – Symbol to run the speed test.</p></li> |
| <li><p><strong>location</strong> (<em>none</em><em> or </em><em>dict of str to np.ndarray</em>) – Location to evaluate the inner executor.</p></li> |
| <li><p><strong>ctx</strong> (<em>Context</em>) – Running context.</p></li> |
| <li><p><strong>N</strong> (<em>int</em><em>, </em><em>optional</em>) – Repeat times.</p></li> |
| <li><p><strong>grad_req</strong> (<em>None</em><em> or </em><em>str</em><em> or </em><em>list of str</em><em> or </em><em>dict of str to str</em><em>, </em><em>optional</em>) – Gradient requirements.</p></li> |
| <li><p><strong>typ</strong> (<em>str</em><em>, </em><em>optional</em>) – <p>“whole” or “forward”</p> |
| <ul> |
| <li><dl class="simple"> |
| <dt>”whole”</dt><dd><p>Test the forward_backward speed.</p> |
| </dd> |
| </dl> |
| </li> |
| <li><dl class="simple"> |
| <dt>”forward”</dt><dd><p>Only test the forward speed.</p> |
| </dd> |
| </dl> |
| </li> |
| </ul> |
| </p></li> |
| </ul> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="function"> |
| <dt id="mxnet.test_utils.check_symbolic_backward"> |
| <code class="sig-name descname">check_symbolic_backward</code><span class="sig-paren">(</span><em class="sig-param">sym</em>, <em class="sig-param">location</em>, <em class="sig-param">out_grads</em>, <em class="sig-param">expected</em>, <em class="sig-param">rtol=None</em>, <em class="sig-param">atol=None</em>, <em class="sig-param">aux_states=None</em>, <em class="sig-param">grad_req='write'</em>, <em class="sig-param">ctx=None</em>, <em class="sig-param">grad_stypes=None</em>, <em class="sig-param">equal_nan=False</em>, <em class="sig-param">dtype=<class 'numpy.float32'></em><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/mxnet/test_utils.html#check_symbolic_backward"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.test_utils.check_symbolic_backward" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Compares a symbol’s backward results with the expected ones. |
| Prints error messages if the backward results are not the same as the expected results.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>sym</strong> (<a class="reference internal" href="../legacy/symbol/symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – output symbol</p></li> |
| <li><p><strong>location</strong> (<em>list of np.ndarray</em><em> or </em><em>dict of str to np.ndarray</em>) – <p>The evaluation point</p> |
| <ul> |
| <li><dl class="simple"> |
| <dt>if type is list of np.ndarray</dt><dd><p>Contains all the NumPy arrays corresponding to <code class="docutils literal notranslate"><span class="pre">mx.sym.list_arguments</span></code>.</p> |
| </dd> |
| </dl> |
| </li> |
| <li><dl class="simple"> |
| <dt>if type is dict of str to np.ndarray</dt><dd><p>Contains the mapping between argument names and their values.</p> |
| </dd> |
| </dl> |
| </li> |
| </ul> |
| </p></li> |
| <li><p><strong>out_grads</strong> (<em>None</em><em> or </em><em>list of np.ndarray</em><em> or </em><em>dict of str to np.ndarray</em>) – <p>NumPys arrays corresponding to sym.outputs for incomming gradient.</p> |
| <ul> |
| <li><dl class="simple"> |
| <dt>if type is list of np.ndarray</dt><dd><p>Contains arrays corresponding to <code class="docutils literal notranslate"><span class="pre">exe.outputs</span></code>.</p> |
| </dd> |
| </dl> |
| </li> |
| <li><dl class="simple"> |
| <dt>if type is dict of str to np.ndarray</dt><dd><p>contains mapping between mxnet.sym.list_output() and Executor.outputs</p> |
| </dd> |
| </dl> |
| </li> |
| </ul> |
| </p></li> |
| <li><p><strong>expected</strong> (<em>list of np.ndarray</em><em> or </em><em>dict of str to np.ndarray</em>) – <p>expected gradient values</p> |
| <ul> |
| <li><dl class="simple"> |
| <dt>if type is list of np.ndarray</dt><dd><p>Contains arrays corresponding to exe.grad_arrays</p> |
| </dd> |
| </dl> |
| </li> |
| <li><dl class="simple"> |
| <dt>if type is dict of str to np.ndarray</dt><dd><p>Contains mapping between <code class="docutils literal notranslate"><span class="pre">sym.list_arguments()</span></code> and exe.outputs.</p> |
| </dd> |
| </dl> |
| </li> |
| </ul> |
| </p></li> |
| <li><p><strong>rtol</strong> (<em>None</em><em> or </em><em>float</em>) – The relative threshold. Default threshold will be used if set to <code class="docutils literal notranslate"><span class="pre">None</span></code>.</p></li> |
| <li><p><strong>atol</strong> (<em>None</em><em> or </em><em>float</em>) – The absolute threshold. Default threshold will be used if set to <code class="docutils literal notranslate"><span class="pre">None</span></code>.</p></li> |
| <li><p><strong>aux_states</strong> (<em>list of np.ndarray</em><em> or </em><em>dict of str to np.ndarray</em>) – </p></li> |
| <li><p><strong>grad_req</strong> (<em>str</em><em> or </em><em>list of str</em><em> or </em><em>dict of str to str</em><em>, </em><em>optional</em>) – Gradient requirements. ‘write’, ‘add’ or ‘null’.</p></li> |
| <li><p><strong>ctx</strong> (<em>Context</em><em>, </em><em>optional</em>) – Running context.</p></li> |
| <li><p><strong>grad_stypes</strong> (<em>dict of str->str</em>) – dictionary of mapping argument name to stype for the gradient</p></li> |
| <li><p><strong>equal_nan</strong> (<em>Boolean</em>) – if True, <cite>nan</cite> is a valid value for checking equivalency (ie <cite>nan</cite> == <cite>nan</cite>)</p></li> |
| <li><p><strong>dtype</strong> (<em>np.float16</em><em> or </em><em>np.float32</em><em> or </em><em>np.float64</em>) – Datatype for mx.nd.array.</p></li> |
| </ul> |
| </dd> |
| </dl> |
| <p class="rubric">Example</p> |
| <div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">lhs</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">symbol</span><span class="o">.</span><span class="n">Variable</span><span class="p">(</span><span class="s1">'lhs'</span><span class="p">)</span> |
| <span class="gp">>>> </span><span class="n">rhs</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">symbol</span><span class="o">.</span><span class="n">Variable</span><span class="p">(</span><span class="s1">'rhs'</span><span class="p">)</span> |
| <span class="gp">>>> </span><span class="n">sym_add</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">symbol</span><span class="o">.</span><span class="n">elemwise_add</span><span class="p">(</span><span class="n">lhs</span><span class="p">,</span> <span class="n">rhs</span><span class="p">)</span> |
| <span class="gp">>>> </span><span class="n">mat1</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">],</span> <span class="p">[</span><span class="mi">3</span><span class="p">,</span> <span class="mi">4</span><span class="p">]])</span> |
| <span class="gp">>>> </span><span class="n">mat2</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([[</span><span class="mi">5</span><span class="p">,</span> <span class="mi">6</span><span class="p">],</span> <span class="p">[</span><span class="mi">7</span><span class="p">,</span> <span class="mi">8</span><span class="p">]])</span> |
| <span class="gp">>>> </span><span class="n">grad1</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="n">shape</span><span class="p">)</span> |
| <span class="gp">>>> </span><span class="n">grad2</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="n">shape</span><span class="p">)</span> |
| <span class="gp">>>> </span><span class="n">exec_add</span> <span class="o">=</span> <span class="n">sym_add</span><span class="o">.</span><span class="n">_bind</span><span class="p">(</span><span class="n">default_device</span><span class="p">(),</span> <span class="n">args</span><span class="o">=</span><span class="p">{</span><span class="s1">'lhs'</span><span class="p">:</span> <span class="n">mat1</span><span class="p">,</span> <span class="s1">'rhs'</span><span class="p">:</span> <span class="n">mat2</span><span class="p">},</span> |
| <span class="gp">... </span><span class="n">args_grad</span><span class="o">=</span><span class="p">{</span><span class="s1">'lhs'</span><span class="p">:</span> <span class="n">grad1</span><span class="p">,</span> <span class="s1">'rhs'</span><span class="p">:</span> <span class="n">grad2</span><span class="p">},</span> <span class="n">grad_req</span><span class="o">=</span><span class="p">{</span><span class="s1">'lhs'</span><span class="p">:</span> <span class="s1">'write'</span><span class="p">,</span> <span class="s1">'rhs'</span><span class="p">:</span> <span class="s1">'write'</span><span class="p">})</span> |
| <span class="gp">>>> </span><span class="n">exec_add</span><span class="o">.</span><span class="n">forward</span><span class="p">(</span><span class="n">is_train</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span> |
| <span class="gp">>>> </span><span class="n">ograd</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">ones</span><span class="p">(</span><span class="n">shape</span><span class="p">)</span> |
| <span class="gp">>>> </span><span class="n">grad_expected</span> <span class="o">=</span> <span class="n">ograd</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span><span class="o">.</span><span class="n">asnumpy</span><span class="p">()</span> |
| <span class="gp">>>> </span><span class="n">check_symbolic_backward</span><span class="p">(</span><span class="n">sym_add</span><span class="p">,</span> <span class="p">[</span><span class="n">mat1</span><span class="p">,</span> <span class="n">mat2</span><span class="p">],</span> <span class="p">[</span><span class="n">ograd</span><span class="p">],</span> <span class="p">[</span><span class="n">grad_expected</span><span class="p">,</span> <span class="n">grad_expected</span><span class="p">])</span> |
| </pre></div> |
| </div> |
| </dd></dl> |
| |
| <dl class="function"> |
| <dt id="mxnet.test_utils.check_symbolic_forward"> |
| <code class="sig-name descname">check_symbolic_forward</code><span class="sig-paren">(</span><em class="sig-param">sym</em>, <em class="sig-param">location</em>, <em class="sig-param">expected</em>, <em class="sig-param">rtol=None</em>, <em class="sig-param">atol=None</em>, <em class="sig-param">aux_states=None</em>, <em class="sig-param">ctx=None</em>, <em class="sig-param">equal_nan=False</em>, <em class="sig-param">dtype=<class 'numpy.float32'></em><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/mxnet/test_utils.html#check_symbolic_forward"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.test_utils.check_symbolic_forward" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Compares a symbol’s forward results with the expected ones. |
| Prints error messages if the forward results are not the same as the expected ones.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>sym</strong> (<a class="reference internal" href="../legacy/symbol/symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – output symbol</p></li> |
| <li><p><strong>location</strong> (<em>list of np.ndarray</em><em> or </em><em>dict of str to np.ndarray</em>) – <p>The evaluation point</p> |
| <ul> |
| <li><dl class="simple"> |
| <dt>if type is list of np.ndarray</dt><dd><p>Contains all the numpy arrays corresponding to <cite>sym.list_arguments()</cite>.</p> |
| </dd> |
| </dl> |
| </li> |
| <li><dl class="simple"> |
| <dt>if type is dict of str to np.ndarray</dt><dd><p>Contains the mapping between argument names and their values.</p> |
| </dd> |
| </dl> |
| </li> |
| </ul> |
| </p></li> |
| <li><p><strong>expected</strong> (<em>list of np.ndarray</em><em> or </em><em>dict of str to np.ndarray</em>) – <p>The expected output value</p> |
| <ul> |
| <li><dl class="simple"> |
| <dt>if type is list of np.ndarray</dt><dd><p>Contains arrays corresponding to exe.outputs.</p> |
| </dd> |
| </dl> |
| </li> |
| <li><dl class="simple"> |
| <dt>if type is dict of str to np.ndarray</dt><dd><p>Contains mapping between sym.list_output() and exe.outputs.</p> |
| </dd> |
| </dl> |
| </li> |
| </ul> |
| </p></li> |
| <li><p><strong>rtol</strong> (<em>None</em><em> or </em><em>float</em>) – The relative threshold. Default threshold will be used if set to <code class="docutils literal notranslate"><span class="pre">None</span></code>.</p></li> |
| <li><p><strong>atol</strong> (<em>None</em><em> or </em><em>float</em>) – The absolute threshold. Default threshold will be used if set to <code class="docutils literal notranslate"><span class="pre">None</span></code>.</p></li> |
| <li><p><strong>aux_states</strong> (<em>list of np.ndarray of dict</em><em>, </em><em>optional</em>) – <ul> |
| <li><dl class="simple"> |
| <dt>if type is list of np.ndarray</dt><dd><p>Contains all the NumPy arrays corresponding to sym.list_auxiliary_states</p> |
| </dd> |
| </dl> |
| </li> |
| <li><dl class="simple"> |
| <dt>if type is dict of str to np.ndarray</dt><dd><p>Contains the mapping between names of auxiliary states and their values.</p> |
| </dd> |
| </dl> |
| </li> |
| </ul> |
| </p></li> |
| <li><p><strong>device</strong> (<a class="reference internal" href="../device/index.html#mxnet.device.Device" title="mxnet.device.Device"><em>Device</em></a><em>, </em><em>optional</em>) – running context</p></li> |
| <li><p><strong>dtype</strong> (<em>"asnumpy"</em><em> or </em><em>np.float16</em><em> or </em><em>np.float32</em><em> or </em><em>np.float64</em>) – If dtype is “asnumpy” then the mx.nd.array created will have the same |
| type as th numpy array from which it is copied. |
| Otherwise, dtype is the explicit datatype for all mx.nd.array objects |
| created in this function.</p></li> |
| <li><p><strong>equal_nan</strong> (<em>Boolean</em>) – if True, <cite>nan</cite> is a valid value for checking equivalency (ie <cite>nan</cite> == <cite>nan</cite>)</p></li> |
| </ul> |
| </dd> |
| </dl> |
| <p class="rubric">Example</p> |
| <div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">shape</span> <span class="o">=</span> <span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">)</span> |
| <span class="gp">>>> </span><span class="n">lhs</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">symbol</span><span class="o">.</span><span class="n">Variable</span><span class="p">(</span><span class="s1">'lhs'</span><span class="p">)</span> |
| <span class="gp">>>> </span><span class="n">rhs</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">symbol</span><span class="o">.</span><span class="n">Variable</span><span class="p">(</span><span class="s1">'rhs'</span><span class="p">)</span> |
| <span class="gp">>>> </span><span class="n">sym_dot</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">symbol</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="n">lhs</span><span class="p">,</span> <span class="n">rhs</span><span class="p">)</span> |
| <span class="gp">>>> </span><span class="n">mat1</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">],</span> <span class="p">[</span><span class="mi">3</span><span class="p">,</span> <span class="mi">4</span><span class="p">]])</span> |
| <span class="gp">>>> </span><span class="n">mat2</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([[</span><span class="mi">5</span><span class="p">,</span> <span class="mi">6</span><span class="p">],</span> <span class="p">[</span><span class="mi">7</span><span class="p">,</span> <span class="mi">8</span><span class="p">]])</span> |
| <span class="gp">>>> </span><span class="n">ret_expected</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([[</span><span class="mi">19</span><span class="p">,</span> <span class="mi">22</span><span class="p">],</span> <span class="p">[</span><span class="mi">43</span><span class="p">,</span> <span class="mi">50</span><span class="p">]])</span> |
| <span class="gp">>>> </span><span class="n">check_symbolic_forward</span><span class="p">(</span><span class="n">sym_dot</span><span class="p">,</span> <span class="p">[</span><span class="n">mat1</span><span class="p">,</span> <span class="n">mat2</span><span class="p">],</span> <span class="p">[</span><span class="n">ret_expected</span><span class="p">])</span> |
| </pre></div> |
| </div> |
| </dd></dl> |
| |
| <dl class="function"> |
| <dt id="mxnet.test_utils.chi_square_check"> |
| <code class="sig-name descname">chi_square_check</code><span class="sig-paren">(</span><em class="sig-param">generator</em>, <em class="sig-param">buckets</em>, <em class="sig-param">probs</em>, <em class="sig-param">nsamples=1000000</em><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/mxnet/test_utils.html#chi_square_check"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.test_utils.chi_square_check" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Run the chi-square test for the generator. The generator can be both continuous and discrete.</p> |
| <p>If the generator is continuous, the buckets should contain tuples of (range_min, range_max) and the probs should be the corresponding ideal probability within the specific ranges. Otherwise, the buckets should contain all the possible values generated over the discrete distribution and the probs should be groud-truth probability.</p> |
| <p>Usually the user is required to specify the probs parameter.</p> |
| <p>After obtaining the p value, we could further use the standard p > 0.05 (alpha) threshold to get the final result.</p> |
| <p>Examples:</p> |
| <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">buckets</span><span class="p">,</span> <span class="n">probs</span> <span class="o">=</span> <span class="n">gen_buckets_probs_with_ppf</span><span class="p">(</span><span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="n">ss</span><span class="o">.</span><span class="n">norm</span><span class="o">.</span><span class="n">ppf</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">),</span> <span class="mi">5</span><span class="p">)</span> |
| <span class="n">generator</span> <span class="o">=</span> <span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">normal</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mf">1.0</span><span class="p">,</span> <span class="n">size</span><span class="o">=</span><span class="n">x</span><span class="p">)</span> |
| <span class="n">p</span> <span class="o">=</span> <span class="n">chi_square_check</span><span class="p">(</span><span class="n">generator</span><span class="o">=</span><span class="n">generator</span><span class="p">,</span> <span class="n">buckets</span><span class="o">=</span><span class="n">buckets</span><span class="p">,</span> <span class="n">probs</span><span class="o">=</span><span class="n">probs</span><span class="p">)</span> |
| <span class="k">assert</span><span class="p">(</span><span class="n">p</span> <span class="o">></span> <span class="mf">0.05</span><span class="p">)</span> |
| </pre></div> |
| </div> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>generator</strong> (<em>function</em>) – A function that is assumed to generate i.i.d samples from a specific distribution. |
| generator(N) should generate N random samples.</p></li> |
| <li><p><strong>buckets</strong> (<em>list of tuple</em><em> or </em><em>list of number</em>) – The buckets to run the chi-square the test. Make sure that the buckets cover |
| the whole range of the distribution. Also, the buckets must be in ascending order and have |
| no intersection</p></li> |
| <li><p><strong>probs</strong> (<em>list</em><em> or </em><em>tuple</em>) – The ground-truth probability of the random value fall in a specific bucket.</p></li> |
| <li><p><strong>nsamples</strong> (<em>int</em>) – The number of samples to generate for the testing</p></li> |
| </ul> |
| </dd> |
| <dt class="field-even">Returns</dt> |
| <dd class="field-even"><p><ul class="simple"> |
| <li><p><strong>p</strong> (<em>float</em>) – p value that the generator has the expected distribution. |
| A higher value indicates a larger confidence</p></li> |
| <li><p><strong>obs_freq</strong> (<em>list</em>) – Observed frequency of buckets</p></li> |
| <li><p><strong>expected_freq</strong> (<em>list</em>) – The expected (ground-truth) frequency of the buckets</p></li> |
| </ul> |
| </p> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="function"> |
| <dt id="mxnet.test_utils.collapse_sum_like"> |
| <code class="sig-name descname">collapse_sum_like</code><span class="sig-paren">(</span><em class="sig-param">a</em>, <em class="sig-param">shape</em><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/mxnet/test_utils.html#collapse_sum_like"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.test_utils.collapse_sum_like" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Given <cite>a</cite> as a numpy ndarray, perform reduce_sum on <cite>a</cite> over the axes that do not |
| exist in <cite>shape</cite>. Note that an ndarray with <cite>shape</cite> must be broadcastable to <cite>a</cite>.</p> |
| </dd></dl> |
| |
| <dl class="function"> |
| <dt id="mxnet.test_utils.compare_ndarray_tuple"> |
| <code class="sig-name descname">compare_ndarray_tuple</code><span class="sig-paren">(</span><em class="sig-param">t1</em>, <em class="sig-param">t2</em>, <em class="sig-param">rtol=None</em>, <em class="sig-param">atol=None</em><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/mxnet/test_utils.html#compare_ndarray_tuple"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.test_utils.compare_ndarray_tuple" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Compare ndarray tuple.</p> |
| </dd></dl> |
| |
| <dl class="function"> |
| <dt id="mxnet.test_utils.compare_optimizer"> |
| <code class="sig-name descname">compare_optimizer</code><span class="sig-paren">(</span><em class="sig-param">opt1</em>, <em class="sig-param">opt2</em>, <em class="sig-param">shapes</em>, <em class="sig-param">dtype</em>, <em class="sig-param">w_stype='default'</em>, <em class="sig-param">g_stype='default'</em>, <em class="sig-param">rtol=0.0001</em>, <em class="sig-param">atol=1e-05</em>, <em class="sig-param">compare_states=True</em><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/mxnet/test_utils.html#compare_optimizer"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.test_utils.compare_optimizer" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Compare opt1 and opt2.</p> |
| </dd></dl> |
| |
| <dl class="function"> |
| <dt id="mxnet.test_utils.compare_optimizer_noise_seeded"> |
| <code class="sig-name descname">compare_optimizer_noise_seeded</code><span class="sig-paren">(</span><em class="sig-param">opt1</em>, <em class="sig-param">opt2</em>, <em class="sig-param">shapes</em>, <em class="sig-param">dtype</em>, <em class="sig-param">noise_seed</em>, <em class="sig-param">w_stype='default'</em>, <em class="sig-param">g_stype='default'</em>, <em class="sig-param">rtol=0.0001</em>, <em class="sig-param">atol=1e-05</em>, <em class="sig-param">compare_states=True</em><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/mxnet/test_utils.html#compare_optimizer_noise_seeded"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.test_utils.compare_optimizer_noise_seeded" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Compare opt1 and opt2 with the added functionality that the seed for generating random noise |
| in the SGLD optimizer update is set so that the same noise is used in opt1 and opt2.</p> |
| </dd></dl> |
| |
| <dl class="function"> |
| <dt id="mxnet.test_utils.create_sparse_array"> |
| <code class="sig-name descname">create_sparse_array</code><span class="sig-paren">(</span><em class="sig-param">shape</em>, <em class="sig-param">stype</em>, <em class="sig-param">data_init=None</em>, <em class="sig-param">rsp_indices=None</em>, <em class="sig-param">dtype=None</em>, <em class="sig-param">modifier_func=None</em>, <em class="sig-param">density=0.5</em>, <em class="sig-param">shuffle_csr_indices=False</em><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/mxnet/test_utils.html#create_sparse_array"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.test_utils.create_sparse_array" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Create a sparse array, For Rsp, assure indices are in a canonical format</p> |
| </dd></dl> |
| |
| <dl class="function"> |
| <dt id="mxnet.test_utils.create_sparse_array_zd"> |
| <code class="sig-name descname">create_sparse_array_zd</code><span class="sig-paren">(</span><em class="sig-param">shape</em>, <em class="sig-param">stype</em>, <em class="sig-param">density</em>, <em class="sig-param">data_init=None</em>, <em class="sig-param">rsp_indices=None</em>, <em class="sig-param">dtype=None</em>, <em class="sig-param">modifier_func=None</em>, <em class="sig-param">shuffle_csr_indices=False</em><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/mxnet/test_utils.html#create_sparse_array_zd"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.test_utils.create_sparse_array_zd" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Create sparse array, using only rsp_indices to determine density</p> |
| </dd></dl> |
| |
| <dl class="function"> |
| <dt id="mxnet.test_utils.default_atols"> |
| <code class="sig-name descname">default_atols</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/mxnet/test_utils.html#default_atols"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.test_utils.default_atols" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Get default absolute tolerances for data comparisons involving each data type.</p> |
| </dd></dl> |
| |
| <dl class="function"> |
| <dt id="mxnet.test_utils.default_device"> |
| <code class="sig-name descname">default_device</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/mxnet/test_utils.html#default_device"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.test_utils.default_device" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Get default device for regression test.</p> |
| </dd></dl> |
| |
| <dl class="function"> |
| <dt id="mxnet.test_utils.default_dtype"> |
| <code class="sig-name descname">default_dtype</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/mxnet/test_utils.html#default_dtype"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.test_utils.default_dtype" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Get default data type for regression test.</p> |
| </dd></dl> |
| |
| <dl class="function"> |
| <dt id="mxnet.test_utils.default_numeric_eps"> |
| <code class="sig-name descname">default_numeric_eps</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/mxnet/test_utils.html#default_numeric_eps"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.test_utils.default_numeric_eps" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Get default epsilon for finite difference gradient calculations with data type.</p> |
| </dd></dl> |
| |
| <dl class="function"> |
| <dt id="mxnet.test_utils.default_rtols"> |
| <code class="sig-name descname">default_rtols</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/mxnet/test_utils.html#default_rtols"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.test_utils.default_rtols" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Get default relative tolerances for data comparisons involving each data type.</p> |
| </dd></dl> |
| |
| <dl class="function"> |
| <dt id="mxnet.test_utils.discard_stderr"> |
| <code class="sig-name descname">discard_stderr</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/mxnet/test_utils.html#discard_stderr"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.test_utils.discard_stderr" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Discards error output of a routine if invoked as:</p> |
| <dl class="simple"> |
| <dt>with discard_stderr():</dt><dd><p>…</p> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="function"> |
| <dt id="mxnet.test_utils.download"> |
| <code class="sig-name descname">download</code><span class="sig-paren">(</span><em class="sig-param">url</em>, <em class="sig-param">fname=None</em>, <em class="sig-param">dirname=None</em>, <em class="sig-param">overwrite=False</em>, <em class="sig-param">retries=5</em><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/mxnet/test_utils.html#download"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.test_utils.download" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Download an given URL</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>url</strong> (<em>str</em>) – URL to download</p></li> |
| <li><p><strong>fname</strong> (<em>str</em><em>, </em><em>optional</em>) – filename of the downloaded file. If None, then will guess a filename |
| from url.</p></li> |
| <li><p><strong>dirname</strong> (<em>str</em><em>, </em><em>optional</em>) – output directory name. If None, then guess from fname or use the current |
| directory</p></li> |
| <li><p><strong>overwrite</strong> (<em>bool</em><em>, </em><em>optional</em>) – Default is false, which means skipping download if the local file |
| exists. If true, then download the url to overwrite the local file if |
| exists.</p></li> |
| <li><p><strong>retries</strong> (<em>integer</em><em>, </em><em>default 5</em>) – The number of times to attempt the download in case of failure or non 200 return codes</p></li> |
| </ul> |
| </dd> |
| <dt class="field-even">Returns</dt> |
| <dd class="field-even"><p>The filename of the downloaded file</p> |
| </dd> |
| <dt class="field-odd">Return type</dt> |
| <dd class="field-odd"><p>str</p> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="function"> |
| <dt id="mxnet.test_utils.effective_dtype"> |
| <code class="sig-name descname">effective_dtype</code><span class="sig-paren">(</span><em class="sig-param">dat</em><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/mxnet/test_utils.html#effective_dtype"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.test_utils.effective_dtype" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Return the most appropriate dtype for determining the tolerance used in dat comparisons |
| :param dat: |
| :type dat: np.ndarray or mx.nd.array or mx.np.ndarray</p> |
| </dd></dl> |
| |
| <dl class="function"> |
| <dt id="mxnet.test_utils.environment"> |
| <code class="sig-name descname">environment</code><span class="sig-paren">(</span><em class="sig-param">*args</em><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/mxnet/test_utils.html#environment"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.test_utils.environment" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Environment variable setter and unsetter via <cite>with</cite> idiom.</p> |
| <p>Takes a specification of env var names and desired values and adds those |
| settings to the environment in advance of running the body of the <cite>with</cite> |
| statement. The original environment state is restored afterwards, even |
| if exceptions are raised in the <cite>with</cite> body.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><p><strong>args</strong> – <dl class="simple"> |
| <dt>if 2 args are passed:</dt><dd><p>name, desired_value strings of the single env var to update, or</p> |
| </dd> |
| <dt>if 1 arg is passed:</dt><dd><p>a dict of name:desired_value for env var’s to update</p> |
| </dd> |
| </dl> |
| </p> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="function"> |
| <dt id="mxnet.test_utils.gen_buckets_probs_with_ppf"> |
| <code class="sig-name descname">gen_buckets_probs_with_ppf</code><span class="sig-paren">(</span><em class="sig-param">ppf</em>, <em class="sig-param">nbuckets</em><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/mxnet/test_utils.html#gen_buckets_probs_with_ppf"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.test_utils.gen_buckets_probs_with_ppf" title="Permalink to this definition">¶</a></dt> |
| <dd><dl class="simple"> |
| <dt>Generate the buckets and probabilities for chi_square test when the ppf (Quantile function)</dt><dd><p>is specified.</p> |
| </dd> |
| </dl> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>ppf</strong> (<em>function</em>) – The Quantile function that takes a probability and maps it back to a value. |
| It’s the inverse of the cdf function</p></li> |
| <li><p><strong>nbuckets</strong> (<em>int</em>) – size of the buckets</p></li> |
| </ul> |
| </dd> |
| <dt class="field-even">Returns</dt> |
| <dd class="field-even"><p><ul class="simple"> |
| <li><p><strong>buckets</strong> (<em>list of tuple</em>) – The generated buckets</p></li> |
| <li><p><strong>probs</strong> (<em>list</em>) – The generate probabilities</p></li> |
| </ul> |
| </p> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="function"> |
| <dt id="mxnet.test_utils.get_atol"> |
| <code class="sig-name descname">get_atol</code><span class="sig-paren">(</span><em class="sig-param">atol=None</em>, <em class="sig-param">dtype=dtype('float64')</em><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/mxnet/test_utils.html#get_atol"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.test_utils.get_atol" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Get default numerical threshold for regression test.</p> |
| </dd></dl> |
| |
| <dl class="function"> |
| <dt id="mxnet.test_utils.get_bz2_data"> |
| <code class="sig-name descname">get_bz2_data</code><span class="sig-paren">(</span><em class="sig-param">data_dir</em>, <em class="sig-param">data_name</em>, <em class="sig-param">url</em>, <em class="sig-param">data_origin_name</em><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/mxnet/test_utils.html#get_bz2_data"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.test_utils.get_bz2_data" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Download and extract bz2 data.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>data_dir</strong> (<em>str</em>) – Absolute or relative path of the directory name to store bz2 files</p></li> |
| <li><p><strong>data_name</strong> (<em>str</em>) – Name of the output file in which bz2 contents will be extracted</p></li> |
| <li><p><strong>url</strong> (<em>str</em>) – URL to download data from</p></li> |
| <li><p><strong>data_origin_name</strong> (<em>str</em>) – Name of the downloaded b2 file</p></li> |
| </ul> |
| </dd> |
| </dl> |
| <p class="rubric">Examples</p> |
| <div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">get_bz2_data</span><span class="p">(</span><span class="s2">"data_dir"</span><span class="p">,</span> <span class="s2">"kdda.t"</span><span class="p">,</span> |
| <span class="go"> "https://www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasets/binary/kdda.t.bz2",</span> |
| <span class="go"> "kdda.t.bz2")</span> |
| </pre></div> |
| </div> |
| </dd></dl> |
| |
| <dl class="function"> |
| <dt id="mxnet.test_utils.get_cifar10"> |
| <code class="sig-name descname">get_cifar10</code><span class="sig-paren">(</span><em class="sig-param">path='data'</em><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/mxnet/test_utils.html#get_cifar10"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.test_utils.get_cifar10" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Downloads CIFAR10 dataset into a directory in the current directory with the name <cite>data</cite>, |
| and then extracts all files into the directory <cite>data/cifar</cite>.</p> |
| </dd></dl> |
| |
| <dl class="function"> |
| <dt id="mxnet.test_utils.get_etol"> |
| <code class="sig-name descname">get_etol</code><span class="sig-paren">(</span><em class="sig-param">etol=None</em><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/mxnet/test_utils.html#get_etol"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.test_utils.get_etol" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Get default numerical threshold for regression test.</p> |
| </dd></dl> |
| |
| <dl class="function"> |
| <dt id="mxnet.test_utils.get_im2rec_path"> |
| <code class="sig-name descname">get_im2rec_path</code><span class="sig-paren">(</span><em class="sig-param">home_env='MXNET_HOME'</em><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/mxnet/test_utils.html#get_im2rec_path"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.test_utils.get_im2rec_path" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Get path to the im2rec.py tool</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><p><strong>home_env</strong> (<em>str</em>) – Env variable that holds the path to the MXNET folder</p> |
| </dd> |
| <dt class="field-even">Returns</dt> |
| <dd class="field-even"><p>The path to im2rec.py</p> |
| </dd> |
| <dt class="field-odd">Return type</dt> |
| <dd class="field-odd"><p>str</p> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="function"> |
| <dt id="mxnet.test_utils.get_mnist"> |
| <code class="sig-name descname">get_mnist</code><span class="sig-paren">(</span><em class="sig-param">path='data'</em><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/mxnet/test_utils.html#get_mnist"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.test_utils.get_mnist" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Download and load the MNIST dataset</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><p><strong>path</strong> (<em>str</em>) – Path in which to save the files.</p> |
| </dd> |
| <dt class="field-even">Returns</dt> |
| <dd class="field-even"><p>A dict containing the data.</p> |
| </dd> |
| <dt class="field-odd">Return type</dt> |
| <dd class="field-odd"><p>dict</p> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="function"> |
| <dt id="mxnet.test_utils.get_mnist_iterator"> |
| <code class="sig-name descname">get_mnist_iterator</code><span class="sig-paren">(</span><em class="sig-param">batch_size</em>, <em class="sig-param">input_shape</em>, <em class="sig-param">num_parts=1</em>, <em class="sig-param">part_index=0</em>, <em class="sig-param">path='data'</em><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/mxnet/test_utils.html#get_mnist_iterator"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.test_utils.get_mnist_iterator" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Returns training and validation iterators for MNIST dataset</p> |
| </dd></dl> |
| |
| <dl class="function"> |
| <dt id="mxnet.test_utils.get_mnist_ubyte"> |
| <code class="sig-name descname">get_mnist_ubyte</code><span class="sig-paren">(</span><em class="sig-param">path='data'</em><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/mxnet/test_utils.html#get_mnist_ubyte"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.test_utils.get_mnist_ubyte" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Downloads ubyte version of the MNIST dataset into a directory in the current directory |
| with the name <cite>data</cite> and extracts all files in the zip archive to this directory.</p> |
| </dd></dl> |
| |
| <dl class="function"> |
| <dt id="mxnet.test_utils.get_rtol"> |
| <code class="sig-name descname">get_rtol</code><span class="sig-paren">(</span><em class="sig-param">rtol=None</em>, <em class="sig-param">dtype=dtype('float64')</em><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/mxnet/test_utils.html#get_rtol"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.test_utils.get_rtol" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Get default numerical threshold for regression test.</p> |
| </dd></dl> |
| |
| <dl class="function"> |
| <dt id="mxnet.test_utils.get_tolerance"> |
| <code class="sig-name descname">get_tolerance</code><span class="sig-paren">(</span><em class="sig-param">dat</em>, <em class="sig-param">tol</em>, <em class="sig-param">default_tol</em><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/mxnet/test_utils.html#get_tolerance"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.test_utils.get_tolerance" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Return the tolerance to be used for dat comparisons based on the given tol, datatype and device. |
| :param dat: |
| :type dat: np.ndarray or mx.nd.array or mx.np.ndarray |
| :param tol: |
| :type tol: float, or a dict of dtype->float |
| :param default_tol: |
| :type default_tol: default dict of dtype->float for all types</p> |
| </dd></dl> |
| |
| <dl class="function"> |
| <dt id="mxnet.test_utils.get_tols"> |
| <code class="sig-name descname">get_tols</code><span class="sig-paren">(</span><em class="sig-param">x</em>, <em class="sig-param">y</em>, <em class="sig-param">rtol</em>, <em class="sig-param">atol</em><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/mxnet/test_utils.html#get_tols"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.test_utils.get_tols" title="Permalink to this definition">¶</a></dt> |
| <dd><p>For comparing two datasets ‘x’ and ‘y’, what relative and absolute tolerances should be used.</p> |
| </dd></dl> |
| |
| <dl class="function"> |
| <dt id="mxnet.test_utils.has_tvm_ops"> |
| <code class="sig-name descname">has_tvm_ops</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/mxnet/test_utils.html#has_tvm_ops"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.test_utils.has_tvm_ops" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Returns True if MXNet is compiled with TVM generated operators. If current ctx |
| is GPU, it only returns True for CUDA compute capability > 52 where FP16 is supported.</p> |
| </dd></dl> |
| |
| <dl class="function"> |
| <dt id="mxnet.test_utils.is_cd_run"> |
| <code class="sig-name descname">is_cd_run</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/mxnet/test_utils.html#is_cd_run"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.test_utils.is_cd_run" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Checks if the test is running as part of a Continuous Delivery run</p> |
| </dd></dl> |
| |
| <dl class="function"> |
| <dt id="mxnet.test_utils.is_op_runnable"> |
| <code class="sig-name descname">is_op_runnable</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/mxnet/test_utils.html#is_op_runnable"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.test_utils.is_op_runnable" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Returns True for all CPU tests. Returns True for GPU tests that are either of the following. |
| 1. Built with USE_TVM_OP=0. |
| 2. Built with USE_TVM_OP=1, but with compute capability >= 53.</p> |
| </dd></dl> |
| |
| <dl class="function"> |
| <dt id="mxnet.test_utils.list_gpus"> |
| <code class="sig-name descname">list_gpus</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/mxnet/test_utils.html#list_gpus"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.test_utils.list_gpus" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Return a list of GPUs</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Returns</dt> |
| <dd class="field-odd"><p>If there are n GPUs, then return a list [0,1,…,n-1]. Otherwise returns |
| [].</p> |
| </dd> |
| <dt class="field-even">Return type</dt> |
| <dd class="field-even"><p>list of int</p> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="function"> |
| <dt id="mxnet.test_utils.locationError"> |
| <code class="sig-name descname">locationError</code><span class="sig-paren">(</span><em class="sig-param">a</em>, <em class="sig-param">b</em>, <em class="sig-param">index</em>, <em class="sig-param">names</em>, <em class="sig-param">maxError=False</em><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/mxnet/test_utils.html#locationError"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.test_utils.locationError" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Create element mismatch comment</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>b</strong> (<em>a</em><em>,</em>) – </p></li> |
| <li><p><strong>index</strong> (<em>tuple of coordinate arrays</em>) – Location of violation</p></li> |
| <li><p><strong>names</strong> (<em>tuple of names</em>) – The names of compared arrays.</p></li> |
| <li><p><strong>maxError</strong> (<em>boolean</em><em>, </em><em>optional</em>) – Flag indicating that maximum error is reporting.</p></li> |
| </ul> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="function"> |
| <dt id="mxnet.test_utils.mean_check"> |
| <code class="sig-name descname">mean_check</code><span class="sig-paren">(</span><em class="sig-param">generator</em>, <em class="sig-param">mu</em>, <em class="sig-param">sigma</em>, <em class="sig-param">nsamples=1000000</em><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/mxnet/test_utils.html#mean_check"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.test_utils.mean_check" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Test the generator by matching the mean.</p> |
| <dl class="simple"> |
| <dt>We test the sample mean by checking if it falls inside the range</dt><dd><p>(mu - 3 * sigma / sqrt(n), mu + 3 * sigma / sqrt(n))</p> |
| </dd> |
| </dl> |
| <p>References:</p> |
| <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="nd">@incollection</span><span class="p">{</span><span class="n">goucher2009beautiful</span><span class="p">,</span> |
| <span class="n">title</span><span class="o">=</span><span class="p">{</span><span class="n">Beautiful</span> <span class="n">Testing</span><span class="p">:</span> <span class="n">Leading</span> <span class="n">Professionals</span> <span class="n">Reveal</span> <span class="n">How</span> <span class="n">They</span> <span class="n">Improve</span> <span class="n">Software</span><span class="p">},</span> |
| <span class="n">author</span><span class="o">=</span><span class="p">{</span><span class="n">Goucher</span><span class="p">,</span> <span class="n">Adam</span> <span class="ow">and</span> <span class="n">Riley</span><span class="p">,</span> <span class="n">Tim</span><span class="p">},</span> |
| <span class="n">year</span><span class="o">=</span><span class="p">{</span><span class="mi">2009</span><span class="p">},</span> |
| <span class="n">chapter</span><span class="o">=</span><span class="mi">10</span> |
| <span class="p">}</span> |
| </pre></div> |
| </div> |
| <p>Examples:</p> |
| <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">generator</span> <span class="o">=</span> <span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">normal</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mf">1.0</span><span class="p">,</span> <span class="n">size</span><span class="o">=</span><span class="n">x</span><span class="p">)</span> |
| <span class="n">mean_check_ret</span> <span class="o">=</span> <span class="n">mean_check</span><span class="p">(</span><span class="n">generator</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mf">1.0</span><span class="p">)</span> |
| </pre></div> |
| </div> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>generator</strong> (<em>function</em>) – The generator function. It’s expected to generate N i.i.d samples by calling generator(N).</p></li> |
| <li><p><strong>mu</strong> (<em>float</em>) – </p></li> |
| <li><p><strong>sigma</strong> (<em>float</em>) – </p></li> |
| <li><p><strong>nsamples</strong> (<em>int</em>) – </p></li> |
| </ul> |
| </dd> |
| <dt class="field-even">Returns</dt> |
| <dd class="field-even"><p><strong>ret</strong> – Whether the mean test succeeds</p> |
| </dd> |
| <dt class="field-odd">Return type</dt> |
| <dd class="field-odd"><p>bool</p> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="function"> |
| <dt id="mxnet.test_utils.new_matrix_with_real_eigvals_2d"> |
| <code class="sig-name descname">new_matrix_with_real_eigvals_2d</code><span class="sig-paren">(</span><em class="sig-param">n</em><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/mxnet/test_utils.html#new_matrix_with_real_eigvals_2d"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.test_utils.new_matrix_with_real_eigvals_2d" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Generate a well-conditioned matrix with small real eigenvalues.</p> |
| </dd></dl> |
| |
| <dl class="function"> |
| <dt id="mxnet.test_utils.new_matrix_with_real_eigvals_nd"> |
| <code class="sig-name descname">new_matrix_with_real_eigvals_nd</code><span class="sig-paren">(</span><em class="sig-param">shape</em><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/mxnet/test_utils.html#new_matrix_with_real_eigvals_nd"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.test_utils.new_matrix_with_real_eigvals_nd" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Generate well-conditioned matrices with small real eigenvalues.</p> |
| </dd></dl> |
| |
| <dl class="function"> |
| <dt id="mxnet.test_utils.new_orthonormal_matrix_2d"> |
| <code class="sig-name descname">new_orthonormal_matrix_2d</code><span class="sig-paren">(</span><em class="sig-param">n</em><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/mxnet/test_utils.html#new_orthonormal_matrix_2d"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.test_utils.new_orthonormal_matrix_2d" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Generate a orthonormal matrix.</p> |
| </dd></dl> |
| |
| <dl class="function"> |
| <dt id="mxnet.test_utils.new_sym_matrix_with_real_eigvals_2d"> |
| <code class="sig-name descname">new_sym_matrix_with_real_eigvals_2d</code><span class="sig-paren">(</span><em class="sig-param">n</em><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/mxnet/test_utils.html#new_sym_matrix_with_real_eigvals_2d"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.test_utils.new_sym_matrix_with_real_eigvals_2d" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Generate a sym matrix with real eigenvalues.</p> |
| </dd></dl> |
| |
| <dl class="function"> |
| <dt id="mxnet.test_utils.new_sym_matrix_with_real_eigvals_nd"> |
| <code class="sig-name descname">new_sym_matrix_with_real_eigvals_nd</code><span class="sig-paren">(</span><em class="sig-param">shape</em><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/mxnet/test_utils.html#new_sym_matrix_with_real_eigvals_nd"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.test_utils.new_sym_matrix_with_real_eigvals_nd" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Generate sym matrices with real eigenvalues.</p> |
| </dd></dl> |
| |
| <dl class="function"> |
| <dt id="mxnet.test_utils.np_reduce"> |
| <code class="sig-name descname">np_reduce</code><span class="sig-paren">(</span><em class="sig-param">dat</em>, <em class="sig-param">axis</em>, <em class="sig-param">keepdims</em>, <em class="sig-param">numpy_reduce_func</em><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/mxnet/test_utils.html#np_reduce"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.test_utils.np_reduce" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Compatible reduce for old version of NumPy.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>dat</strong> (<em>np.ndarray</em>) – Same as NumPy.</p></li> |
| <li><p><strong>axis</strong> (<em>None</em><em> or </em><em>int</em><em> or </em><em>list-like</em>) – Same as NumPy.</p></li> |
| <li><p><strong>keepdims</strong> (<em>bool</em>) – Same as NumPy.</p></li> |
| <li><p><strong>numpy_reduce_func</strong> (<em>function</em>) – A NumPy reducing function like <code class="docutils literal notranslate"><span class="pre">np.sum</span></code> or <code class="docutils literal notranslate"><span class="pre">np.max</span></code>.</p></li> |
| </ul> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="function"> |
| <dt id="mxnet.test_utils.numeric_grad"> |
| <code class="sig-name descname">numeric_grad</code><span class="sig-paren">(</span><em class="sig-param">executor</em>, <em class="sig-param">location</em>, <em class="sig-param">aux_states=None</em>, <em class="sig-param">eps=0.0001</em>, <em class="sig-param">use_forward_train=True</em>, <em class="sig-param">dtype=<class 'numpy.float32'></em><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/mxnet/test_utils.html#numeric_grad"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.test_utils.numeric_grad" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Calculates a numeric gradient via finite difference method.</p> |
| <p>Class based on Theano’s <cite>theano.gradient.numeric_grad</cite> [1]</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>executor</strong> (<a class="reference internal" href="../executor/index.html#mxnet.executor.Executor" title="mxnet.executor.Executor"><em>Executor</em></a>) – Executor that computes the forward pass.</p></li> |
| <li><p><strong>location</strong> (<em>list of numpy.ndarray</em><em> or </em><em>dict of str to numpy.ndarray</em>) – Argument values used as location to compute gradient |
| Maps the name of arguments to the corresponding numpy.ndarray. |
| Value of all the arguments must be provided.</p></li> |
| <li><p><strong>aux_states</strong> (<em>None</em><em> or </em><em>list of numpy.ndarray</em><em> or </em><em>dict of str to numpy.ndarray</em><em>, </em><em>optional</em>) – Auxiliary states values used as location to compute gradient |
| Maps the name of aux_states to the corresponding numpy.ndarray. |
| Value of all the auxiliary arguments must be provided.</p></li> |
| <li><p><strong>eps</strong> (<em>float</em><em>, </em><em>optional</em>) – Epsilon for the finite-difference method.</p></li> |
| <li><p><strong>use_forward_train</strong> (<em>bool</em><em>, </em><em>optional</em>) – Whether to use <cite>is_train=True</cite> in testing.</p></li> |
| <li><p><strong>dtype</strong> (<em>np.float16</em><em> or </em><em>np.float32</em><em> or </em><em>np.float64</em>) – Datatype for mx.nd.array.</p></li> |
| </ul> |
| </dd> |
| </dl> |
| <p class="rubric">References</p> |
| <p>..[1] <a class="reference external" href="https://github.com/Theano/Theano/blob/master/theano/gradient.py">https://github.com/Theano/Theano/blob/master/theano/gradient.py</a></p> |
| </dd></dl> |
| |
| <dl class="function"> |
| <dt id="mxnet.test_utils.rand_ndarray"> |
| <code class="sig-name descname">rand_ndarray</code><span class="sig-paren">(</span><em class="sig-param">shape</em>, <em class="sig-param">stype='default'</em>, <em class="sig-param">density=None</em>, <em class="sig-param">dtype=None</em>, <em class="sig-param">modifier_func=None</em>, <em class="sig-param">shuffle_csr_indices=False</em>, <em class="sig-param">distribution=None</em>, <em class="sig-param">ctx=None</em><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/mxnet/test_utils.html#rand_ndarray"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.test_utils.rand_ndarray" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Generate a random sparse ndarray. Returns the generated ndarray.</p> |
| </dd></dl> |
| |
| <dl class="function"> |
| <dt id="mxnet.test_utils.rand_sparse_ndarray"> |
| <code class="sig-name descname">rand_sparse_ndarray</code><span class="sig-paren">(</span><em class="sig-param">shape</em>, <em class="sig-param">stype</em>, <em class="sig-param">density=None</em>, <em class="sig-param">dtype=None</em>, <em class="sig-param">distribution=None</em>, <em class="sig-param">data_init=None</em>, <em class="sig-param">rsp_indices=None</em>, <em class="sig-param">modifier_func=None</em>, <em class="sig-param">shuffle_csr_indices=False</em>, <em class="sig-param">ctx=None</em><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/mxnet/test_utils.html#rand_sparse_ndarray"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.test_utils.rand_sparse_ndarray" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Generate a random sparse ndarray. Returns the ndarray, value(np) and indices(np)</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>shape</strong> (<em>list</em><em> or </em><em>tuple</em>) – </p></li> |
| <li><p><strong>stype</strong> (<em>str</em>) – valid values: “csr” or “row_sparse”</p></li> |
| <li><p><strong>density</strong> (<em>float</em><em>, </em><em>optional</em>) – should be between 0 and 1</p></li> |
| <li><p><strong>distribution</strong> (<em>str</em><em>, </em><em>optional</em>) – valid values: “uniform” or “powerlaw”</p></li> |
| <li><p><strong>dtype</strong> (<em>numpy.dtype</em><em>, </em><em>optional</em>) – default value is None</p></li> |
| </ul> |
| </dd> |
| <dt class="field-even">Returns</dt> |
| <dd class="field-even"><p></p> |
| </dd> |
| <dt class="field-odd">Return type</dt> |
| <dd class="field-odd"><p>Result of type CSRNDArray or <a class="reference internal" href="../legacy/ndarray/sparse/index.html#mxnet.ndarray.sparse.RowSparseNDArray" title="mxnet.ndarray.sparse.RowSparseNDArray">RowSparseNDArray</a></p> |
| </dd> |
| </dl> |
| <p class="rubric">Examples</p> |
| <p>Below is an example of the powerlaw distribution with csr as the stype. |
| It calculates the nnz using the shape and density. |
| It fills up the ndarray with exponentially increasing number of elements. |
| If there are enough unused_nnzs, n+1th row will have twice more nnzs compared to nth row. |
| else, remaining unused_nnzs will be used in n+1th row |
| If number of cols is too small and we have already reached column size it will fill up |
| all following columns in all followings rows until we reach the required density.</p> |
| <div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">csr_arr</span><span class="p">,</span> <span class="n">_</span> <span class="o">=</span> <span class="n">rand_sparse_ndarray</span><span class="p">(</span><span class="n">shape</span><span class="o">=</span><span class="p">(</span><span class="mi">5</span><span class="p">,</span> <span class="mi">16</span><span class="p">),</span> <span class="n">stype</span><span class="o">=</span><span class="s2">"csr"</span><span class="p">,</span> |
| <span class="go"> density=0.50, distribution="powerlaw")</span> |
| <span class="gp">>>> </span><span class="n">indptr</span> <span class="o">=</span> <span class="n">csr_arr</span><span class="o">.</span><span class="n">indptr</span><span class="o">.</span><span class="n">asnumpy</span><span class="p">()</span> |
| <span class="gp">>>> </span><span class="n">indices</span> <span class="o">=</span> <span class="n">csr_arr</span><span class="o">.</span><span class="n">indices</span><span class="o">.</span><span class="n">asnumpy</span><span class="p">()</span> |
| <span class="gp">>>> </span><span class="n">data</span> <span class="o">=</span> <span class="n">csr_arr</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">asnumpy</span><span class="p">()</span> |
| <span class="gp">>>> </span><span class="n">row2nnz</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">data</span><span class="p">[</span><span class="n">indptr</span><span class="p">[</span><span class="mi">1</span><span class="p">]:</span><span class="n">indptr</span><span class="p">[</span><span class="mi">2</span><span class="p">]])</span> |
| <span class="gp">>>> </span><span class="n">row3nnz</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">data</span><span class="p">[</span><span class="n">indptr</span><span class="p">[</span><span class="mi">2</span><span class="p">]:</span><span class="n">indptr</span><span class="p">[</span><span class="mi">3</span><span class="p">]])</span> |
| <span class="gp">>>> </span><span class="k">assert</span><span class="p">(</span><span class="n">row3nnz</span> <span class="o">==</span> <span class="mi">2</span><span class="o">*</span><span class="n">row2nnz</span><span class="p">)</span> |
| <span class="gp">>>> </span><span class="n">row4nnz</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">data</span><span class="p">[</span><span class="n">indptr</span><span class="p">[</span><span class="mi">3</span><span class="p">]:</span><span class="n">indptr</span><span class="p">[</span><span class="mi">4</span><span class="p">]])</span> |
| <span class="gp">>>> </span><span class="k">assert</span><span class="p">(</span><span class="n">row4nnz</span> <span class="o">==</span> <span class="mi">2</span><span class="o">*</span><span class="n">row3nnz</span><span class="p">)</span> |
| </pre></div> |
| </div> |
| </dd></dl> |
| |
| <dl class="function"> |
| <dt id="mxnet.test_utils.random_arrays"> |
| <code class="sig-name descname">random_arrays</code><span class="sig-paren">(</span><em class="sig-param">*shapes</em><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/mxnet/test_utils.html#random_arrays"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.test_utils.random_arrays" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Generate some random numpy arrays.</p> |
| </dd></dl> |
| |
| <dl class="function"> |
| <dt id="mxnet.test_utils.random_sample"> |
| <code class="sig-name descname">random_sample</code><span class="sig-paren">(</span><em class="sig-param">population</em>, <em class="sig-param">k</em><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/mxnet/test_utils.html#random_sample"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.test_utils.random_sample" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Return a k length list of the elements chosen from the population sequence.</p> |
| </dd></dl> |
| |
| <dl class="function"> |
| <dt id="mxnet.test_utils.random_uniform_arrays"> |
| <code class="sig-name descname">random_uniform_arrays</code><span class="sig-paren">(</span><em class="sig-param">*shapes</em>, <em class="sig-param">**kwargs</em><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/mxnet/test_utils.html#random_uniform_arrays"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.test_utils.random_uniform_arrays" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Generate some random numpy arrays.</p> |
| </dd></dl> |
| |
| <dl class="function"> |
| <dt id="mxnet.test_utils.same"> |
| <code class="sig-name descname">same</code><span class="sig-paren">(</span><em class="sig-param">a</em>, <em class="sig-param">b</em><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/mxnet/test_utils.html#same"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.test_utils.same" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Test if two NumPy arrays are the same.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>a</strong> (<em>np.ndarray</em>) – </p></li> |
| <li><p><strong>b</strong> (<em>np.ndarray</em>) – </p></li> |
| </ul> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="function"> |
| <dt id="mxnet.test_utils.same_array"> |
| <code class="sig-name descname">same_array</code><span class="sig-paren">(</span><em class="sig-param">array1</em>, <em class="sig-param">array2</em><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/mxnet/test_utils.html#same_array"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.test_utils.same_array" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Check whether two NDArrays sharing the same memory block</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>array1</strong> (<a class="reference internal" href="../legacy/ndarray/ndarray.html#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – First NDArray to be checked</p></li> |
| <li><p><strong>array2</strong> (<a class="reference internal" href="../legacy/ndarray/ndarray.html#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – Second NDArray to be checked</p></li> |
| </ul> |
| </dd> |
| <dt class="field-even">Returns</dt> |
| <dd class="field-even"><p>Whether two NDArrays share the same memory</p> |
| </dd> |
| <dt class="field-odd">Return type</dt> |
| <dd class="field-odd"><p>bool</p> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="function"> |
| <dt id="mxnet.test_utils.same_symbol_structure"> |
| <code class="sig-name descname">same_symbol_structure</code><span class="sig-paren">(</span><em class="sig-param">sym1</em>, <em class="sig-param">sym2</em><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/mxnet/test_utils.html#same_symbol_structure"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.test_utils.same_symbol_structure" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Compare two symbols to check if they have the same computation graph structure. |
| Returns true if operator corresponding to a particular node id is same in both |
| symbols for all nodes</p> |
| </dd></dl> |
| |
| <dl class="function"> |
| <dt id="mxnet.test_utils.set_default_device"> |
| <code class="sig-name descname">set_default_device</code><span class="sig-paren">(</span><em class="sig-param">device</em><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/mxnet/test_utils.html#set_default_device"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.test_utils.set_default_device" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Set default device.</p> |
| </dd></dl> |
| |
| <dl class="function"> |
| <dt id="mxnet.test_utils.shuffle_csr_column_indices"> |
| <code class="sig-name descname">shuffle_csr_column_indices</code><span class="sig-paren">(</span><em class="sig-param">csr</em><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/mxnet/test_utils.html#shuffle_csr_column_indices"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.test_utils.shuffle_csr_column_indices" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Shuffle CSR column indices per row |
| This allows validation of unordered column indices, which is not a requirement |
| for a valid CSR matrix</p> |
| </dd></dl> |
| |
| <dl class="function"> |
| <dt id="mxnet.test_utils.var_check"> |
| <code class="sig-name descname">var_check</code><span class="sig-paren">(</span><em class="sig-param">generator</em>, <em class="sig-param">sigma</em>, <em class="sig-param">nsamples=1000000</em><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/mxnet/test_utils.html#var_check"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.test_utils.var_check" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Test the generator by matching the variance. |
| It will need a large number of samples and is not recommended to use</p> |
| <dl class="simple"> |
| <dt>We test the sample variance by checking if it falls inside the range</dt><dd><p>(sigma^2 - 3 * sqrt(2 * sigma^4 / (n-1)), sigma^2 + 3 * sqrt(2 * sigma^4 / (n-1)))</p> |
| </dd> |
| </dl> |
| <p>References:</p> |
| <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="nd">@incollection</span><span class="p">{</span><span class="n">goucher2009beautiful</span><span class="p">,</span> |
| <span class="n">title</span><span class="o">=</span><span class="p">{</span><span class="n">Beautiful</span> <span class="n">Testing</span><span class="p">:</span> <span class="n">Leading</span> <span class="n">Professionals</span> <span class="n">Reveal</span> <span class="n">How</span> <span class="n">They</span> <span class="n">Improve</span> <span class="n">Software</span><span class="p">},</span> |
| <span class="n">author</span><span class="o">=</span><span class="p">{</span><span class="n">Goucher</span><span class="p">,</span> <span class="n">Adam</span> <span class="ow">and</span> <span class="n">Riley</span><span class="p">,</span> <span class="n">Tim</span><span class="p">},</span> |
| <span class="n">year</span><span class="o">=</span><span class="p">{</span><span class="mi">2009</span><span class="p">},</span> |
| <span class="n">chapter</span><span class="o">=</span><span class="mi">10</span> |
| <span class="p">}</span> |
| </pre></div> |
| </div> |
| <p>Examples:</p> |
| <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">generator</span> <span class="o">=</span> <span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">normal</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mf">1.0</span><span class="p">,</span> <span class="n">size</span><span class="o">=</span><span class="n">x</span><span class="p">)</span> |
| <span class="n">var_check_ret</span> <span class="o">=</span> <span class="n">var_check</span><span class="p">(</span><span class="n">generator</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mf">1.0</span><span class="p">)</span> |
| </pre></div> |
| </div> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>generator</strong> (<em>function</em>) – The generator function. It’s expected to generate N i.i.d samples by calling generator(N).</p></li> |
| <li><p><strong>sigma</strong> (<em>float</em>) – </p></li> |
| <li><p><strong>nsamples</strong> (<em>int</em>) – </p></li> |
| </ul> |
| </dd> |
| <dt class="field-even">Returns</dt> |
| <dd class="field-even"><p><strong>ret</strong> – Whether the variance test succeeds</p> |
| </dd> |
| <dt class="field-odd">Return type</dt> |
| <dd class="field-odd"><p>bool</p> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="function"> |
| <dt id="mxnet.test_utils.verify_generator"> |
| <code class="sig-name descname">verify_generator</code><span class="sig-paren">(</span><em class="sig-param">generator</em>, <em class="sig-param">buckets</em>, <em class="sig-param">probs</em>, <em class="sig-param">nsamples=1000000</em>, <em class="sig-param">nrepeat=5</em>, <em class="sig-param">success_rate=0.2</em>, <em class="sig-param">alpha=0.05</em><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/mxnet/test_utils.html#verify_generator"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.test_utils.verify_generator" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Verify whether the generator is correct using chi-square testing.</p> |
| <dl class="simple"> |
| <dt>The test is repeated for “nrepeat” times and we check if the success rate is</dt><dd><p>above the threshold (25% by default).</p> |
| </dd> |
| </dl> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>generator</strong> (<em>function</em>) – <dl class="simple"> |
| <dt>A function that is assumed to generate i.i.d samples from a specific distribution.</dt><dd><p>generator(N) should generate N random samples.</p> |
| </dd> |
| </dl> |
| </p></li> |
| <li><p><strong>buckets</strong> (<em>list of tuple</em><em> or </em><em>list of number</em>) – <dl class="simple"> |
| <dt>The buckets to run the chi-square the test. Make sure that the buckets cover</dt><dd><p>the whole range of the distribution. Also, the buckets must be in ascending order and |
| have no intersection</p> |
| </dd> |
| </dl> |
| </p></li> |
| <li><p><strong>probs</strong> (<em>list</em><em> or </em><em>tuple</em>) – The ground-truth probability of the random value fall in a specific bucket.</p></li> |
| <li><p><strong>nsamples</strong> (<em>int</em>) – The number of samples to generate for the testing</p></li> |
| <li><p><strong>nrepeat</strong> (<em>int</em>) – The times to repeat the test</p></li> |
| <li><p><strong>success_rate</strong> (<em>float</em>) – The desired success rate</p></li> |
| <li><p><strong>alpha</strong> (<em>float</em>) – The desired threshold for type-I error i.e. when a true null hypothesis is rejected</p></li> |
| </ul> |
| </dd> |
| <dt class="field-even">Returns</dt> |
| <dd class="field-even"><p><strong>cs_ret_l</strong> – The p values of the chi-square test.</p> |
| </dd> |
| <dt class="field-odd">Return type</dt> |
| <dd class="field-odd"><p>list</p> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
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