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| <span class="mdl-layout-title toc">Table Of Contents</span> |
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| <li class="toctree-l1"><a class="reference internal" href="../../../tutorials/index.html">Python Tutorials</a><ul> |
| <li class="toctree-l2"><a class="reference internal" href="../../../tutorials/getting-started/index.html">Getting Started</a><ul> |
| <li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/index.html">Crash Course</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/0-introduction.html">Introduction</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/1-nparray.html">Step 1: Manipulate data with NP on MXNet</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/2-create-nn.html">Step 2: Create a neural network</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/3-autograd.html">Step 3: Automatic differentiation with autograd</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/4-components.html">Step 4: Necessary components that are not in the network</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html">Step 5: <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html#Using-own-data-with-included-Datasets">Using own data with included <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html#Using-your-own-data-with-custom-Datasets">Using your own data with custom <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html#New-in-MXNet-2.0:-faster-C++-backend-dataloaders">New in MXNet 2.0: faster C++ backend dataloaders</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/6-train-nn.html">Step 6: Train a Neural Network</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/7-use-gpus.html">Step 7: Load and Run a NN using GPU</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/to-mxnet/index.html">Moving to MXNet from Other Frameworks</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/to-mxnet/pytorch.html">PyTorch vs Apache MXNet</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/gluon_from_experiment_to_deployment.html">Gluon: from experiment to deployment</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/gluon_migration_guide.html">Gluon2.0: Migration Guide</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/logistic_regression_explained.html">Logistic regression explained</a></li> |
| <li class="toctree-l3"><a class="reference external" href="https://mxnet.apache.org/api/python/docs/tutorials/packages/gluon/image/mnist.html">MNIST</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l2"><a class="reference internal" href="../../../tutorials/packages/index.html">Packages</a><ul> |
| <li class="toctree-l3"><a class="reference internal" href="../../../tutorials/packages/autograd/index.html">Automatic Differentiation</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../tutorials/packages/gluon/index.html">Gluon</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/packages/gluon/blocks/index.html">Blocks</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/blocks/custom-layer.html">Custom Layers</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/blocks/hybridize.html">Hybridize</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/blocks/init.html">Initialization</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/blocks/naming.html">Parameter and Block Naming</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/blocks/nn.html">Layers and Blocks</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/blocks/parameters.html">Parameter Management</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/blocks/save_load_params.html">Saving and Loading Gluon Models</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/blocks/activations/activations.html">Activation Blocks</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/packages/gluon/data/index.html">Data Tutorials</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/data/data_augmentation.html">Image Augmentation</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/data/datasets.html">Gluon <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/data/datasets.html#Using-own-data-with-included-Datasets">Using own data with included <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/data/datasets.html#Using-own-data-with-custom-Datasets">Using own data with custom <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/data/datasets.html#Appendix:-Upgrading-from-Module-DataIter-to-Gluon-DataLoader">Appendix: Upgrading from Module <code class="docutils literal notranslate"><span class="pre">DataIter</span></code> to Gluon <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li> |
| </ul> |
| </li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/packages/gluon/image/index.html">Image Tutorials</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/image/info_gan.html">Image similarity search with InfoGAN</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/image/mnist.html">Handwritten Digit Recognition</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/packages/gluon/loss/index.html">Losses</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/loss/custom-loss.html">Custom Loss Blocks</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/loss/kl_divergence.html">Kullback-Leibler (KL) Divergence</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/loss/loss.html">Loss functions</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/packages/gluon/text/index.html">Text Tutorials</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/text/gnmt.html">Google Neural Machine Translation</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/text/transformer.html">Machine Translation with Transformer</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/packages/gluon/training/index.html">Training</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/training/fit_api_tutorial.html">MXNet Gluon Fit API</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/training/trainer.html">Trainer</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/training/learning_rates/index.html">Learning Rates</a><ul> |
| <li class="toctree-l6"><a class="reference internal" href="../../../tutorials/packages/gluon/training/learning_rates/learning_rate_finder.html">Learning Rate Finder</a></li> |
| <li class="toctree-l6"><a class="reference internal" href="../../../tutorials/packages/gluon/training/learning_rates/learning_rate_schedules.html">Learning Rate Schedules</a></li> |
| <li class="toctree-l6"><a class="reference internal" href="../../../tutorials/packages/gluon/training/learning_rates/learning_rate_schedules_advanced.html">Advanced Learning Rate Schedules</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/training/normalization/index.html">Normalization Blocks</a></li> |
| </ul> |
| </li> |
| </ul> |
| </li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../tutorials/packages/kvstore/index.html">KVStore</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/packages/kvstore/kvstore.html">Distributed Key-Value Store</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../tutorials/packages/legacy/index.html">Legacy</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/packages/legacy/ndarray/index.html">NDArray</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/legacy/ndarray/01-ndarray-intro.html">An Intro: Manipulate Data the MXNet Way with NDArray</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/legacy/ndarray/02-ndarray-operations.html">NDArray Operations</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/legacy/ndarray/03-ndarray-contexts.html">NDArray Contexts</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/legacy/ndarray/gotchas_numpy_in_mxnet.html">Gotchas using NumPy in Apache MXNet</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/legacy/ndarray/sparse/index.html">Tutorials</a><ul> |
| <li class="toctree-l6"><a class="reference internal" href="../../../tutorials/packages/legacy/ndarray/sparse/csr.html">CSRNDArray - NDArray in Compressed Sparse Row Storage Format</a></li> |
| <li class="toctree-l6"><a class="reference internal" href="../../../tutorials/packages/legacy/ndarray/sparse/row_sparse.html">RowSparseNDArray - NDArray for Sparse Gradient Updates</a></li> |
| </ul> |
| </li> |
| </ul> |
| </li> |
| </ul> |
| </li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../tutorials/packages/np/index.html">What is NP on MXNet</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/packages/np/cheat-sheet.html">The NP on MXNet cheat sheet</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/packages/np/np-vs-numpy.html">Differences between NP on MXNet and NumPy</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../tutorials/packages/onnx/index.html">ONNX</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/packages/onnx/fine_tuning_gluon.html">Fine-tuning an ONNX model</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/packages/onnx/inference_on_onnx_model.html">Running inference on MXNet/Gluon from an ONNX model</a></li> |
| <li class="toctree-l4"><a class="reference external" href="https://mxnet.apache.org/api/python/docs/tutorials/deploy/export/onnx.html">Export ONNX Models</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../tutorials/packages/optimizer/index.html">Optimizers</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../tutorials/packages/viz/index.html">Visualization</a><ul> |
| <li class="toctree-l4"><a class="reference external" href="https://mxnet.apache.org/api/faq/visualize_graph">Visualize networks</a></li> |
| </ul> |
| </li> |
| </ul> |
| </li> |
| <li class="toctree-l2"><a class="reference internal" href="../../../tutorials/performance/index.html">Performance</a><ul> |
| <li class="toctree-l3"><a class="reference internal" href="../../../tutorials/performance/compression/index.html">Compression</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/performance/compression/int8.html">Deploy with int-8</a></li> |
| <li class="toctree-l4"><a class="reference external" href="https://mxnet.apache.org/api/faq/float16">Float16</a></li> |
| <li class="toctree-l4"><a class="reference external" href="https://mxnet.apache.org/api/faq/gradient_compression">Gradient Compression</a></li> |
| <li class="toctree-l4"><a class="reference external" href="https://gluon-cv.mxnet.io/build/examples_deployment/int8_inference.html">GluonCV with Quantized Models</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../tutorials/performance/backend/index.html">Accelerated Backend Tools</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/performance/backend/dnnl/index.html">oneDNN</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/performance/backend/dnnl/dnnl_readme.html">Install MXNet with oneDNN</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/performance/backend/dnnl/dnnl_quantization.html">oneDNN Quantization</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/performance/backend/dnnl/dnnl_quantization_inc.html">Improving accuracy with Intel® Neural Compressor</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/performance/backend/tvm.html">Use TVM</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/performance/backend/profiler.html">Profiling MXNet Models</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/performance/backend/amp.html">Using AMP: Automatic Mixed Precision</a></li> |
| </ul> |
| </li> |
| </ul> |
| </li> |
| <li class="toctree-l2"><a class="reference internal" href="../../../tutorials/deploy/index.html">Deployment</a><ul> |
| <li class="toctree-l3"><a class="reference internal" href="../../../tutorials/deploy/export/index.html">Export</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/deploy/export/onnx.html">Exporting to ONNX format</a></li> |
| <li class="toctree-l4"><a class="reference external" href="https://gluon-cv.mxnet.io/build/examples_deployment/export_network.html">Export Gluon CV Models</a></li> |
| <li class="toctree-l4"><a class="reference external" href="https://mxnet.apache.org/api/python/docs/tutorials/packages/gluon/blocks/save_load_params.html">Save / Load Parameters</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../tutorials/deploy/inference/index.html">Inference</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/deploy/inference/cpp.html">Deploy into C++</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/deploy/inference/image_classification_jetson.html">Image Classication using pretrained ResNet-50 model on Jetson module</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../tutorials/deploy/run-on-aws/index.html">Run on AWS</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/deploy/run-on-aws/use_ec2.html">Run on an EC2 Instance</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/deploy/run-on-aws/use_sagemaker.html">Run on Amazon SageMaker</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/deploy/run-on-aws/cloud.html">MXNet on the Cloud</a></li> |
| </ul> |
| </li> |
| </ul> |
| </li> |
| <li class="toctree-l2"><a class="reference internal" href="../../../tutorials/extend/index.html">Extend</a><ul> |
| <li class="toctree-l3"><a class="reference internal" href="../../../tutorials/extend/customop.html">Custom Numpy Operators</a></li> |
| <li class="toctree-l3"><a class="reference external" href="https://mxnet.apache.org/api/faq/new_op">New Operator Creation</a></li> |
| <li class="toctree-l3"><a class="reference external" href="https://mxnet.apache.org/api/faq/add_op_in_backend">New Operator in MXNet Backend</a></li> |
| <li class="toctree-l3"><a class="reference external" href="https://mxnet.apache.org/api/faq/using_rtc">Using RTC for CUDA kernels</a></li> |
| </ul> |
| </li> |
| </ul> |
| </li> |
| <li class="toctree-l1"><a class="reference internal" href="../../../api/index.html">Python API</a><ul> |
| <li class="toctree-l2"><a class="reference internal" href="../../../api/np/index.html">mxnet.np</a><ul> |
| <li class="toctree-l3"><a class="reference internal" href="../../../api/np/arrays.html">Array objects</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../../api/np/arrays.ndarray.html">The N-dimensional array (<code class="xref py py-class docutils literal notranslate"><span class="pre">ndarray</span></code>)</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../api/np/arrays.indexing.html">Indexing</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../api/np/routines.html">Routines</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../../api/np/routines.array-creation.html">Array creation routines</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.eye.html">mxnet.np.eye</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.empty.html">mxnet.np.empty</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.full.html">mxnet.np.full</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.identity.html">mxnet.np.identity</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.ones.html">mxnet.np.ones</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.ones_like.html">mxnet.np.ones_like</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.zeros.html">mxnet.np.zeros</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.zeros_like.html">mxnet.np.zeros_like</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.array.html">mxnet.np.array</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.copy.html">mxnet.np.copy</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.arange.html">mxnet.np.arange</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.linspace.html">mxnet.np.linspace</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.logspace.html">mxnet.np.logspace</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.meshgrid.html">mxnet.np.meshgrid</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.tril.html">mxnet.np.tril</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../api/np/routines.array-manipulation.html">Array manipulation routines</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.reshape.html">mxnet.np.reshape</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.ravel.html">mxnet.np.ravel</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.ndarray.flatten.html">mxnet.np.ndarray.flatten</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.swapaxes.html">mxnet.np.swapaxes</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.ndarray.T.html">mxnet.np.ndarray.T</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.transpose.html">mxnet.np.transpose</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.moveaxis.html">mxnet.np.moveaxis</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.rollaxis.html">mxnet.np.rollaxis</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.expand_dims.html">mxnet.np.expand_dims</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.squeeze.html">mxnet.np.squeeze</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.broadcast_to.html">mxnet.np.broadcast_to</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.broadcast_arrays.html">mxnet.np.broadcast_arrays</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.atleast_1d.html">mxnet.np.atleast_1d</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.atleast_2d.html">mxnet.np.atleast_2d</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.atleast_3d.html">mxnet.np.atleast_3d</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.concatenate.html">mxnet.np.concatenate</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.stack.html">mxnet.np.stack</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.dstack.html">mxnet.np.dstack</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.vstack.html">mxnet.np.vstack</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.column_stack.html">mxnet.np.column_stack</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.hstack.html">mxnet.np.hstack</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.split.html">mxnet.np.split</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.hsplit.html">mxnet.np.hsplit</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.vsplit.html">mxnet.np.vsplit</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.array_split.html">mxnet.np.array_split</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.dsplit.html">mxnet.np.dsplit</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.tile.html">mxnet.np.tile</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.repeat.html">mxnet.np.repeat</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.unique.html">mxnet.np.unique</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.delete.html">mxnet.np.delete</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.insert.html">mxnet.np.insert</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.append.html">mxnet.np.append</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.resize.html">mxnet.np.resize</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.trim_zeros.html">mxnet.np.trim_zeros</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.reshape.html">mxnet.np.reshape</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.flip.html">mxnet.np.flip</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.roll.html">mxnet.np.roll</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.rot90.html">mxnet.np.rot90</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.fliplr.html">mxnet.np.fliplr</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.flipud.html">mxnet.np.flipud</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../api/np/routines.io.html">Input and output</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.genfromtxt.html">mxnet.np.genfromtxt</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.ndarray.tolist.html">mxnet.np.ndarray.tolist</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.set_printoptions.html">mxnet.np.set_printoptions</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../api/np/routines.linalg.html">Linear algebra (<code class="xref py py-mod docutils literal notranslate"><span class="pre">numpy.linalg</span></code>)</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.dot.html">mxnet.np.dot</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.vdot.html">mxnet.np.vdot</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.inner.html">mxnet.np.inner</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.outer.html">mxnet.np.outer</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.tensordot.html">mxnet.np.tensordot</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.einsum.html">mxnet.np.einsum</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.linalg.multi_dot.html">mxnet.np.linalg.multi_dot</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.matmul.html">mxnet.np.matmul</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.linalg.matrix_power.html">mxnet.np.linalg.matrix_power</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.kron.html">mxnet.np.kron</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.linalg.svd.html">mxnet.np.linalg.svd</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.linalg.cholesky.html">mxnet.np.linalg.cholesky</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.linalg.qr.html">mxnet.np.linalg.qr</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.linalg.eig.html">mxnet.np.linalg.eig</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.linalg.eigh.html">mxnet.np.linalg.eigh</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.linalg.eigvals.html">mxnet.np.linalg.eigvals</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.linalg.eigvalsh.html">mxnet.np.linalg.eigvalsh</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.linalg.norm.html">mxnet.np.linalg.norm</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.trace.html">mxnet.np.trace</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.linalg.cond.html">mxnet.np.linalg.cond</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.linalg.det.html">mxnet.np.linalg.det</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.linalg.matrix_rank.html">mxnet.np.linalg.matrix_rank</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.linalg.slogdet.html">mxnet.np.linalg.slogdet</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.linalg.solve.html">mxnet.np.linalg.solve</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.linalg.tensorsolve.html">mxnet.np.linalg.tensorsolve</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.linalg.lstsq.html">mxnet.np.linalg.lstsq</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.linalg.inv.html">mxnet.np.linalg.inv</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.linalg.pinv.html">mxnet.np.linalg.pinv</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.linalg.tensorinv.html">mxnet.np.linalg.tensorinv</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../api/np/routines.math.html">Mathematical functions</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.sin.html">mxnet.np.sin</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.cos.html">mxnet.np.cos</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.tan.html">mxnet.np.tan</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.arcsin.html">mxnet.np.arcsin</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.arccos.html">mxnet.np.arccos</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.arctan.html">mxnet.np.arctan</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.degrees.html">mxnet.np.degrees</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.radians.html">mxnet.np.radians</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.hypot.html">mxnet.np.hypot</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.arctan2.html">mxnet.np.arctan2</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.deg2rad.html">mxnet.np.deg2rad</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.rad2deg.html">mxnet.np.rad2deg</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.unwrap.html">mxnet.np.unwrap</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.sinh.html">mxnet.np.sinh</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.cosh.html">mxnet.np.cosh</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.tanh.html">mxnet.np.tanh</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.arcsinh.html">mxnet.np.arcsinh</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.arccosh.html">mxnet.np.arccosh</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.arctanh.html">mxnet.np.arctanh</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.rint.html">mxnet.np.rint</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.fix.html">mxnet.np.fix</a></li> |
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| <li class="toctree-l3"><a class="reference internal" href="../../../api/npx/generated/mxnet.npx.leaky_relu.html">mxnet.npx.leaky_relu</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../api/npx/generated/mxnet.npx.multibox_detection.html">mxnet.npx.multibox_detection</a></li> |
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| <span class="mdl-layout-title toc">Table Of Contents</span> |
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| |
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| <nav class="mdl-navigation"> |
| <ul> |
| <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> |
| </li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../tutorials/packages/optimizer/index.html">Optimizers</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../tutorials/packages/viz/index.html">Visualization</a><ul> |
| <li class="toctree-l4"><a class="reference external" href="https://mxnet.apache.org/api/faq/visualize_graph">Visualize networks</a></li> |
| </ul> |
| </li> |
| </ul> |
| </li> |
| <li class="toctree-l2"><a class="reference internal" href="../../../tutorials/performance/index.html">Performance</a><ul> |
| <li class="toctree-l3"><a class="reference internal" href="../../../tutorials/performance/compression/index.html">Compression</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/performance/compression/int8.html">Deploy with int-8</a></li> |
| <li class="toctree-l4"><a class="reference external" href="https://mxnet.apache.org/api/faq/float16">Float16</a></li> |
| <li class="toctree-l4"><a class="reference external" href="https://mxnet.apache.org/api/faq/gradient_compression">Gradient Compression</a></li> |
| <li class="toctree-l4"><a class="reference external" href="https://gluon-cv.mxnet.io/build/examples_deployment/int8_inference.html">GluonCV with Quantized Models</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../tutorials/performance/backend/index.html">Accelerated Backend Tools</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/performance/backend/dnnl/index.html">oneDNN</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/performance/backend/dnnl/dnnl_readme.html">Install MXNet with oneDNN</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/performance/backend/dnnl/dnnl_quantization.html">oneDNN Quantization</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/performance/backend/dnnl/dnnl_quantization_inc.html">Improving accuracy with Intel® Neural Compressor</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/performance/backend/tvm.html">Use TVM</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/performance/backend/profiler.html">Profiling MXNet Models</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/performance/backend/amp.html">Using AMP: Automatic Mixed Precision</a></li> |
| </ul> |
| </li> |
| </ul> |
| </li> |
| <li class="toctree-l2"><a class="reference internal" href="../../../tutorials/deploy/index.html">Deployment</a><ul> |
| <li class="toctree-l3"><a class="reference internal" href="../../../tutorials/deploy/export/index.html">Export</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/deploy/export/onnx.html">Exporting to ONNX format</a></li> |
| <li class="toctree-l4"><a class="reference external" href="https://gluon-cv.mxnet.io/build/examples_deployment/export_network.html">Export Gluon CV Models</a></li> |
| <li class="toctree-l4"><a class="reference external" href="https://mxnet.apache.org/api/python/docs/tutorials/packages/gluon/blocks/save_load_params.html">Save / Load Parameters</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../tutorials/deploy/inference/index.html">Inference</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/deploy/inference/cpp.html">Deploy into C++</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/deploy/inference/image_classification_jetson.html">Image Classication using pretrained ResNet-50 model on Jetson module</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../tutorials/deploy/run-on-aws/index.html">Run on AWS</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/deploy/run-on-aws/use_ec2.html">Run on an EC2 Instance</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/deploy/run-on-aws/use_sagemaker.html">Run on Amazon SageMaker</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/deploy/run-on-aws/cloud.html">MXNet on the Cloud</a></li> |
| </ul> |
| </li> |
| </ul> |
| </li> |
| <li class="toctree-l2"><a class="reference internal" href="../../../tutorials/extend/index.html">Extend</a><ul> |
| <li class="toctree-l3"><a class="reference internal" href="../../../tutorials/extend/customop.html">Custom Numpy Operators</a></li> |
| <li class="toctree-l3"><a class="reference external" href="https://mxnet.apache.org/api/faq/new_op">New Operator Creation</a></li> |
| <li class="toctree-l3"><a class="reference external" href="https://mxnet.apache.org/api/faq/add_op_in_backend">New Operator in MXNet Backend</a></li> |
| <li class="toctree-l3"><a class="reference external" href="https://mxnet.apache.org/api/faq/using_rtc">Using RTC for CUDA kernels</a></li> |
| </ul> |
| </li> |
| </ul> |
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| <li class="toctree-l1"><a class="reference internal" href="../../../api/index.html">Python API</a><ul> |
| <li class="toctree-l2"><a class="reference internal" href="../../../api/np/index.html">mxnet.np</a><ul> |
| <li class="toctree-l3"><a class="reference internal" href="../../../api/np/arrays.html">Array objects</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../../api/np/arrays.ndarray.html">The N-dimensional array (<code class="xref py py-class docutils literal notranslate"><span class="pre">ndarray</span></code>)</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../api/np/arrays.indexing.html">Indexing</a></li> |
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| <li class="toctree-l3"><a class="reference internal" href="../../../api/np/routines.html">Routines</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../../api/np/routines.array-creation.html">Array creation routines</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.eye.html">mxnet.np.eye</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.empty.html">mxnet.np.empty</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.full.html">mxnet.np.full</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.identity.html">mxnet.np.identity</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.ones.html">mxnet.np.ones</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.ones_like.html">mxnet.np.ones_like</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.zeros.html">mxnet.np.zeros</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.zeros_like.html">mxnet.np.zeros_like</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.array.html">mxnet.np.array</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.copy.html">mxnet.np.copy</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.arange.html">mxnet.np.arange</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.linspace.html">mxnet.np.linspace</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.logspace.html">mxnet.np.logspace</a></li> |
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| </li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../api/np/routines.array-manipulation.html">Array manipulation routines</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.reshape.html">mxnet.np.reshape</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.ravel.html">mxnet.np.ravel</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.ndarray.flatten.html">mxnet.np.ndarray.flatten</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.swapaxes.html">mxnet.np.swapaxes</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.ndarray.T.html">mxnet.np.ndarray.T</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.transpose.html">mxnet.np.transpose</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.moveaxis.html">mxnet.np.moveaxis</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.rollaxis.html">mxnet.np.rollaxis</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.expand_dims.html">mxnet.np.expand_dims</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.squeeze.html">mxnet.np.squeeze</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.broadcast_to.html">mxnet.np.broadcast_to</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.broadcast_arrays.html">mxnet.np.broadcast_arrays</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.atleast_1d.html">mxnet.np.atleast_1d</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.atleast_2d.html">mxnet.np.atleast_2d</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.atleast_3d.html">mxnet.np.atleast_3d</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.concatenate.html">mxnet.np.concatenate</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.stack.html">mxnet.np.stack</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.dstack.html">mxnet.np.dstack</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.vstack.html">mxnet.np.vstack</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.column_stack.html">mxnet.np.column_stack</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.hstack.html">mxnet.np.hstack</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.split.html">mxnet.np.split</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.hsplit.html">mxnet.np.hsplit</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.vsplit.html">mxnet.np.vsplit</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.array_split.html">mxnet.np.array_split</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.dsplit.html">mxnet.np.dsplit</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.tile.html">mxnet.np.tile</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.repeat.html">mxnet.np.repeat</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.unique.html">mxnet.np.unique</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.delete.html">mxnet.np.delete</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.insert.html">mxnet.np.insert</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.append.html">mxnet.np.append</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.resize.html">mxnet.np.resize</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.trim_zeros.html">mxnet.np.trim_zeros</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.reshape.html">mxnet.np.reshape</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.flip.html">mxnet.np.flip</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.roll.html">mxnet.np.roll</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.rot90.html">mxnet.np.rot90</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.fliplr.html">mxnet.np.fliplr</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.flipud.html">mxnet.np.flipud</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../api/np/routines.io.html">Input and output</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.genfromtxt.html">mxnet.np.genfromtxt</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.ndarray.tolist.html">mxnet.np.ndarray.tolist</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.set_printoptions.html">mxnet.np.set_printoptions</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../api/np/routines.linalg.html">Linear algebra (<code class="xref py py-mod docutils literal notranslate"><span class="pre">numpy.linalg</span></code>)</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.dot.html">mxnet.np.dot</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.vdot.html">mxnet.np.vdot</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.inner.html">mxnet.np.inner</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.outer.html">mxnet.np.outer</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.tensordot.html">mxnet.np.tensordot</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.einsum.html">mxnet.np.einsum</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.linalg.multi_dot.html">mxnet.np.linalg.multi_dot</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.matmul.html">mxnet.np.matmul</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.linalg.matrix_power.html">mxnet.np.linalg.matrix_power</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.kron.html">mxnet.np.kron</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.linalg.svd.html">mxnet.np.linalg.svd</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.linalg.cholesky.html">mxnet.np.linalg.cholesky</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.linalg.qr.html">mxnet.np.linalg.qr</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.linalg.eig.html">mxnet.np.linalg.eig</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.linalg.eigh.html">mxnet.np.linalg.eigh</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.linalg.eigvals.html">mxnet.np.linalg.eigvals</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.linalg.eigvalsh.html">mxnet.np.linalg.eigvalsh</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.linalg.norm.html">mxnet.np.linalg.norm</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.trace.html">mxnet.np.trace</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.linalg.cond.html">mxnet.np.linalg.cond</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.linalg.det.html">mxnet.np.linalg.det</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.linalg.matrix_rank.html">mxnet.np.linalg.matrix_rank</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.linalg.slogdet.html">mxnet.np.linalg.slogdet</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.linalg.solve.html">mxnet.np.linalg.solve</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.linalg.tensorsolve.html">mxnet.np.linalg.tensorsolve</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.linalg.lstsq.html">mxnet.np.linalg.lstsq</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.linalg.inv.html">mxnet.np.linalg.inv</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.linalg.pinv.html">mxnet.np.linalg.pinv</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.linalg.tensorinv.html">mxnet.np.linalg.tensorinv</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../api/np/routines.math.html">Mathematical functions</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.sin.html">mxnet.np.sin</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.cos.html">mxnet.np.cos</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.tan.html">mxnet.np.tan</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.arcsin.html">mxnet.np.arcsin</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.arccos.html">mxnet.np.arccos</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.arctan.html">mxnet.np.arctan</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.degrees.html">mxnet.np.degrees</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.radians.html">mxnet.np.radians</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.hypot.html">mxnet.np.hypot</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.arctan2.html">mxnet.np.arctan2</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.deg2rad.html">mxnet.np.deg2rad</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.rad2deg.html">mxnet.np.rad2deg</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.unwrap.html">mxnet.np.unwrap</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.sinh.html">mxnet.np.sinh</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.cosh.html">mxnet.np.cosh</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.tanh.html">mxnet.np.tanh</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.arcsinh.html">mxnet.np.arcsinh</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.arccosh.html">mxnet.np.arccosh</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.arctanh.html">mxnet.np.arctanh</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.rint.html">mxnet.np.rint</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.fix.html">mxnet.np.fix</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.floor.html">mxnet.np.floor</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.ceil.html">mxnet.np.ceil</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.trunc.html">mxnet.np.trunc</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.around.html">mxnet.np.around</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.round_.html">mxnet.np.round_</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.sum.html">mxnet.np.sum</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.prod.html">mxnet.np.prod</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.cumsum.html">mxnet.np.cumsum</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.nanprod.html">mxnet.np.nanprod</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.nansum.html">mxnet.np.nansum</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.cumprod.html">mxnet.np.cumprod</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.nancumprod.html">mxnet.np.nancumprod</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.nancumsum.html">mxnet.np.nancumsum</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.diff.html">mxnet.np.diff</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.ediff1d.html">mxnet.np.ediff1d</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.cross.html">mxnet.np.cross</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.trapz.html">mxnet.np.trapz</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.exp.html">mxnet.np.exp</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.expm1.html">mxnet.np.expm1</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.log.html">mxnet.np.log</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.log10.html">mxnet.np.log10</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.log2.html">mxnet.np.log2</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.log1p.html">mxnet.np.log1p</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.logaddexp.html">mxnet.np.logaddexp</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.i0.html">mxnet.np.i0</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.ldexp.html">mxnet.np.ldexp</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.signbit.html">mxnet.np.signbit</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.copysign.html">mxnet.np.copysign</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.frexp.html">mxnet.np.frexp</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.spacing.html">mxnet.np.spacing</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.lcm.html">mxnet.np.lcm</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.gcd.html">mxnet.np.gcd</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.add.html">mxnet.np.add</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.reciprocal.html">mxnet.np.reciprocal</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.negative.html">mxnet.np.negative</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.divide.html">mxnet.np.divide</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.power.html">mxnet.np.power</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.subtract.html">mxnet.np.subtract</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.mod.html">mxnet.np.mod</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.multiply.html">mxnet.np.multiply</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.true_divide.html">mxnet.np.true_divide</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.remainder.html">mxnet.np.remainder</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.positive.html">mxnet.np.positive</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.float_power.html">mxnet.np.float_power</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.fmod.html">mxnet.np.fmod</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.modf.html">mxnet.np.modf</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.divmod.html">mxnet.np.divmod</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.floor_divide.html">mxnet.np.floor_divide</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.clip.html">mxnet.np.clip</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.sqrt.html">mxnet.np.sqrt</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.cbrt.html">mxnet.np.cbrt</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.square.html">mxnet.np.square</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.absolute.html">mxnet.np.absolute</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.sign.html">mxnet.np.sign</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.maximum.html">mxnet.np.maximum</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.minimum.html">mxnet.np.minimum</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.fabs.html">mxnet.np.fabs</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.heaviside.html">mxnet.np.heaviside</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.fmax.html">mxnet.np.fmax</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.fmin.html">mxnet.np.fmin</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.nan_to_num.html">mxnet.np.nan_to_num</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.interp.html">mxnet.np.interp</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../api/np/random/index.html">np.random</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/random/generated/mxnet.np.random.choice.html">mxnet.np.random.choice</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/random/generated/mxnet.np.random.shuffle.html">mxnet.np.random.shuffle</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/random/generated/mxnet.np.random.normal.html">mxnet.np.random.normal</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/random/generated/mxnet.np.random.uniform.html">mxnet.np.random.uniform</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/random/generated/mxnet.np.random.rand.html">mxnet.np.random.rand</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/random/generated/mxnet.np.random.randint.html">mxnet.np.random.randint</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/random/generated/mxnet.np.random.beta.html">mxnet.np.random.beta</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/random/generated/mxnet.np.random.chisquare.html">mxnet.np.random.chisquare</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/random/generated/mxnet.np.random.exponential.html">mxnet.np.random.exponential</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/random/generated/mxnet.np.random.f.html">mxnet.np.random.f</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/random/generated/mxnet.np.random.gamma.html">mxnet.np.random.gamma</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/random/generated/mxnet.np.random.gumbel.html">mxnet.np.random.gumbel</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/random/generated/mxnet.np.random.laplace.html">mxnet.np.random.laplace</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/random/generated/mxnet.np.random.logistic.html">mxnet.np.random.logistic</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/random/generated/mxnet.np.random.lognormal.html">mxnet.np.random.lognormal</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/random/generated/mxnet.np.random.multinomial.html">mxnet.np.random.multinomial</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/random/generated/mxnet.np.random.multivariate_normal.html">mxnet.np.random.multivariate_normal</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/random/generated/mxnet.np.random.pareto.html">mxnet.np.random.pareto</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/random/generated/mxnet.np.random.power.html">mxnet.np.random.power</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/random/generated/mxnet.np.random.rayleigh.html">mxnet.np.random.rayleigh</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/random/generated/mxnet.np.random.weibull.html">mxnet.np.random.weibull</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../api/np/routines.sort.html">Sorting, searching, and counting</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.ndarray.sort.html">mxnet.np.ndarray.sort</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.sort.html">mxnet.np.sort</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.lexsort.html">mxnet.np.lexsort</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.argsort.html">mxnet.np.argsort</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.msort.html">mxnet.np.msort</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.partition.html">mxnet.np.partition</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.argpartition.html">mxnet.np.argpartition</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.argmax.html">mxnet.np.argmax</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.argmin.html">mxnet.np.argmin</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.nanargmax.html">mxnet.np.nanargmax</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.nanargmin.html">mxnet.np.nanargmin</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.argwhere.html">mxnet.np.argwhere</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.nonzero.html">mxnet.np.nonzero</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.flatnonzero.html">mxnet.np.flatnonzero</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.where.html">mxnet.np.where</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.searchsorted.html">mxnet.np.searchsorted</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.extract.html">mxnet.np.extract</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.count_nonzero.html">mxnet.np.count_nonzero</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../api/np/routines.statistics.html">Statistics</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.min.html">mxnet.np.min</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.max.html">mxnet.np.max</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.amin.html">mxnet.np.amin</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.amax.html">mxnet.np.amax</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.nanmin.html">mxnet.np.nanmin</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.nanmax.html">mxnet.np.nanmax</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.ptp.html">mxnet.np.ptp</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.percentile.html">mxnet.np.percentile</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.nanpercentile.html">mxnet.np.nanpercentile</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.quantile.html">mxnet.np.quantile</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.nanquantile.html">mxnet.np.nanquantile</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.mean.html">mxnet.np.mean</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.std.html">mxnet.np.std</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.var.html">mxnet.np.var</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.median.html">mxnet.np.median</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.average.html">mxnet.np.average</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.nanmedian.html">mxnet.np.nanmedian</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.nanstd.html">mxnet.np.nanstd</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.nanvar.html">mxnet.np.nanvar</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.corrcoef.html">mxnet.np.corrcoef</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.correlate.html">mxnet.np.correlate</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.cov.html">mxnet.np.cov</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.histogram.html">mxnet.np.histogram</a></li> |
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| |
| <h1>Source code for mxnet.gluon.metric</h1><div class="highlight"><pre> |
| <span></span><span class="c1"># Licensed to the Apache Software Foundation (ASF) under one</span> |
| <span class="c1"># or more contributor license agreements. See the NOTICE file</span> |
| <span class="c1"># distributed with this work for additional information</span> |
| <span class="c1"># regarding copyright ownership. The ASF licenses this file</span> |
| <span class="c1"># to you under the Apache License, Version 2.0 (the</span> |
| <span class="c1"># "License"); you may not use this file except in compliance</span> |
| <span class="c1"># with the License. You may obtain a copy of the License at</span> |
| <span class="c1">#</span> |
| <span class="c1"># http://www.apache.org/licenses/LICENSE-2.0</span> |
| <span class="c1">#</span> |
| <span class="c1"># Unless required by applicable law or agreed to in writing,</span> |
| <span class="c1"># software distributed under the License is distributed on an</span> |
| <span class="c1"># "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY</span> |
| <span class="c1"># KIND, either express or implied. See the License for the</span> |
| <span class="c1"># specific language governing permissions and limitations</span> |
| <span class="c1"># under the License.</span> |
| |
| <span class="c1"># coding: utf-8</span> |
| <span class="c1"># pylint: disable=no-member, too-many-lines</span> |
| |
| <span class="sd">"""Online evaluation metric module."""</span> |
| <span class="kn">import</span> <span class="nn">math</span> |
| <span class="kn">from</span> <span class="nn">collections</span> <span class="kn">import</span> <span class="n">OrderedDict</span> |
| |
| <span class="kn">from</span> <span class="nn">..</span> <span class="kn">import</span> <span class="n">numpy</span> |
| <span class="kn">from</span> <span class="nn">..util</span> <span class="kn">import</span> <span class="n">use_np</span> |
| |
| <span class="kn">from</span> <span class="nn">..base</span> <span class="kn">import</span> <span class="n">numeric_types</span><span class="p">,</span> <span class="n">string_types</span> |
| <span class="kn">from</span> <span class="nn">..</span> <span class="kn">import</span> <span class="n">ndarray</span><span class="p">,</span> <span class="n">npx</span> |
| <span class="kn">from</span> <span class="nn">..</span> <span class="kn">import</span> <span class="n">registry</span> |
| |
| |
| <div class="viewcode-block" id="check_label_shapes"><a class="viewcode-back" href="../../../api/gluon/metric/index.html#mxnet.gluon.metric.check_label_shapes">[docs]</a><span class="k">def</span> <span class="nf">check_label_shapes</span><span class="p">(</span><span class="n">labels</span><span class="p">,</span> <span class="n">preds</span><span class="p">,</span> <span class="n">wrap</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">shape</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""Helper function for checking shape of label and prediction</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> labels : list of `NDArray`</span> |
| <span class="sd"> The labels of the data.</span> |
| |
| <span class="sd"> preds : list of `NDArray`</span> |
| <span class="sd"> Predicted values.</span> |
| |
| <span class="sd"> wrap : boolean</span> |
| <span class="sd"> If True, wrap labels/preds in a list if they are single NDArray</span> |
| |
| <span class="sd"> shape : boolean</span> |
| <span class="sd"> If True, check the shape of labels and preds;</span> |
| <span class="sd"> Otherwise only check their length.</span> |
| <span class="sd"> """</span> |
| <span class="k">if</span> <span class="ow">not</span> <span class="n">shape</span><span class="p">:</span> |
| <span class="n">label_shape</span><span class="p">,</span> <span class="n">pred_shape</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">labels</span><span class="p">),</span> <span class="nb">len</span><span class="p">(</span><span class="n">preds</span><span class="p">)</span> |
| <span class="k">else</span><span class="p">:</span> |
| <span class="n">label_shape</span><span class="p">,</span> <span class="n">pred_shape</span> <span class="o">=</span> <span class="n">labels</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span> <span class="n">preds</span><span class="o">.</span><span class="n">shape</span> |
| |
| <span class="k">if</span> <span class="n">label_shape</span> <span class="o">!=</span> <span class="n">pred_shape</span><span class="p">:</span> |
| <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">"Shape of labels </span><span class="si">{}</span><span class="s2"> does not match shape of "</span> |
| <span class="s2">"predictions </span><span class="si">{}</span><span class="s2">"</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">label_shape</span><span class="p">,</span> <span class="n">pred_shape</span><span class="p">))</span> |
| |
| <span class="k">if</span> <span class="n">wrap</span><span class="p">:</span> |
| <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">labels</span><span class="p">,</span> <span class="n">ndarray</span><span class="o">.</span><span class="n">ndarray</span><span class="o">.</span><span class="n">NDArray</span><span class="p">):</span> |
| <span class="n">labels</span> <span class="o">=</span> <span class="p">[</span><span class="n">labels</span><span class="p">]</span> |
| <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">preds</span><span class="p">,</span> <span class="n">ndarray</span><span class="o">.</span><span class="n">ndarray</span><span class="o">.</span><span class="n">NDArray</span><span class="p">):</span> |
| <span class="n">preds</span> <span class="o">=</span> <span class="p">[</span><span class="n">preds</span><span class="p">]</span> |
| |
| <span class="k">return</span> <span class="n">labels</span><span class="p">,</span> <span class="n">preds</span></div> |
| |
| <div class="viewcode-block" id="EvalMetric"><a class="viewcode-back" href="../../../api/gluon/metric/index.html#mxnet.gluon.metric.EvalMetric">[docs]</a><span class="k">class</span> <span class="nc">EvalMetric</span><span class="p">(</span><span class="nb">object</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""Base class for all evaluation metrics.</span> |
| |
| <span class="sd"> .. note::</span> |
| |
| <span class="sd"> This is a base class that provides common metric interfaces.</span> |
| <span class="sd"> One should not use this class directly, but instead create new metric</span> |
| <span class="sd"> classes that extend it.</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> name : str</span> |
| <span class="sd"> Name of this metric instance for display.</span> |
| <span class="sd"> output_names : list of str, or None</span> |
| <span class="sd"> Name of predictions that should be used when updating with update_dict.</span> |
| <span class="sd"> By default include all predictions.</span> |
| <span class="sd"> label_names : list of str, or None</span> |
| <span class="sd"> Name of labels that should be used when updating with update_dict.</span> |
| <span class="sd"> By default include all labels.</span> |
| <span class="sd"> """</span> |
| <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">name</span><span class="p">,</span> <span class="n">output_names</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> |
| <span class="n">label_names</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">name</span> <span class="o">=</span> <span class="nb">str</span><span class="p">(</span><span class="n">name</span><span class="p">)</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">output_names</span> <span class="o">=</span> <span class="n">output_names</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">label_names</span> <span class="o">=</span> <span class="n">label_names</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_kwargs</span> <span class="o">=</span> <span class="n">kwargs</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">reset</span><span class="p">()</span> |
| |
| <span class="k">def</span> <span class="fm">__str__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span> |
| <span class="k">return</span> <span class="s2">"EvalMetric: </span><span class="si">{}</span><span class="s2">"</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="nb">dict</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">get_name_value</span><span class="p">()))</span> |
| |
| <div class="viewcode-block" id="EvalMetric.get_config"><a class="viewcode-back" href="../../../api/gluon/metric/index.html#mxnet.gluon.metric.EvalMetric.get_config">[docs]</a> <span class="k">def</span> <span class="nf">get_config</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""Save configurations of metric. Can be recreated</span> |
| <span class="sd"> from configs with metric.create(``**config``)</span> |
| <span class="sd"> """</span> |
| <span class="n">config</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_kwargs</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span> |
| <span class="n">config</span><span class="o">.</span><span class="n">update</span><span class="p">({</span> |
| <span class="s1">'metric'</span><span class="p">:</span> <span class="bp">self</span><span class="o">.</span><span class="vm">__class__</span><span class="o">.</span><span class="vm">__name__</span><span class="p">,</span> |
| <span class="s1">'name'</span><span class="p">:</span> <span class="bp">self</span><span class="o">.</span><span class="n">name</span><span class="p">,</span> |
| <span class="s1">'output_names'</span><span class="p">:</span> <span class="bp">self</span><span class="o">.</span><span class="n">output_names</span><span class="p">,</span> |
| <span class="s1">'label_names'</span><span class="p">:</span> <span class="bp">self</span><span class="o">.</span><span class="n">label_names</span><span class="p">})</span> |
| <span class="k">return</span> <span class="n">config</span></div> |
| |
| <div class="viewcode-block" id="EvalMetric.update_dict"><a class="viewcode-back" href="../../../api/gluon/metric/index.html#mxnet.gluon.metric.EvalMetric.update_dict">[docs]</a> <span class="k">def</span> <span class="nf">update_dict</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">label</span><span class="p">,</span> <span class="n">pred</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""Update the internal evaluation with named label and pred</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> labels : OrderedDict of str -> NDArray</span> |
| <span class="sd"> name to array mapping for labels.</span> |
| |
| <span class="sd"> preds : OrderedDict of str -> NDArray</span> |
| <span class="sd"> name to array mapping of predicted outputs.</span> |
| <span class="sd"> """</span> |
| <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">output_names</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span> |
| <span class="n">pred</span> <span class="o">=</span> <span class="p">[</span><span class="n">pred</span><span class="p">[</span><span class="n">name</span><span class="p">]</span> <span class="k">for</span> <span class="n">name</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">output_names</span><span class="p">]</span> |
| <span class="k">else</span><span class="p">:</span> |
| <span class="n">pred</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="n">pred</span><span class="o">.</span><span class="n">values</span><span class="p">())</span> |
| |
| <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">label_names</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span> |
| <span class="n">label</span> <span class="o">=</span> <span class="p">[</span><span class="n">label</span><span class="p">[</span><span class="n">name</span><span class="p">]</span> <span class="k">for</span> <span class="n">name</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">label_names</span><span class="p">]</span> |
| <span class="k">else</span><span class="p">:</span> |
| <span class="n">label</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="n">label</span><span class="o">.</span><span class="n">values</span><span class="p">())</span> |
| |
| <span class="bp">self</span><span class="o">.</span><span class="n">update</span><span class="p">(</span><span class="n">label</span><span class="p">,</span> <span class="n">pred</span><span class="p">)</span></div> |
| |
| <div class="viewcode-block" id="EvalMetric.update"><a class="viewcode-back" href="../../../api/gluon/metric/index.html#mxnet.gluon.metric.EvalMetric.update">[docs]</a> <span class="k">def</span> <span class="nf">update</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">labels</span><span class="p">,</span> <span class="n">preds</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""Updates the internal evaluation result.</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> labels : list of `NDArray`</span> |
| <span class="sd"> The labels of the data.</span> |
| |
| <span class="sd"> preds : list of `NDArray`</span> |
| <span class="sd"> Predicted values.</span> |
| <span class="sd"> """</span> |
| <span class="k">raise</span> <span class="ne">NotImplementedError</span><span class="p">()</span></div> |
| |
| <div class="viewcode-block" id="EvalMetric.reset"><a class="viewcode-back" href="../../../api/gluon/metric/index.html#mxnet.gluon.metric.EvalMetric.reset">[docs]</a> <span class="k">def</span> <span class="nf">reset</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""Resets the internal evaluation result to initial state."""</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">num_inst</span> <span class="o">=</span> <span class="mi">0</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">sum_metric</span> <span class="o">=</span> <span class="mf">0.0</span></div> |
| |
| <div class="viewcode-block" id="EvalMetric.get"><a class="viewcode-back" href="../../../api/gluon/metric/index.html#mxnet.gluon.metric.EvalMetric.get">[docs]</a> <span class="k">def</span> <span class="nf">get</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""Gets the current evaluation result.</span> |
| |
| <span class="sd"> Returns</span> |
| <span class="sd"> -------</span> |
| <span class="sd"> names : list of str</span> |
| <span class="sd"> Name of the metrics.</span> |
| <span class="sd"> values : list of float</span> |
| <span class="sd"> Value of the evaluations.</span> |
| <span class="sd"> """</span> |
| <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">num_inst</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span> |
| <span class="k">return</span> <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">name</span><span class="p">,</span> <span class="nb">float</span><span class="p">(</span><span class="s1">'nan'</span><span class="p">))</span> |
| <span class="k">else</span><span class="p">:</span> |
| <span class="n">res</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">sum_metric</span> <span class="o">/</span> <span class="bp">self</span><span class="o">.</span><span class="n">num_inst</span> |
| <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">res</span><span class="p">,</span> <span class="n">numpy</span><span class="o">.</span><span class="n">ndarray</span><span class="p">)</span> <span class="ow">and</span> <span class="nb">len</span><span class="p">(</span><span class="n">res</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span> |
| <span class="c1"># currently calling ' c = mxnet.numpy.array([1,2,3]).sum() ' would get</span> |
| <span class="c1"># ' array(6.) ', a ndarray with shape ()</span> |
| <span class="c1"># In this case, returning a 'float' in .get() is more explicit.</span> |
| <span class="n">res</span> <span class="o">=</span> <span class="n">res</span><span class="o">.</span><span class="n">item</span><span class="p">()</span> |
| <span class="k">return</span> <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">name</span><span class="p">,</span> <span class="n">res</span><span class="p">)</span></div> |
| |
| <div class="viewcode-block" id="EvalMetric.get_name_value"><a class="viewcode-back" href="../../../api/gluon/metric/index.html#mxnet.gluon.metric.EvalMetric.get_name_value">[docs]</a> <span class="k">def</span> <span class="nf">get_name_value</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""Returns zipped name and value pairs.</span> |
| |
| <span class="sd"> Returns</span> |
| <span class="sd"> -------</span> |
| <span class="sd"> list of tuples</span> |
| <span class="sd"> A (name, value) tuple list.</span> |
| <span class="sd"> """</span> |
| <span class="n">name</span><span class="p">,</span> <span class="n">value</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">get</span><span class="p">()</span> |
| <span class="k">if</span> <span class="ow">not</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">name</span><span class="p">,</span> <span class="nb">list</span><span class="p">):</span> |
| <span class="n">name</span> <span class="o">=</span> <span class="p">[</span><span class="n">name</span><span class="p">]</span> |
| <span class="k">if</span> <span class="ow">not</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">value</span><span class="p">,</span> <span class="nb">list</span><span class="p">):</span> |
| <span class="n">value</span> <span class="o">=</span> <span class="p">[</span><span class="n">value</span><span class="p">]</span> |
| <span class="k">return</span> <span class="nb">list</span><span class="p">(</span><span class="nb">zip</span><span class="p">(</span><span class="n">name</span><span class="p">,</span> <span class="n">value</span><span class="p">))</span></div></div> |
| |
| <span class="c1"># pylint: disable=invalid-name</span> |
| <span class="n">register</span> <span class="o">=</span> <span class="n">registry</span><span class="o">.</span><span class="n">get_register_func</span><span class="p">(</span><span class="n">EvalMetric</span><span class="p">,</span> <span class="s1">'metric'</span><span class="p">)</span> |
| <span class="n">alias</span> <span class="o">=</span> <span class="n">registry</span><span class="o">.</span><span class="n">get_alias_func</span><span class="p">(</span><span class="n">EvalMetric</span><span class="p">,</span> <span class="s1">'metric'</span><span class="p">)</span> |
| <span class="n">_create</span> <span class="o">=</span> <span class="n">registry</span><span class="o">.</span><span class="n">get_create_func</span><span class="p">(</span><span class="n">EvalMetric</span><span class="p">,</span> <span class="s1">'metric'</span><span class="p">)</span> |
| <span class="c1"># pylint: enable=invalid-name</span> |
| |
| |
| <div class="viewcode-block" id="create"><a class="viewcode-back" href="../../../api/gluon/metric/index.html#mxnet.gluon.metric.create">[docs]</a><span class="k">def</span> <span class="nf">create</span><span class="p">(</span><span class="n">metric</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""Creates evaluation metric from metric names or instances of EvalMetric</span> |
| <span class="sd"> or a custom metric function.</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> metric : str or callable</span> |
| <span class="sd"> Specifies the metric to create.</span> |
| <span class="sd"> This argument must be one of the below:</span> |
| |
| <span class="sd"> - Name of a metric.</span> |
| <span class="sd"> - An instance of `EvalMetric`.</span> |
| <span class="sd"> - A list, each element of which is a metric or a metric name.</span> |
| <span class="sd"> - An evaluation function that computes custom metric for a given batch of</span> |
| <span class="sd"> labels and predictions.</span> |
| <span class="sd"> *args : list</span> |
| <span class="sd"> Additional arguments to metric constructor.</span> |
| <span class="sd"> Only used when metric is str.</span> |
| <span class="sd"> **kwargs : dict</span> |
| <span class="sd"> Additional arguments to metric constructor.</span> |
| <span class="sd"> Only used when metric is str</span> |
| |
| <span class="sd"> Examples</span> |
| <span class="sd"> --------</span> |
| <span class="sd"> >>> def custom_metric(label, pred):</span> |
| <span class="sd"> ... return np.mean(np.abs(label - pred))</span> |
| <span class="sd"> ...</span> |
| <span class="sd"> >>> metric1 = mx.gluon.metric.create('acc')</span> |
| <span class="sd"> >>> metric2 = mx.gluon.metric.create(custom_metric)</span> |
| <span class="sd"> >>> metric3 = mx.gluon.metric.create([metric1, metric2, 'rmse'])</span> |
| <span class="sd"> """</span> |
| <span class="k">if</span> <span class="n">callable</span><span class="p">(</span><span class="n">metric</span><span class="p">):</span> |
| <span class="k">return</span> <span class="n">CustomMetric</span><span class="p">(</span><span class="n">metric</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span> |
| <span class="k">elif</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">metric</span><span class="p">,</span> <span class="nb">list</span><span class="p">):</span> |
| <span class="n">composite_metric</span> <span class="o">=</span> <span class="n">CompositeEvalMetric</span><span class="p">()</span> |
| <span class="k">for</span> <span class="n">child_metric</span> <span class="ow">in</span> <span class="n">metric</span><span class="p">:</span> |
| <span class="n">composite_metric</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">create</span><span class="p">(</span><span class="n">child_metric</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">))</span> |
| <span class="k">return</span> <span class="n">composite_metric</span> |
| |
| <span class="k">return</span> <span class="n">_create</span><span class="p">(</span><span class="n">metric</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span></div> |
| |
| |
| <div class="viewcode-block" id="CompositeEvalMetric"><a class="viewcode-back" href="../../../api/gluon/metric/index.html#mxnet.gluon.metric.CompositeEvalMetric">[docs]</a><span class="nd">@register</span> |
| <span class="nd">@alias</span><span class="p">(</span><span class="s1">'composite'</span><span class="p">)</span> |
| <span class="k">class</span> <span class="nc">CompositeEvalMetric</span><span class="p">(</span><span class="n">EvalMetric</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""Manages multiple evaluation metrics.</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> metrics : list of EvalMetric</span> |
| <span class="sd"> List of child metrics.</span> |
| <span class="sd"> name : str</span> |
| <span class="sd"> Name of this metric instance for display.</span> |
| <span class="sd"> output_names : list of str, or None</span> |
| <span class="sd"> Name of predictions that should be used when updating with update_dict.</span> |
| <span class="sd"> By default include all predictions.</span> |
| <span class="sd"> label_names : list of str, or None</span> |
| <span class="sd"> Name of labels that should be used when updating with update_dict.</span> |
| <span class="sd"> By default include all labels.</span> |
| |
| <span class="sd"> Examples</span> |
| <span class="sd"> --------</span> |
| <span class="sd"> >>> predicts = [mx.np.array([[0.3, 0.7], [0, 1.], [0.4, 0.6]])]</span> |
| <span class="sd"> >>> labels = [mx.np.array([0, 1, 1])]</span> |
| <span class="sd"> >>> eval_metrics_1 = mx.gluon.metric.Accuracy()</span> |
| <span class="sd"> >>> eval_metrics_2 = mx.gluon.metric.F1()</span> |
| <span class="sd"> >>> eval_metrics = mx.gluon.metric.CompositeEvalMetric()</span> |
| <span class="sd"> >>> for child_metric in [eval_metrics_1, eval_metrics_2]:</span> |
| <span class="sd"> >>> eval_metrics.add(child_metric)</span> |
| <span class="sd"> >>> eval_metrics.update(labels = labels, preds = predicts)</span> |
| <span class="sd"> >>> eval_metrics.get()</span> |
| <span class="sd"> (['accuracy', 'f1'], [0.6666666666666666, 0.8])</span> |
| <span class="sd"> """</span> |
| |
| <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">metrics</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">name</span><span class="o">=</span><span class="s1">'composite'</span><span class="p">,</span> |
| <span class="n">output_names</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">label_names</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> |
| <span class="nb">super</span><span class="p">(</span><span class="n">CompositeEvalMetric</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span> |
| <span class="n">name</span><span class="p">,</span> <span class="n">output_names</span><span class="o">=</span><span class="n">output_names</span><span class="p">,</span> <span class="n">label_names</span><span class="o">=</span><span class="n">label_names</span><span class="p">)</span> |
| <span class="k">if</span> <span class="n">metrics</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span> |
| <span class="n">metrics</span> <span class="o">=</span> <span class="p">[]</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">metrics</span> <span class="o">=</span> <span class="p">[</span><span class="n">create</span><span class="p">(</span><span class="n">i</span><span class="p">)</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">metrics</span><span class="p">]</span> |
| |
| <div class="viewcode-block" id="CompositeEvalMetric.add"><a class="viewcode-back" href="../../../api/gluon/metric/index.html#mxnet.gluon.metric.CompositeEvalMetric.add">[docs]</a> <span class="k">def</span> <span class="nf">add</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">metric</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""Adds a child metric.</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> metric</span> |
| <span class="sd"> A metric instance.</span> |
| <span class="sd"> """</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">metrics</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">create</span><span class="p">(</span><span class="n">metric</span><span class="p">))</span></div> |
| |
| <div class="viewcode-block" id="CompositeEvalMetric.get_metric"><a class="viewcode-back" href="../../../api/gluon/metric/index.html#mxnet.gluon.metric.CompositeEvalMetric.get_metric">[docs]</a> <span class="k">def</span> <span class="nf">get_metric</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">index</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""Returns a child metric.</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> index : int</span> |
| <span class="sd"> Index of child metric in the list of metrics.</span> |
| <span class="sd"> """</span> |
| <span class="k">try</span><span class="p">:</span> |
| <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">metrics</span><span class="p">[</span><span class="n">index</span><span class="p">]</span> |
| <span class="k">except</span> <span class="ne">IndexError</span><span class="p">:</span> |
| <span class="k">return</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">"Metric index </span><span class="si">{}</span><span class="s2"> is out of range 0 and </span><span class="si">{}</span><span class="s2">"</span><span class="o">.</span><span class="n">format</span><span class="p">(</span> |
| <span class="n">index</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">metrics</span><span class="p">)))</span></div> |
| |
| <div class="viewcode-block" id="CompositeEvalMetric.update_dict"><a class="viewcode-back" href="../../../api/gluon/metric/index.html#mxnet.gluon.metric.CompositeEvalMetric.update_dict">[docs]</a> <span class="k">def</span> <span class="nf">update_dict</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">labels</span><span class="p">,</span> <span class="n">preds</span><span class="p">):</span> <span class="c1"># pylint: disable=arguments-differ</span> |
| <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">label_names</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span> |
| <span class="n">labels</span> <span class="o">=</span> <span class="n">OrderedDict</span><span class="p">([</span><span class="n">i</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">labels</span><span class="o">.</span><span class="n">items</span><span class="p">()</span> |
| <span class="k">if</span> <span class="n">i</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">label_names</span><span class="p">])</span> |
| <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">output_names</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span> |
| <span class="n">preds</span> <span class="o">=</span> <span class="n">OrderedDict</span><span class="p">([</span><span class="n">i</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">preds</span><span class="o">.</span><span class="n">items</span><span class="p">()</span> |
| <span class="k">if</span> <span class="n">i</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">output_names</span><span class="p">])</span> |
| |
| <span class="k">for</span> <span class="n">metric</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">metrics</span><span class="p">:</span> |
| <span class="n">metric</span><span class="o">.</span><span class="n">update_dict</span><span class="p">(</span><span class="n">labels</span><span class="p">,</span> <span class="n">preds</span><span class="p">)</span></div> |
| |
| <div class="viewcode-block" id="CompositeEvalMetric.update"><a class="viewcode-back" href="../../../api/gluon/metric/index.html#mxnet.gluon.metric.CompositeEvalMetric.update">[docs]</a> <span class="k">def</span> <span class="nf">update</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">labels</span><span class="p">,</span> <span class="n">preds</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""Updates the internal evaluation result.</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> labels : list of `NDArray`</span> |
| <span class="sd"> The labels of the data.</span> |
| |
| <span class="sd"> preds : list of `NDArray`</span> |
| <span class="sd"> Predicted values.</span> |
| <span class="sd"> """</span> |
| <span class="k">for</span> <span class="n">metric</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">metrics</span><span class="p">:</span> |
| <span class="n">metric</span><span class="o">.</span><span class="n">update</span><span class="p">(</span><span class="n">labels</span><span class="p">,</span> <span class="n">preds</span><span class="p">)</span></div> |
| |
| <div class="viewcode-block" id="CompositeEvalMetric.reset"><a class="viewcode-back" href="../../../api/gluon/metric/index.html#mxnet.gluon.metric.CompositeEvalMetric.reset">[docs]</a> <span class="k">def</span> <span class="nf">reset</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""Resets the internal evaluation result to initial state."""</span> |
| <span class="k">try</span><span class="p">:</span> |
| <span class="k">for</span> <span class="n">metric</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">metrics</span><span class="p">:</span> |
| <span class="n">metric</span><span class="o">.</span><span class="n">reset</span><span class="p">()</span> |
| <span class="k">except</span> <span class="ne">AttributeError</span><span class="p">:</span> |
| <span class="k">pass</span></div> |
| |
| <div class="viewcode-block" id="CompositeEvalMetric.get"><a class="viewcode-back" href="../../../api/gluon/metric/index.html#mxnet.gluon.metric.CompositeEvalMetric.get">[docs]</a> <span class="k">def</span> <span class="nf">get</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""Returns the current evaluation result.</span> |
| |
| <span class="sd"> Returns</span> |
| <span class="sd"> -------</span> |
| <span class="sd"> names : list of str</span> |
| <span class="sd"> Name of the metrics.</span> |
| <span class="sd"> values : list of float</span> |
| <span class="sd"> Value of the evaluations.</span> |
| <span class="sd"> """</span> |
| <span class="n">names</span> <span class="o">=</span> <span class="p">[]</span> |
| <span class="n">values</span> <span class="o">=</span> <span class="p">[]</span> |
| <span class="k">for</span> <span class="n">metric</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">metrics</span><span class="p">:</span> |
| <span class="n">name</span><span class="p">,</span> <span class="n">value</span> <span class="o">=</span> <span class="n">metric</span><span class="o">.</span><span class="n">get</span><span class="p">()</span> |
| <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">name</span><span class="p">,</span> <span class="n">string_types</span><span class="p">):</span> |
| <span class="n">name</span> <span class="o">=</span> <span class="p">[</span><span class="n">name</span><span class="p">]</span> |
| <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">value</span><span class="p">,</span> <span class="n">numeric_types</span><span class="p">):</span> |
| <span class="n">value</span> <span class="o">=</span> <span class="p">[</span><span class="n">value</span><span class="p">]</span> |
| <span class="n">names</span><span class="o">.</span><span class="n">extend</span><span class="p">(</span><span class="n">name</span><span class="p">)</span> |
| <span class="n">values</span><span class="o">.</span><span class="n">extend</span><span class="p">(</span><span class="n">value</span><span class="p">)</span> |
| <span class="k">return</span> <span class="p">(</span><span class="n">names</span><span class="p">,</span> <span class="n">values</span><span class="p">)</span></div> |
| |
| <div class="viewcode-block" id="CompositeEvalMetric.get_config"><a class="viewcode-back" href="../../../api/gluon/metric/index.html#mxnet.gluon.metric.CompositeEvalMetric.get_config">[docs]</a> <span class="k">def</span> <span class="nf">get_config</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span> |
| <span class="n">config</span> <span class="o">=</span> <span class="nb">super</span><span class="p">(</span><span class="n">CompositeEvalMetric</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="n">get_config</span><span class="p">()</span> |
| <span class="n">config</span><span class="o">.</span><span class="n">update</span><span class="p">({</span><span class="s1">'metrics'</span><span class="p">:</span> <span class="p">[</span><span class="n">i</span><span class="o">.</span><span class="n">get_config</span><span class="p">()</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">metrics</span><span class="p">]})</span> |
| <span class="k">return</span> <span class="n">config</span></div></div> |
| |
| |
| <span class="c1">########################</span> |
| <span class="c1"># CLASSIFICATION METRICS</span> |
| <span class="c1">########################</span> |
| |
| |
| <div class="viewcode-block" id="Accuracy"><a class="viewcode-back" href="../../../api/gluon/metric/index.html#mxnet.gluon.metric.Accuracy">[docs]</a><span class="nd">@register</span> |
| <span class="nd">@alias</span><span class="p">(</span><span class="s1">'acc'</span><span class="p">)</span> |
| <span class="nd">@use_np</span> |
| <span class="k">class</span> <span class="nc">Accuracy</span><span class="p">(</span><span class="n">EvalMetric</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""Computes accuracy classification score.</span> |
| |
| <span class="sd"> The accuracy score is defined as</span> |
| |
| <span class="sd"> .. math::</span> |
| |
| <span class="sd"> \\text{accuracy}(y, \\hat{y}) = \\frac{1}{n} \\sum_{i=0}^{n-1}</span> |
| <span class="sd"> \\text{1}(\\hat{y_i} == y_i)</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> axis : int, default=1</span> |
| <span class="sd"> The axis that represents classes</span> |
| <span class="sd"> name : str</span> |
| <span class="sd"> Name of this metric instance for display.</span> |
| <span class="sd"> output_names : list of str, or None</span> |
| <span class="sd"> Name of predictions that should be used when updating with update_dict.</span> |
| <span class="sd"> By default include all predictions.</span> |
| <span class="sd"> label_names : list of str, or None</span> |
| <span class="sd"> Name of labels that should be used when updating with update_dict.</span> |
| <span class="sd"> By default include all labels.</span> |
| |
| <span class="sd"> Examples</span> |
| <span class="sd"> --------</span> |
| <span class="sd"> >>> predicts = [mx.np.array([[0.3, 0.7], [0, 1.], [0.4, 0.6]])]</span> |
| <span class="sd"> >>> labels = [mx.np.array([0, 1, 1])]</span> |
| <span class="sd"> >>> acc = mx.gluon.metric.Accuracy()</span> |
| <span class="sd"> >>> acc.update(preds = predicts, labels = labels)</span> |
| <span class="sd"> >>> acc.get()</span> |
| <span class="sd"> ('accuracy', 0.6666666666666666)</span> |
| <span class="sd"> """</span> |
| <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">name</span><span class="o">=</span><span class="s1">'accuracy'</span><span class="p">,</span> |
| <span class="n">output_names</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">label_names</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> |
| <span class="nb">super</span><span class="p">(</span><span class="n">Accuracy</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span> |
| <span class="n">name</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="n">axis</span><span class="p">,</span> |
| <span class="n">output_names</span><span class="o">=</span><span class="n">output_names</span><span class="p">,</span> <span class="n">label_names</span><span class="o">=</span><span class="n">label_names</span><span class="p">)</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">axis</span> <span class="o">=</span> <span class="n">axis</span> |
| |
| <div class="viewcode-block" id="Accuracy.update"><a class="viewcode-back" href="../../../api/gluon/metric/index.html#mxnet.gluon.metric.Accuracy.update">[docs]</a> <span class="k">def</span> <span class="nf">update</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">labels</span><span class="p">,</span> <span class="n">preds</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""Updates the internal evaluation result.</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> labels : list of `NDArray`</span> |
| <span class="sd"> The labels of the data with class indices as values, one per sample.</span> |
| |
| <span class="sd"> preds : list of `NDArray`</span> |
| <span class="sd"> Prediction values for samples. Each prediction value can either be the class index,</span> |
| <span class="sd"> or a vector of likelihoods for all classes.</span> |
| <span class="sd"> """</span> |
| <span class="n">labels</span><span class="p">,</span> <span class="n">preds</span> <span class="o">=</span> <span class="n">check_label_shapes</span><span class="p">(</span><span class="n">labels</span><span class="p">,</span> <span class="n">preds</span><span class="p">,</span> <span class="kc">True</span><span class="p">)</span> |
| |
| <span class="k">for</span> <span class="n">label</span><span class="p">,</span> <span class="n">pred_label</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="n">labels</span><span class="p">,</span> <span class="n">preds</span><span class="p">):</span> |
| <span class="n">pred_label</span> <span class="o">=</span> <span class="n">pred_label</span><span class="o">.</span><span class="n">as_np_ndarray</span><span class="p">()</span><span class="o">.</span><span class="n">to_device</span><span class="p">(</span><span class="n">label</span><span class="o">.</span><span class="n">device</span><span class="p">)</span> |
| <span class="n">label</span> <span class="o">=</span> <span class="n">label</span><span class="o">.</span><span class="n">as_np_ndarray</span><span class="p">()</span> |
| <span class="k">if</span> <span class="n">pred_label</span><span class="o">.</span><span class="n">shape</span> <span class="o">!=</span> <span class="n">label</span><span class="o">.</span><span class="n">shape</span><span class="p">:</span> |
| <span class="n">pred_label</span> <span class="o">=</span> <span class="n">pred_label</span><span class="o">.</span><span class="n">argmax</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">axis</span><span class="p">)</span> |
| <span class="n">pred_label</span> <span class="o">=</span> <span class="n">pred_label</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="s1">'int32'</span><span class="p">)</span> |
| <span class="n">label</span> <span class="o">=</span> <span class="n">label</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="s1">'int32'</span><span class="p">)</span> |
| <span class="c1"># flatten before checking shapes to avoid shape miss match</span> |
| <span class="n">label</span> <span class="o">=</span> <span class="n">label</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">)</span> |
| <span class="n">pred_label</span> <span class="o">=</span> <span class="n">pred_label</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">)</span> |
| |
| <span class="n">check_label_shapes</span><span class="p">(</span><span class="n">label</span><span class="p">,</span> <span class="n">pred_label</span><span class="p">)</span> |
| |
| <span class="n">num_correct</span> <span class="o">=</span> <span class="p">(</span><span class="n">pred_label</span> <span class="o">==</span> <span class="n">label</span><span class="p">)</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="s1">'float64'</span><span class="p">)</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">sum_metric</span> <span class="o">+=</span> <span class="n">num_correct</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">num_inst</span> <span class="o">+=</span> <span class="nb">len</span><span class="p">(</span><span class="n">pred_label</span><span class="p">)</span></div></div> |
| |
| |
| <div class="viewcode-block" id="TopKAccuracy"><a class="viewcode-back" href="../../../api/gluon/metric/index.html#mxnet.gluon.metric.TopKAccuracy">[docs]</a><span class="nd">@register</span> |
| <span class="nd">@alias</span><span class="p">(</span><span class="s1">'top_k_accuracy'</span><span class="p">,</span> <span class="s1">'top_k_acc'</span><span class="p">)</span> |
| <span class="nd">@use_np</span> |
| <span class="k">class</span> <span class="nc">TopKAccuracy</span><span class="p">(</span><span class="n">EvalMetric</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""Computes top k predictions accuracy.</span> |
| |
| <span class="sd"> `TopKAccuracy` differs from Accuracy in that it considers the prediction</span> |
| <span class="sd"> to be ``True`` as long as the ground truth label is in the top K</span> |
| <span class="sd"> predicated labels.</span> |
| |
| <span class="sd"> If `top_k` = ``1``, then `TopKAccuracy` is identical to `Accuracy`.</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> top_k : int</span> |
| <span class="sd"> Whether targets are in top k predictions.</span> |
| <span class="sd"> name : str</span> |
| <span class="sd"> Name of this metric instance for display.</span> |
| <span class="sd"> output_names : list of str, or None</span> |
| <span class="sd"> Name of predictions that should be used when updating with update_dict.</span> |
| <span class="sd"> By default include all predictions.</span> |
| <span class="sd"> label_names : list of str, or None</span> |
| <span class="sd"> Name of labels that should be used when updating with update_dict.</span> |
| <span class="sd"> By default include all labels.</span> |
| |
| <span class="sd"> Examples</span> |
| <span class="sd"> --------</span> |
| <span class="sd"> >>> np.random.seed(999)</span> |
| <span class="sd"> >>> top_k = 3</span> |
| <span class="sd"> >>> labels = [mx.np.array([2, 6, 9, 2, 3, 4, 7, 8, 9, 6])]</span> |
| <span class="sd"> >>> predicts = [mx.np.array(np.random.rand(10, 10))]</span> |
| <span class="sd"> >>> acc = mx.gluon.metric.TopKAccuracy(top_k=top_k)</span> |
| <span class="sd"> >>> acc.update(labels, predicts)</span> |
| <span class="sd"> >>> acc.get()</span> |
| <span class="sd"> ('top_k_accuracy', 0.3)</span> |
| <span class="sd"> """</span> |
| |
| <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">top_k</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">name</span><span class="o">=</span><span class="s1">'top_k_accuracy'</span><span class="p">,</span> |
| <span class="n">output_names</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">label_names</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> |
| <span class="nb">super</span><span class="p">(</span><span class="n">TopKAccuracy</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span> |
| <span class="n">name</span><span class="p">,</span> <span class="n">top_k</span><span class="o">=</span><span class="n">top_k</span><span class="p">,</span> |
| <span class="n">output_names</span><span class="o">=</span><span class="n">output_names</span><span class="p">,</span> <span class="n">label_names</span><span class="o">=</span><span class="n">label_names</span><span class="p">)</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">top_k</span> <span class="o">=</span> <span class="n">top_k</span> |
| <span class="k">assert</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">top_k</span> <span class="o">></span> <span class="mi">1</span><span class="p">),</span> <span class="s1">'Please use Accuracy if top_k is no more than 1'</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">name</span> <span class="o">+=</span> <span class="sa">f</span><span class="s1">'_</span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">top_k</span><span class="si">}</span><span class="s1">'</span> |
| |
| <div class="viewcode-block" id="TopKAccuracy.update"><a class="viewcode-back" href="../../../api/gluon/metric/index.html#mxnet.gluon.metric.TopKAccuracy.update">[docs]</a> <span class="k">def</span> <span class="nf">update</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">labels</span><span class="p">,</span> <span class="n">preds</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""Updates the internal evaluation result.</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> labels : list of `NDArray`</span> |
| <span class="sd"> The labels of the data.</span> |
| |
| <span class="sd"> preds : list of `NDArray`</span> |
| <span class="sd"> Predicted values.</span> |
| <span class="sd"> """</span> |
| <span class="n">labels</span><span class="p">,</span> <span class="n">preds</span> <span class="o">=</span> <span class="n">check_label_shapes</span><span class="p">(</span><span class="n">labels</span><span class="p">,</span> <span class="n">preds</span><span class="p">,</span> <span class="kc">True</span><span class="p">)</span> |
| |
| <span class="k">for</span> <span class="n">label</span><span class="p">,</span> <span class="n">pred_label</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="n">labels</span><span class="p">,</span> <span class="n">preds</span><span class="p">):</span> |
| <span class="k">assert</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">pred_label</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span> <span class="o"><=</span> <span class="mi">2</span><span class="p">),</span> <span class="s1">'Predictions should be no more than 2 dims'</span> |
| <span class="c1"># Using argpartition here instead of argsort is safe because</span> |
| <span class="c1"># we do not care about the order of top k elements. It is</span> |
| <span class="c1"># much faster, which is important since that computation is</span> |
| <span class="c1"># single-threaded due to Python GIL.</span> |
| <span class="n">pred_label</span> <span class="o">=</span> <span class="n">pred_label</span><span class="o">.</span><span class="n">as_np_ndarray</span><span class="p">()</span><span class="o">.</span><span class="n">to_device</span><span class="p">(</span><span class="n">label</span><span class="o">.</span><span class="n">device</span><span class="p">)</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="s1">'float32'</span><span class="p">)</span> |
| <span class="n">pred_label</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">argpartition</span><span class="p">(</span><span class="n">pred_label</span><span class="p">,</span> <span class="o">-</span><span class="bp">self</span><span class="o">.</span><span class="n">top_k</span><span class="p">)</span><span class="o">.</span><span class="n">to_device</span><span class="p">(</span><span class="n">label</span><span class="o">.</span><span class="n">device</span><span class="p">)</span> |
| <span class="n">label</span> <span class="o">=</span> <span class="n">label</span><span class="o">.</span><span class="n">as_np_ndarray</span><span class="p">()</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="s1">'int32'</span><span class="p">)</span> |
| <span class="n">check_label_shapes</span><span class="p">(</span><span class="n">label</span><span class="p">,</span> <span class="n">pred_label</span><span class="p">)</span> |
| <span class="n">num_samples</span> <span class="o">=</span> <span class="n">pred_label</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> |
| <span class="n">num_dims</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">pred_label</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span> |
| <span class="k">if</span> <span class="n">num_dims</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span> |
| <span class="n">num_correct</span> <span class="o">=</span> <span class="p">(</span><span class="n">pred_label</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">)</span> <span class="o">==</span> <span class="n">label</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">))</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">sum_metric</span> <span class="o">+=</span> <span class="n">num_correct</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="s1">'float64'</span><span class="p">)</span> |
| <span class="k">elif</span> <span class="n">num_dims</span> <span class="o">==</span> <span class="mi">2</span><span class="p">:</span> |
| <span class="n">num_classes</span> <span class="o">=</span> <span class="n">pred_label</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> |
| <span class="n">top_k</span> <span class="o">=</span> <span class="nb">min</span><span class="p">(</span><span class="n">num_classes</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">top_k</span><span class="p">)</span> |
| <span class="k">for</span> <span class="n">j</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">top_k</span><span class="p">):</span> |
| <span class="n">num_correct</span> <span class="o">=</span> <span class="p">(</span><span class="n">pred_label</span><span class="p">[:,</span> <span class="n">num_classes</span> <span class="o">-</span> <span class="mi">1</span> <span class="o">-</span> <span class="n">j</span><span class="p">]</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">)</span> <span class="o">==</span> <span class="n">label</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">))</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">sum_metric</span> <span class="o">+=</span> <span class="n">num_correct</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="s1">'float64'</span><span class="p">)</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">num_inst</span> <span class="o">+=</span> <span class="n">num_samples</span></div></div> |
| |
| |
| <div class="viewcode-block" id="predict_with_threshold"><a class="viewcode-back" href="../../../api/gluon/metric/index.html#mxnet.gluon.metric.predict_with_threshold">[docs]</a><span class="k">def</span> <span class="nf">predict_with_threshold</span><span class="p">(</span><span class="n">pred</span><span class="p">,</span> <span class="n">threshold</span><span class="o">=</span><span class="mf">0.5</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""Do thresholding of predictions in binary and multilabel cases.</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> preds : ndarray</span> |
| <span class="sd"> predictions in shape of (batch_size, ...) or (batch_size, ..., num_categories)</span> |
| |
| <span class="sd"> preds : float or ndarray</span> |
| <span class="sd"> threshold（s) in shape of float or (num_categories)</span> |
| <span class="sd"> """</span> |
| <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">threshold</span><span class="p">,</span> <span class="nb">float</span><span class="p">):</span> |
| <span class="k">return</span> <span class="n">pred</span> <span class="o">></span> <span class="n">threshold</span> |
| <span class="k">elif</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">threshold</span><span class="p">,</span> <span class="p">(</span><span class="n">numpy</span><span class="o">.</span><span class="n">ndarray</span><span class="p">,</span> <span class="n">ndarray</span><span class="o">.</span><span class="n">ndarray</span><span class="o">.</span><span class="n">NDArray</span><span class="p">)):</span> |
| <span class="n">num_classes</span> <span class="o">=</span> <span class="n">pred</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span> |
| <span class="k">assert</span> <span class="n">threshold</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span> <span class="o">==</span> <span class="n">num_classes</span><span class="p">,</span> \ |
| <span class="sa">f</span><span class="s2">"shape mismatch: </span><span class="si">{</span><span class="n">pred</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span><span class="si">}</span><span class="s2"> vs. </span><span class="si">{</span><span class="n">threshold</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span><span class="si">}</span><span class="s2">"</span> |
| <span class="k">return</span> <span class="n">pred</span> <span class="o">></span> <span class="n">threshold</span> |
| <span class="k">else</span><span class="p">:</span> |
| <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">"</span><span class="si">{}</span><span class="s2"> is a wrong type for threshold!"</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="nb">type</span><span class="p">(</span><span class="n">threshold</span><span class="p">)))</span></div> |
| |
| |
| <span class="k">def</span> <span class="nf">one_hot</span><span class="p">(</span><span class="n">idx</span><span class="p">,</span> <span class="n">num</span><span class="p">):</span> |
| <span class="k">return</span> <span class="p">(</span><span class="n">numpy</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="n">num</span><span class="p">)</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="n">idx</span><span class="p">)</span> <span class="o">==</span> <span class="n">idx</span><span class="p">[:,</span> <span class="kc">None</span><span class="p">])</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="s1">'int32'</span><span class="p">)</span> |
| |
| |
| <span class="nd">@use_np</span> |
| <span class="k">class</span> <span class="nc">_ClassificationMetrics</span><span class="p">(</span><span class="nb">object</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""Private container class for classification metric statistics.</span> |
| |
| <span class="sd"> True/false positive and true/false negative counts are sufficient statistics for various classification metrics.</span> |
| <span class="sd"> This class provides the machinery to track those statistics across mini-batches of</span> |
| <span class="sd"> (label, prediction) pairs.</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> class_type : str, default "binary"</span> |
| <span class="sd"> "binary": f1 for binary classification.</span> |
| <span class="sd"> "multiclass": f1 for multiclassification problem.</span> |
| <span class="sd"> "multilabel": f1 for multilabel classification.</span> |
| <span class="sd"> beta : float, default 1</span> |
| <span class="sd"> weight of precision in harmonic mean.</span> |
| <span class="sd"> threshold : float, default 0.5</span> |
| <span class="sd"> threshold for deciding whether the predictions are positive or negative.</span> |
| |
| <span class="sd"> """</span> |
| |
| <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">class_type</span><span class="o">=</span><span class="s2">"binary"</span><span class="p">,</span> <span class="n">threshold</span><span class="o">=</span><span class="mf">0.5</span><span class="p">,</span> <span class="n">beta</span><span class="o">=</span><span class="mi">1</span><span class="p">):</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">class_type</span> <span class="o">=</span> <span class="n">class_type</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">threshold</span> <span class="o">=</span> <span class="n">threshold</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">beta</span> <span class="o">=</span> <span class="n">beta</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">reset_stats</span><span class="p">()</span> |
| |
| <span class="k">def</span> <span class="nf">_set</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">num</span><span class="p">,</span> <span class="n">device</span><span class="p">):</span> |
| <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">num_classes</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">num_classes</span> <span class="o">=</span> <span class="n">num</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">true_positives</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="n">num</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="s1">'float64'</span><span class="p">)</span><span class="o">.</span><span class="n">to_device</span><span class="p">(</span><span class="n">device</span><span class="p">)</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">false_negatives</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="n">num</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="s1">'float64'</span><span class="p">)</span><span class="o">.</span><span class="n">to_device</span><span class="p">(</span><span class="n">device</span><span class="p">)</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">false_positives</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="n">num</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="s1">'float64'</span><span class="p">)</span><span class="o">.</span><span class="n">to_device</span><span class="p">(</span><span class="n">device</span><span class="p">)</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">true_negatives</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="n">num</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="s1">'float64'</span><span class="p">)</span><span class="o">.</span><span class="n">to_device</span><span class="p">(</span><span class="n">device</span><span class="p">)</span> |
| <span class="k">else</span><span class="p">:</span> |
| <span class="k">assert</span> <span class="bp">self</span><span class="o">.</span><span class="n">num_classes</span> <span class="o">==</span> <span class="n">num</span><span class="p">,</span> \ |
| <span class="s2">"Input number of classes has changed from </span><span class="si">{}</span><span class="s2"> to </span><span class="si">{}</span><span class="s2">"</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">num_classes</span><span class="p">,</span> <span class="n">num</span><span class="p">)</span> |
| |
| <span class="k">def</span> <span class="nf">update_stats</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">label</span><span class="p">,</span> <span class="n">pred</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""Update various binary classification counts for a single (label, pred) pair.</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> label : `NDArray`</span> |
| <span class="sd"> The labels of the data.</span> |
| |
| <span class="sd"> pred : `NDArray`</span> |
| <span class="sd"> Predicted values.</span> |
| <span class="sd"> """</span> |
| <span class="n">pred</span> <span class="o">=</span> <span class="n">pred</span><span class="o">.</span><span class="n">as_np_ndarray</span><span class="p">()</span><span class="o">.</span><span class="n">to_device</span><span class="p">(</span><span class="n">label</span><span class="o">.</span><span class="n">device</span><span class="p">)</span> |
| <span class="n">label</span> <span class="o">=</span> <span class="n">label</span><span class="o">.</span><span class="n">as_np_ndarray</span><span class="p">()</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="s1">'int32'</span><span class="p">)</span> |
| <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">class_type</span> <span class="o">==</span> <span class="s2">"binary"</span><span class="p">:</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_set</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="n">label</span><span class="o">.</span><span class="n">device</span><span class="p">)</span> |
| <span class="k">if</span> <span class="n">label</span><span class="o">.</span><span class="n">max</span><span class="p">()</span> <span class="o">></span> <span class="mi">1</span><span class="p">:</span> |
| <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">"Wrong label for binary classification."</span><span class="p">)</span> |
| <span class="k">if</span> <span class="n">pred</span><span class="o">.</span><span class="n">shape</span> <span class="o">==</span> <span class="n">label</span><span class="o">.</span><span class="n">shape</span><span class="p">:</span> |
| <span class="k">pass</span> |
| <span class="k">elif</span> <span class="n">pred</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span> <span class="o">></span> <span class="mi">2</span><span class="p">:</span> |
| <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">"The shape of prediction </span><span class="si">{}</span><span class="s2"> is wrong for binary classification."</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">pred</span><span class="o">.</span><span class="n">shape</span><span class="p">))</span> |
| <span class="k">elif</span> <span class="n">pred</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span> <span class="o">==</span> <span class="mi">2</span><span class="p">:</span> |
| <span class="n">pred</span> <span class="o">=</span> <span class="n">pred</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">)[:,</span> <span class="mi">1</span><span class="p">]</span> |
| <span class="n">pred_label</span> <span class="o">=</span> <span class="n">predict_with_threshold</span><span class="p">(</span><span class="n">pred</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">threshold</span><span class="p">)</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">)</span> |
| <span class="n">label</span> <span class="o">=</span> <span class="n">label</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">)</span> |
| |
| <span class="k">elif</span> <span class="bp">self</span><span class="o">.</span><span class="n">class_type</span> <span class="o">==</span> <span class="s2">"multiclass"</span><span class="p">:</span> |
| <span class="n">num</span> <span class="o">=</span> <span class="n">pred</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_set</span><span class="p">(</span><span class="n">num</span><span class="p">,</span> <span class="n">label</span><span class="o">.</span><span class="n">device</span><span class="p">)</span> |
| <span class="k">assert</span> <span class="n">label</span><span class="o">.</span><span class="n">max</span><span class="p">()</span> <span class="o"><</span> <span class="n">num</span><span class="p">,</span> <span class="s2">"pred contains fewer classes than label!"</span> |
| <span class="n">pred_label</span> <span class="o">=</span> <span class="n">one_hot</span><span class="p">(</span><span class="n">pred</span><span class="o">.</span><span class="n">argmax</span><span class="p">(</span><span class="n">axis</span><span class="o">=-</span><span class="mi">1</span><span class="p">)</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">),</span> <span class="n">num</span><span class="p">)</span> |
| <span class="n">label</span> <span class="o">=</span> <span class="n">one_hot</span><span class="p">(</span><span class="n">label</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">),</span> <span class="n">num</span><span class="p">)</span> |
| |
| <span class="k">elif</span> <span class="bp">self</span><span class="o">.</span><span class="n">class_type</span> <span class="o">==</span> <span class="s2">"multilabel"</span><span class="p">:</span> |
| <span class="n">num</span> <span class="o">=</span> <span class="n">pred</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_set</span><span class="p">(</span><span class="n">num</span><span class="p">,</span> <span class="n">label</span><span class="o">.</span><span class="n">device</span><span class="p">)</span> |
| <span class="k">assert</span> <span class="n">pred</span><span class="o">.</span><span class="n">shape</span> <span class="o">==</span> <span class="n">label</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span> \ |
| <span class="s2">"The shape of label should be same as that of prediction for multilabel classification."</span> |
| <span class="n">pred_label</span> <span class="o">=</span> <span class="n">predict_with_threshold</span><span class="p">(</span><span class="n">pred</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">threshold</span><span class="p">)</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="n">num</span><span class="p">)</span> |
| <span class="n">label</span> <span class="o">=</span> <span class="n">label</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="n">num</span><span class="p">)</span> |
| <span class="k">else</span><span class="p">:</span> |
| <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span> |
| <span class="s2">"Wrong class_type </span><span class="si">{}</span><span class="s2">! Only supports ['binary', 'multiclass', 'multilabel']"</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">class_type</span><span class="p">))</span> |
| |
| <span class="n">check_label_shapes</span><span class="p">(</span><span class="n">label</span><span class="p">,</span> <span class="n">pred_label</span><span class="p">)</span> |
| |
| <span class="n">pred_true</span> <span class="o">=</span> <span class="p">(</span><span class="n">pred_label</span> <span class="o">==</span> <span class="mi">1</span><span class="p">)</span> |
| <span class="n">pred_false</span> <span class="o">=</span> <span class="p">(</span><span class="n">pred_label</span> <span class="o">==</span> <span class="mi">0</span><span class="p">)</span> |
| <span class="n">label_true</span> <span class="o">=</span> <span class="p">(</span><span class="n">label</span> <span class="o">==</span> <span class="mi">1</span><span class="p">)</span> |
| <span class="n">label_false</span> <span class="o">=</span> <span class="p">(</span><span class="n">label</span> <span class="o">==</span> <span class="mi">0</span><span class="p">)</span> |
| |
| <span class="n">true_pos</span> <span class="o">=</span> <span class="p">(</span><span class="n">pred_true</span> <span class="o">*</span> <span class="n">label_true</span><span class="p">)</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span> |
| <span class="n">false_pos</span> <span class="o">=</span> <span class="p">(</span><span class="n">pred_true</span> <span class="o">*</span> <span class="n">label_false</span><span class="p">)</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span> |
| <span class="n">false_neg</span> <span class="o">=</span> <span class="p">(</span><span class="n">pred_false</span> <span class="o">*</span> <span class="n">label_true</span><span class="p">)</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span> |
| <span class="n">true_neg</span> <span class="o">=</span> <span class="p">(</span><span class="n">pred_false</span> <span class="o">*</span> <span class="n">label_false</span><span class="p">)</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">true_positives</span> <span class="o">+=</span> <span class="n">true_pos</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">false_positives</span> <span class="o">+=</span> <span class="n">false_pos</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">false_negatives</span> <span class="o">+=</span> <span class="n">false_neg</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">true_negatives</span> <span class="o">+=</span> <span class="n">true_neg</span> |
| |
| <span class="nd">@property</span> |
| <span class="k">def</span> <span class="nf">precision</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span> |
| <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">num_classes</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span> |
| <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">true_positives</span> <span class="o">/</span> <span class="n">numpy</span><span class="o">.</span><span class="n">maximum</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">true_positives</span> <span class="o">+</span> <span class="bp">self</span><span class="o">.</span><span class="n">false_positives</span><span class="p">,</span> <span class="mf">1e-12</span><span class="p">)</span> |
| <span class="k">else</span><span class="p">:</span> |
| <span class="k">return</span> <span class="mf">0.</span> |
| |
| <span class="nd">@property</span> |
| <span class="k">def</span> <span class="nf">micro_precision</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span> |
| <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">num_classes</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span> |
| <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">true_positives</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span> <span class="o">/</span> \ |
| <span class="n">numpy</span><span class="o">.</span><span class="n">maximum</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">true_positives</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span> <span class="o">+</span> <span class="bp">self</span><span class="o">.</span><span class="n">false_positives</span><span class="o">.</span><span class="n">sum</span><span class="p">(),</span> <span class="mf">1e-12</span><span class="p">)</span> |
| <span class="k">else</span><span class="p">:</span> |
| <span class="k">return</span> <span class="mf">0.</span> |
| |
| <span class="nd">@property</span> |
| <span class="k">def</span> <span class="nf">recall</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span> |
| <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">num_classes</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span> |
| <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">true_positives</span> <span class="o">/</span> <span class="n">numpy</span><span class="o">.</span><span class="n">maximum</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">true_positives</span> <span class="o">+</span> <span class="bp">self</span><span class="o">.</span><span class="n">false_negatives</span><span class="p">,</span> <span class="mf">1e-12</span><span class="p">)</span> |
| <span class="k">else</span><span class="p">:</span> |
| <span class="k">return</span> <span class="mf">0.</span> |
| |
| <span class="nd">@property</span> |
| <span class="k">def</span> <span class="nf">micro_recall</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span> |
| <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">num_classes</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span> |
| <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">true_positives</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span> <span class="o">/</span> \ |
| <span class="n">numpy</span><span class="o">.</span><span class="n">maximum</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">true_positives</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span> <span class="o">+</span> <span class="bp">self</span><span class="o">.</span><span class="n">false_negatives</span><span class="o">.</span><span class="n">sum</span><span class="p">(),</span> <span class="mf">1e-12</span><span class="p">)</span> |
| <span class="k">else</span><span class="p">:</span> |
| <span class="k">return</span> <span class="mf">0.</span> |
| |
| <span class="nd">@property</span> |
| <span class="k">def</span> <span class="nf">fscore</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span> |
| <span class="k">return</span> <span class="p">(</span><span class="mi">1</span> <span class="o">+</span> <span class="bp">self</span><span class="o">.</span><span class="n">beta</span> <span class="o">**</span> <span class="mi">2</span><span class="p">)</span> <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">precision</span> <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">recall</span> <span class="o">/</span> \ |
| <span class="n">numpy</span><span class="o">.</span><span class="n">maximum</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">beta</span> <span class="o">**</span> <span class="mi">2</span> <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">precision</span> <span class="o">+</span> <span class="bp">self</span><span class="o">.</span><span class="n">recall</span><span class="p">,</span> <span class="mf">1e-12</span><span class="p">)</span> |
| |
| <span class="nd">@property</span> |
| <span class="k">def</span> <span class="nf">micro_fscore</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span> |
| <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">micro_precision</span> <span class="o">+</span> <span class="bp">self</span><span class="o">.</span><span class="n">micro_recall</span> <span class="o">></span> <span class="mi">0</span><span class="p">:</span> |
| <span class="k">return</span> <span class="p">(</span><span class="mi">1</span> <span class="o">+</span> <span class="bp">self</span><span class="o">.</span><span class="n">beta</span> <span class="o">**</span> <span class="mi">2</span><span class="p">)</span> <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">micro_precision</span> <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">micro_recall</span> <span class="o">/</span> \ |
| <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">beta</span> <span class="o">**</span> <span class="mi">2</span> <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">micro_precision</span> <span class="o">+</span> <span class="bp">self</span><span class="o">.</span><span class="n">micro_recall</span><span class="p">)</span> |
| <span class="k">else</span><span class="p">:</span> |
| <span class="k">return</span> <span class="mf">0.</span> |
| |
| <span class="k">def</span> <span class="nf">binary_matthewscc</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""Calculate the Matthew's Correlation Coefficent"""</span> |
| <span class="k">if</span> <span class="ow">not</span> <span class="bp">self</span><span class="o">.</span><span class="n">total_examples</span><span class="p">:</span> |
| <span class="k">return</span> <span class="mf">0.</span> |
| |
| <span class="n">true_pos</span> <span class="o">=</span> <span class="nb">float</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">true_positives</span><span class="p">)</span> |
| <span class="n">false_pos</span> <span class="o">=</span> <span class="nb">float</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">false_positives</span><span class="p">)</span> |
| <span class="n">false_neg</span> <span class="o">=</span> <span class="nb">float</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">false_negatives</span><span class="p">)</span> |
| <span class="n">true_neg</span> <span class="o">=</span> <span class="nb">float</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">true_negatives</span><span class="p">)</span> |
| |
| <span class="n">terms</span> <span class="o">=</span> <span class="p">[(</span><span class="n">true_pos</span> <span class="o">+</span> <span class="n">false_pos</span><span class="p">),</span> |
| <span class="p">(</span><span class="n">true_pos</span> <span class="o">+</span> <span class="n">false_neg</span><span class="p">),</span> |
| <span class="p">(</span><span class="n">true_neg</span> <span class="o">+</span> <span class="n">false_pos</span><span class="p">),</span> |
| <span class="p">(</span><span class="n">true_neg</span> <span class="o">+</span> <span class="n">false_neg</span><span class="p">)]</span> |
| <span class="n">denom</span> <span class="o">=</span> <span class="mf">1.</span> |
| <span class="k">for</span> <span class="n">t</span> <span class="ow">in</span> <span class="nb">filter</span><span class="p">(</span><span class="k">lambda</span> <span class="n">t</span><span class="p">:</span> <span class="n">t</span> <span class="o">!=</span> <span class="mf">0.</span><span class="p">,</span> <span class="n">terms</span><span class="p">):</span> |
| <span class="n">denom</span> <span class="o">*=</span> <span class="n">t</span> |
| <span class="k">return</span> <span class="p">((</span><span class="n">true_pos</span> <span class="o">*</span> <span class="n">true_neg</span><span class="p">)</span> <span class="o">-</span> <span class="p">(</span><span class="n">false_pos</span> <span class="o">*</span> <span class="n">false_neg</span><span class="p">))</span> <span class="o">/</span> <span class="n">math</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="n">denom</span><span class="p">)</span> |
| |
| <span class="nd">@property</span> |
| <span class="k">def</span> <span class="nf">total_examples</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span> |
| <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">num_classes</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span> |
| <span class="k">return</span> <span class="mi">0</span> |
| <span class="k">return</span> <span class="nb">int</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">false_negatives</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">+</span> <span class="bp">self</span><span class="o">.</span><span class="n">false_positives</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">+</span> \ |
| <span class="bp">self</span><span class="o">.</span><span class="n">true_negatives</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">+</span> <span class="bp">self</span><span class="o">.</span><span class="n">true_positives</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span> |
| |
| <span class="k">def</span> <span class="nf">reset_stats</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">num_classes</span> <span class="o">=</span> <span class="kc">None</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">true_positives</span> <span class="o">=</span> <span class="kc">None</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">false_negatives</span> <span class="o">=</span> <span class="kc">None</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">false_positives</span> <span class="o">=</span> <span class="kc">None</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">true_negatives</span> <span class="o">=</span> <span class="kc">None</span> |
| |
| |
| <div class="viewcode-block" id="F1"><a class="viewcode-back" href="../../../api/gluon/metric/index.html#mxnet.gluon.metric.F1">[docs]</a><span class="nd">@register</span> |
| <span class="nd">@use_np</span> |
| <span class="k">class</span> <span class="nc">F1</span><span class="p">(</span><span class="n">EvalMetric</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""Computes the F1 score of a binary classification problem.</span> |
| |
| <span class="sd"> The F1 score is equivalent to harmonic mean of the precision and recall,</span> |
| <span class="sd"> where the best value is 1.0 and the worst value is 0.0. The formula for F1 score is::</span> |
| |
| <span class="sd"> F1 = 2 * (precision * recall) / (precision + recall)</span> |
| |
| <span class="sd"> The formula for precision and recall is::</span> |
| |
| <span class="sd"> precision = true_positives / (true_positives + false_positives)</span> |
| <span class="sd"> recall = true_positives / (true_positives + false_negatives)</span> |
| |
| <span class="sd"> .. note::</span> |
| |
| <span class="sd"> This F1 score only supports binary classification.</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> name : str</span> |
| <span class="sd"> Name of this metric instance for display.</span> |
| <span class="sd"> output_names : list of str, or None</span> |
| <span class="sd"> Name of predictions that should be used when updating with update_dict.</span> |
| <span class="sd"> By default include all predictions.</span> |
| <span class="sd"> label_names : list of str, or None</span> |
| <span class="sd"> Name of labels that should be used when updating with update_dict.</span> |
| <span class="sd"> By default include all labels.</span> |
| <span class="sd"> class_type : str, default "binary"</span> |
| <span class="sd"> "binary": f1 for binary classification.</span> |
| <span class="sd"> "multiclass": f1 for multiclassification problem.</span> |
| <span class="sd"> "multilabel": f1 for multilabel classification.</span> |
| <span class="sd"> threshold : float, default 0.5</span> |
| <span class="sd"> threshold for postive confidence value.</span> |
| <span class="sd"> average : str, default 'micro'</span> |
| <span class="sd"> Strategy to be used for aggregating across mini-batches.</span> |
| <span class="sd"> "macro": Calculate metrics for each label and return unweighted mean of f1.</span> |
| <span class="sd"> "micro": Calculate metrics globally by counting the total TP, FN and FP.</span> |
| <span class="sd"> None: Return f1 scores for each class (numpy.ndarray) .</span> |
| |
| <span class="sd"> Examples</span> |
| <span class="sd"> --------</span> |
| <span class="sd"> >>> predicts = [mx.np.array([[0.3, 0.7], [0., 1.], [0.4, 0.6]])]</span> |
| <span class="sd"> >>> labels = [mx.np.array([0., 1., 1.])]</span> |
| <span class="sd"> >>> f1 = mx.gluon.metric.F1()</span> |
| <span class="sd"> >>> f1.update(preds = predicts, labels = labels)</span> |
| <span class="sd"> >>> f1.get()</span> |
| <span class="sd"> ('f1', 0.8)</span> |
| <span class="sd"> """</span> |
| |
| <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">name</span><span class="o">=</span><span class="s1">'f1'</span><span class="p">,</span> |
| <span class="n">output_names</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">label_names</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">class_type</span><span class="o">=</span><span class="s2">"binary"</span><span class="p">,</span> <span class="n">threshold</span><span class="o">=</span><span class="mf">0.5</span><span class="p">,</span> <span class="n">average</span><span class="o">=</span><span class="s2">"micro"</span><span class="p">):</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">average</span> <span class="o">=</span> <span class="n">average</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">metrics</span> <span class="o">=</span> <span class="n">_ClassificationMetrics</span><span class="p">(</span><span class="n">class_type</span><span class="o">=</span><span class="n">class_type</span><span class="p">,</span> <span class="n">threshold</span><span class="o">=</span><span class="n">threshold</span><span class="p">)</span> |
| <span class="n">EvalMetric</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">name</span><span class="o">=</span><span class="n">name</span><span class="p">,</span> |
| <span class="n">output_names</span><span class="o">=</span><span class="n">output_names</span><span class="p">,</span> <span class="n">label_names</span><span class="o">=</span><span class="n">label_names</span><span class="p">)</span> |
| |
| <div class="viewcode-block" id="F1.update"><a class="viewcode-back" href="../../../api/gluon/metric/index.html#mxnet.gluon.metric.F1.update">[docs]</a> <span class="k">def</span> <span class="nf">update</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">labels</span><span class="p">,</span> <span class="n">preds</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""Updates the internal evaluation result.</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> labels : list of `NDArray`</span> |
| <span class="sd"> The labels of the data.</span> |
| |
| <span class="sd"> preds : list of `NDArray`</span> |
| <span class="sd"> Predicted values.</span> |
| <span class="sd"> """</span> |
| <span class="n">labels</span><span class="p">,</span> <span class="n">preds</span> <span class="o">=</span> <span class="n">check_label_shapes</span><span class="p">(</span><span class="n">labels</span><span class="p">,</span> <span class="n">preds</span><span class="p">,</span> <span class="kc">True</span><span class="p">)</span> |
| |
| <span class="k">for</span> <span class="n">label</span><span class="p">,</span> <span class="n">pred</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="n">labels</span><span class="p">,</span> <span class="n">preds</span><span class="p">):</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">metrics</span><span class="o">.</span><span class="n">update_stats</span><span class="p">(</span><span class="n">label</span><span class="p">,</span> <span class="n">pred</span><span class="p">)</span> |
| |
| <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">average</span> <span class="o">==</span> <span class="s2">"micro"</span><span class="p">:</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">sum_metric</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">metrics</span><span class="o">.</span><span class="n">micro_fscore</span> <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">metrics</span><span class="o">.</span><span class="n">total_examples</span> |
| <span class="k">elif</span> <span class="bp">self</span><span class="o">.</span><span class="n">average</span> <span class="o">==</span> <span class="s2">"macro"</span><span class="p">:</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">sum_metric</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">metrics</span><span class="o">.</span><span class="n">fscore</span><span class="o">.</span><span class="n">mean</span><span class="p">()</span> <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">metrics</span><span class="o">.</span><span class="n">total_examples</span> |
| <span class="k">else</span><span class="p">:</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">sum_metric</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">metrics</span><span class="o">.</span><span class="n">fscore</span> <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">metrics</span><span class="o">.</span><span class="n">total_examples</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">num_inst</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">metrics</span><span class="o">.</span><span class="n">total_examples</span></div> |
| |
| <div class="viewcode-block" id="F1.reset"><a class="viewcode-back" href="../../../api/gluon/metric/index.html#mxnet.gluon.metric.F1.reset">[docs]</a> <span class="k">def</span> <span class="nf">reset</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""Resets the internal evaluation result to initial state."""</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">sum_metric</span> <span class="o">=</span> <span class="mf">0.</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">num_inst</span> <span class="o">=</span> <span class="mi">0</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">metrics</span><span class="o">.</span><span class="n">reset_stats</span><span class="p">()</span></div></div> |
| |
| |
| <div class="viewcode-block" id="Fbeta"><a class="viewcode-back" href="../../../api/gluon/metric/index.html#mxnet.gluon.metric.Fbeta">[docs]</a><span class="nd">@register</span> |
| <span class="nd">@use_np</span> |
| <span class="k">class</span> <span class="nc">Fbeta</span><span class="p">(</span><span class="n">F1</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""Computes the Fbeta score of a binary classification problem.</span> |
| |
| <span class="sd"> The Fbeta score is equivalent to harmonic mean of the precision and recall,</span> |
| <span class="sd"> where the best value is 1.0 and the worst value is 0.0. The formula for Fbeta score is::</span> |
| |
| <span class="sd"> Fbeta = (1 + beta ** 2) * (precision * recall) / (beta ** 2 * precision + recall)</span> |
| |
| <span class="sd"> The formula for precision and recall is::</span> |
| |
| <span class="sd"> precision = true_positives / (true_positives + false_positives)</span> |
| <span class="sd"> recall = true_positives / (true_positives + false_negatives)</span> |
| |
| <span class="sd"> .. note::</span> |
| |
| <span class="sd"> This Fbeta score only supports binary classification.</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> name : str</span> |
| <span class="sd"> Name of this metric instance for display.</span> |
| <span class="sd"> output_names : list of str, or None</span> |
| <span class="sd"> Name of predictions that should be used when updating with update_dict.</span> |
| <span class="sd"> By default include all predictions.</span> |
| <span class="sd"> label_names : list of str, or None</span> |
| <span class="sd"> Name of labels that should be used when updating with update_dict.</span> |
| <span class="sd"> By default include all labels.</span> |
| <span class="sd"> class_type : str, default "binary"</span> |
| <span class="sd"> "binary": f1 for binary classification.</span> |
| <span class="sd"> "multiclass": f1 for multiclassification problem.</span> |
| <span class="sd"> "multilabel": f1 for multilabel classification.</span> |
| <span class="sd"> beta : float, default 1</span> |
| <span class="sd"> weight of precision in harmonic mean.</span> |
| <span class="sd"> threshold : float, default 0.5</span> |
| <span class="sd"> threshold for postive confidence value.</span> |
| <span class="sd"> average : str, default 'micro'</span> |
| <span class="sd"> Strategy to be used for aggregating across mini-batches.</span> |
| <span class="sd"> "macro": Calculate metrics for each label and return unweighted mean of f1.</span> |
| <span class="sd"> "micro": Calculate metrics globally by counting the total TP, FN and FP.</span> |
| <span class="sd"> None: Return f1 scores for each class.</span> |
| |
| <span class="sd"> Examples</span> |
| <span class="sd"> --------</span> |
| <span class="sd"> >>> predicts = [mx.np.array([[0.3, 0.7], [0., 1.], [0.4, 0.6]])]</span> |
| <span class="sd"> >>> labels = [mx.np.array([0., 1., 1.])]</span> |
| <span class="sd"> >>> fbeta = mx.gluon.metric.Fbeta(beta=2)</span> |
| <span class="sd"> >>> fbeta.update(preds = predicts, labels = labels)</span> |
| <span class="sd"> >>> fbeta.get()</span> |
| <span class="sd"> ('fbeta', 0.9090909090909091)</span> |
| <span class="sd"> """</span> |
| |
| <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">name</span><span class="o">=</span><span class="s1">'fbeta'</span><span class="p">,</span> |
| <span class="n">output_names</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">label_names</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">class_type</span><span class="o">=</span><span class="s2">"binary"</span><span class="p">,</span> <span class="n">beta</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">threshold</span><span class="o">=</span><span class="mf">0.5</span><span class="p">,</span> <span class="n">average</span><span class="o">=</span><span class="s2">"micro"</span><span class="p">):</span> |
| <span class="nb">super</span><span class="p">(</span><span class="n">Fbeta</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span> |
| <span class="n">name</span><span class="o">=</span><span class="n">name</span><span class="p">,</span> <span class="n">output_names</span><span class="o">=</span><span class="n">output_names</span><span class="p">,</span> <span class="n">label_names</span><span class="o">=</span><span class="n">label_names</span><span class="p">,</span> |
| <span class="n">class_type</span><span class="o">=</span><span class="n">class_type</span><span class="p">,</span> <span class="n">threshold</span><span class="o">=</span><span class="n">threshold</span><span class="p">,</span> <span class="n">average</span><span class="o">=</span><span class="n">average</span><span class="p">)</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">metrics</span> <span class="o">=</span> <span class="n">_ClassificationMetrics</span><span class="p">(</span><span class="n">class_type</span><span class="o">=</span><span class="n">class_type</span><span class="p">,</span> <span class="n">threshold</span><span class="o">=</span><span class="n">threshold</span><span class="p">,</span> <span class="n">beta</span><span class="o">=</span><span class="n">beta</span><span class="p">)</span></div> |
| |
| |
| <div class="viewcode-block" id="BinaryAccuracy"><a class="viewcode-back" href="../../../api/gluon/metric/index.html#mxnet.gluon.metric.BinaryAccuracy">[docs]</a><span class="nd">@register</span> |
| <span class="nd">@use_np</span> |
| <span class="k">class</span> <span class="nc">BinaryAccuracy</span><span class="p">(</span><span class="n">EvalMetric</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""Computes the accuracy of a binary or multilabel classification problem.</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> name : str</span> |
| <span class="sd"> Name of this metric instance for display.</span> |
| <span class="sd"> output_names : list of str, or None</span> |
| <span class="sd"> Name of predictions that should be used when updating with update_dict.</span> |
| <span class="sd"> By default include all predictions.</span> |
| <span class="sd"> label_names : list of str, or None</span> |
| <span class="sd"> Name of labels that should be used when updating with update_dict.</span> |
| <span class="sd"> By default include all labels.</span> |
| <span class="sd"> threshold : float or ndarray, default 0.5</span> |
| <span class="sd"> threshold for deciding whether the predictions are positive or negative.</span> |
| |
| <span class="sd"> Examples</span> |
| <span class="sd"> --------</span> |
| <span class="sd"> >>> predicts = [mx.np.array([0.7, 1, 0.55])]</span> |
| <span class="sd"> >>> labels = [mx.np.array([0., 1., 0.])]</span> |
| <span class="sd"> >>> bacc = mx.gluon.metric.BinaryAccuracy(threshold=0.6)</span> |
| <span class="sd"> >>> bacc.update(preds = predicts, labels = labels)</span> |
| <span class="sd"> >>> bacc.get()</span> |
| <span class="sd"> ('binary_accuracy', 0.6666666666666666)</span> |
| <span class="sd"> """</span> |
| |
| <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">name</span><span class="o">=</span><span class="s1">'binary_accuracy'</span><span class="p">,</span> |
| <span class="n">output_names</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">label_names</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">threshold</span><span class="o">=</span><span class="mf">0.5</span><span class="p">):</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">threshold</span> <span class="o">=</span> <span class="n">threshold</span> |
| <span class="n">EvalMetric</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">name</span><span class="o">=</span><span class="n">name</span><span class="p">,</span> |
| <span class="n">output_names</span><span class="o">=</span><span class="n">output_names</span><span class="p">,</span> <span class="n">label_names</span><span class="o">=</span><span class="n">label_names</span><span class="p">)</span> |
| |
| <div class="viewcode-block" id="BinaryAccuracy.update"><a class="viewcode-back" href="../../../api/gluon/metric/index.html#mxnet.gluon.metric.BinaryAccuracy.update">[docs]</a> <span class="k">def</span> <span class="nf">update</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">labels</span><span class="p">,</span> <span class="n">preds</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""Updates the internal evaluation result.</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> labels : list of `NDArray`</span> |
| <span class="sd"> Each label denotes positive/negative for each class.</span> |
| |
| <span class="sd"> preds : list of `NDArray`</span> |
| <span class="sd"> Each prediction value is a confidence value of being positive for each class.</span> |
| <span class="sd"> """</span> |
| <span class="n">labels</span><span class="p">,</span> <span class="n">preds</span> <span class="o">=</span> <span class="n">check_label_shapes</span><span class="p">(</span><span class="n">labels</span><span class="p">,</span> <span class="n">preds</span><span class="p">,</span> <span class="kc">True</span><span class="p">)</span> |
| |
| <span class="k">for</span> <span class="n">label</span><span class="p">,</span> <span class="n">pred_label</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="n">labels</span><span class="p">,</span> <span class="n">preds</span><span class="p">):</span> |
| <span class="n">pred_label</span> <span class="o">=</span> <span class="n">predict_with_threshold</span><span class="p">(</span><span class="n">pred_label</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">threshold</span><span class="p">)</span> |
| |
| <span class="n">pred_label</span> <span class="o">=</span> <span class="n">pred_label</span><span class="o">.</span><span class="n">as_np_ndarray</span><span class="p">()</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="s1">'int32'</span><span class="p">)</span><span class="o">.</span><span class="n">to_device</span><span class="p">(</span><span class="n">label</span><span class="o">.</span><span class="n">device</span><span class="p">)</span> |
| <span class="n">label</span> <span class="o">=</span> <span class="n">label</span><span class="o">.</span><span class="n">as_np_ndarray</span><span class="p">()</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="s1">'int32'</span><span class="p">)</span> |
| <span class="c1"># flatten before checking shapes to avoid shape miss match</span> |
| <span class="n">label</span> <span class="o">=</span> <span class="n">label</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">)</span> |
| <span class="n">pred_label</span> <span class="o">=</span> <span class="n">pred_label</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">)</span> |
| |
| <span class="n">check_label_shapes</span><span class="p">(</span><span class="n">label</span><span class="p">,</span> <span class="n">pred_label</span><span class="p">)</span> |
| |
| <span class="n">num_correct</span> <span class="o">=</span> <span class="p">(</span><span class="n">pred_label</span> <span class="o">==</span> <span class="n">label</span><span class="p">)</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="s1">'float64'</span><span class="p">)</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">sum_metric</span> <span class="o">+=</span> <span class="n">num_correct</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">num_inst</span> <span class="o">+=</span> <span class="nb">len</span><span class="p">(</span><span class="n">pred_label</span><span class="p">)</span></div></div> |
| |
| |
| <div class="viewcode-block" id="MCC"><a class="viewcode-back" href="../../../api/gluon/metric/index.html#mxnet.gluon.metric.MCC">[docs]</a><span class="nd">@register</span> |
| <span class="nd">@use_np</span> |
| <span class="k">class</span> <span class="nc">MCC</span><span class="p">(</span><span class="n">EvalMetric</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""Computes the Matthews Correlation Coefficient of a binary classification problem.</span> |
| |
| <span class="sd"> While slower to compute than F1 the MCC can give insight that F1 or Accuracy cannot.</span> |
| <span class="sd"> For instance, if the network always predicts the same result</span> |
| <span class="sd"> then the MCC will immeadiately show this. The MCC is also symetric with respect</span> |
| <span class="sd"> to positive and negative categorization, however, there needs to be both</span> |
| <span class="sd"> positive and negative examples in the labels or it will always return 0.</span> |
| <span class="sd"> MCC of 0 is uncorrelated, 1 is completely correlated, and -1 is negatively correlated.</span> |
| |
| <span class="sd"> .. math::</span> |
| |
| <span class="sd"> \\text{MCC} = \\frac{ TP \\times TN - FP \\times FN }</span> |
| <span class="sd"> {\\sqrt{ (TP + FP) ( TP + FN ) ( TN + FP ) ( TN + FN ) } }</span> |
| |
| <span class="sd"> where 0 terms in the denominator are replaced by 1.</span> |
| |
| <span class="sd"> .. note::</span> |
| |
| <span class="sd"> This version of MCC only supports binary classification. See PCC.</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> name : str</span> |
| <span class="sd"> Name of this metric instance for display.</span> |
| <span class="sd"> output_names : list of str, or None</span> |
| <span class="sd"> Name of predictions that should be used when updating with update_dict.</span> |
| <span class="sd"> By default include all predictions.</span> |
| <span class="sd"> label_names : list of str, or None</span> |
| <span class="sd"> Name of labels that should be used when updating with update_dict.</span> |
| <span class="sd"> By default include all labels.</span> |
| |
| <span class="sd"> Examples</span> |
| <span class="sd"> --------</span> |
| <span class="sd"> >>> # In this example the network almost always predicts positive</span> |
| <span class="sd"> >>> false_positives = 1000</span> |
| <span class="sd"> >>> false_negatives = 1</span> |
| <span class="sd"> >>> true_positives = 10000</span> |
| <span class="sd"> >>> true_negatives = 1</span> |
| <span class="sd"> >>> predicts = [mx.np.array(</span> |
| <span class="sd"> [[.3, .7]]*false_positives +</span> |
| <span class="sd"> [[.7, .3]]*true_negatives +</span> |
| <span class="sd"> [[.7, .3]]*false_negatives +</span> |
| <span class="sd"> [[.3, .7]]*true_positives</span> |
| <span class="sd"> )]</span> |
| <span class="sd"> >>> labels = [mx.np.array(</span> |
| <span class="sd"> [0.]*(false_positives + true_negatives) +</span> |
| <span class="sd"> [1.]*(false_negatives + true_positives)</span> |
| <span class="sd"> )]</span> |
| <span class="sd"> >>> f1 = mx.gluon.metric.F1()</span> |
| <span class="sd"> >>> f1.update(preds = predicts, labels = labels)</span> |
| <span class="sd"> >>> mcc = mx.gluon.metric.MCC()</span> |
| <span class="sd"> >>> mcc.update(preds = predicts, labels = labels)</span> |
| <span class="sd"> >>> f1.get()</span> |
| <span class="sd"> ('f1', 0.95233560306652054)</span> |
| <span class="sd"> >>> mcc.get()</span> |
| <span class="sd"> ('mcc', 0.01917751877733392)</span> |
| <span class="sd"> """</span> |
| |
| <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">name</span><span class="o">=</span><span class="s1">'mcc'</span><span class="p">,</span> |
| <span class="n">output_names</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">label_names</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_metrics</span> <span class="o">=</span> <span class="n">_ClassificationMetrics</span><span class="p">()</span> |
| <span class="n">EvalMetric</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">name</span><span class="o">=</span><span class="n">name</span><span class="p">,</span> |
| <span class="n">output_names</span><span class="o">=</span><span class="n">output_names</span><span class="p">,</span> <span class="n">label_names</span><span class="o">=</span><span class="n">label_names</span><span class="p">)</span> |
| |
| <div class="viewcode-block" id="MCC.update"><a class="viewcode-back" href="../../../api/gluon/metric/index.html#mxnet.gluon.metric.MCC.update">[docs]</a> <span class="k">def</span> <span class="nf">update</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">labels</span><span class="p">,</span> <span class="n">preds</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""Updates the internal evaluation result.</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> labels : list of `NDArray`</span> |
| <span class="sd"> The labels of the data.</span> |
| |
| <span class="sd"> preds : list of `NDArray`</span> |
| <span class="sd"> Predicted values.</span> |
| <span class="sd"> """</span> |
| <span class="n">labels</span><span class="p">,</span> <span class="n">preds</span> <span class="o">=</span> <span class="n">check_label_shapes</span><span class="p">(</span><span class="n">labels</span><span class="p">,</span> <span class="n">preds</span><span class="p">,</span> <span class="kc">True</span><span class="p">)</span> |
| |
| <span class="k">for</span> <span class="n">label</span><span class="p">,</span> <span class="n">pred</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="n">labels</span><span class="p">,</span> <span class="n">preds</span><span class="p">):</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_metrics</span><span class="o">.</span><span class="n">update_stats</span><span class="p">(</span><span class="n">label</span><span class="p">,</span> <span class="n">pred</span><span class="p">)</span> |
| |
| <span class="bp">self</span><span class="o">.</span><span class="n">sum_metric</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_metrics</span><span class="o">.</span><span class="n">binary_matthewscc</span><span class="p">()</span> <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">_metrics</span><span class="o">.</span><span class="n">total_examples</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">num_inst</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_metrics</span><span class="o">.</span><span class="n">total_examples</span></div> |
| |
| <div class="viewcode-block" id="MCC.reset"><a class="viewcode-back" href="../../../api/gluon/metric/index.html#mxnet.gluon.metric.MCC.reset">[docs]</a> <span class="k">def</span> <span class="nf">reset</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""Resets the internal evaluation result to initial state."""</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">sum_metric</span> <span class="o">=</span> <span class="mf">0.</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">num_inst</span> <span class="o">=</span> <span class="mf">0.</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_metrics</span><span class="o">.</span><span class="n">reset_stats</span><span class="p">()</span></div></div> |
| |
| |
| <span class="c1">####################</span> |
| <span class="c1"># REGRESSION METRICS</span> |
| <span class="c1">####################</span> |
| |
| |
| <div class="viewcode-block" id="MAE"><a class="viewcode-back" href="../../../api/gluon/metric/index.html#mxnet.gluon.metric.MAE">[docs]</a><span class="nd">@register</span> |
| <span class="nd">@use_np</span> |
| <span class="k">class</span> <span class="nc">MAE</span><span class="p">(</span><span class="n">EvalMetric</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""Computes Mean Absolute Error (MAE) loss.</span> |
| |
| <span class="sd"> The mean absolute error is given by</span> |
| |
| <span class="sd"> .. math::</span> |
| |
| <span class="sd"> \\frac{\\sum_i^n |y_i - \\hat{y}_i|}{n}</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> name : str</span> |
| <span class="sd"> Name of this metric instance for display.</span> |
| <span class="sd"> output_names : list of str, or None</span> |
| <span class="sd"> Name of predictions that should be used when updating with update_dict.</span> |
| <span class="sd"> By default include all predictions.</span> |
| <span class="sd"> label_names : list of str, or None</span> |
| <span class="sd"> Name of labels that should be used when updating with update_dict.</span> |
| <span class="sd"> By default include all labels.</span> |
| |
| <span class="sd"> Examples</span> |
| <span class="sd"> --------</span> |
| <span class="sd"> >>> predicts = [mx.np.array([3, -0.5, 2, 7])]</span> |
| <span class="sd"> >>> labels = [mx.np.array([2.5, 0.0, 2, 8])]</span> |
| <span class="sd"> >>> mean_absolute_error = mx.gluon.metric.MAE()</span> |
| <span class="sd"> >>> mean_absolute_error.update(labels = labels, preds = predicts)</span> |
| <span class="sd"> >>> mean_absolute_error.get()</span> |
| <span class="sd"> ('mae', 0.5)</span> |
| <span class="sd"> """</span> |
| |
| <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">name</span><span class="o">=</span><span class="s1">'mae'</span><span class="p">,</span> |
| <span class="n">output_names</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">label_names</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> |
| <span class="nb">super</span><span class="p">(</span><span class="n">MAE</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span> |
| <span class="n">name</span><span class="p">,</span> <span class="n">output_names</span><span class="o">=</span><span class="n">output_names</span><span class="p">,</span> <span class="n">label_names</span><span class="o">=</span><span class="n">label_names</span><span class="p">)</span> |
| |
| <div class="viewcode-block" id="MAE.update"><a class="viewcode-back" href="../../../api/gluon/metric/index.html#mxnet.gluon.metric.MAE.update">[docs]</a> <span class="k">def</span> <span class="nf">update</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">labels</span><span class="p">,</span> <span class="n">preds</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""Updates the internal evaluation result.</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> labels : list of `NDArray`</span> |
| <span class="sd"> The labels of the data.</span> |
| |
| <span class="sd"> preds : list of `NDArray`</span> |
| <span class="sd"> Predicted values.</span> |
| <span class="sd"> """</span> |
| <span class="n">labels</span><span class="p">,</span> <span class="n">preds</span> <span class="o">=</span> <span class="n">check_label_shapes</span><span class="p">(</span><span class="n">labels</span><span class="p">,</span> <span class="n">preds</span><span class="p">,</span> <span class="kc">True</span><span class="p">)</span> |
| |
| <span class="k">for</span> <span class="n">label</span><span class="p">,</span> <span class="n">pred</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="n">labels</span><span class="p">,</span> <span class="n">preds</span><span class="p">):</span> |
| <span class="n">label</span> <span class="o">=</span> <span class="n">label</span><span class="o">.</span><span class="n">as_np_ndarray</span><span class="p">()</span> |
| <span class="n">pred</span> <span class="o">=</span> <span class="n">pred</span><span class="o">.</span><span class="n">as_np_ndarray</span><span class="p">()</span><span class="o">.</span><span class="n">to_device</span><span class="p">(</span><span class="n">label</span><span class="o">.</span><span class="n">device</span><span class="p">)</span> |
| |
| <span class="n">num_inst</span> <span class="o">=</span> <span class="n">label</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> |
| <span class="n">mae</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">abs</span><span class="p">(</span><span class="n">label</span> <span class="o">-</span> <span class="n">pred</span><span class="p">)</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="n">num_inst</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">)</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">axis</span><span class="o">=-</span><span class="mi">1</span><span class="p">)</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span> |
| |
| <span class="bp">self</span><span class="o">.</span><span class="n">sum_metric</span> <span class="o">+=</span> <span class="n">mae</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">num_inst</span> <span class="o">+=</span> <span class="n">num_inst</span></div></div> |
| |
| |
| <div class="viewcode-block" id="MSE"><a class="viewcode-back" href="../../../api/gluon/metric/index.html#mxnet.gluon.metric.MSE">[docs]</a><span class="nd">@register</span> |
| <span class="nd">@use_np</span> |
| <span class="k">class</span> <span class="nc">MSE</span><span class="p">(</span><span class="n">EvalMetric</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""Computes Mean Squared Error (MSE) loss.</span> |
| |
| <span class="sd"> The mean squared error is given by</span> |
| |
| <span class="sd"> .. math::</span> |
| |
| <span class="sd"> \\frac{\\sum_i^n (y_i - \\hat{y}_i)^2}{n}</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> name : str</span> |
| <span class="sd"> Name of this metric instance for display.</span> |
| <span class="sd"> output_names : list of str, or None</span> |
| <span class="sd"> Name of predictions that should be used when updating with update_dict.</span> |
| <span class="sd"> By default include all predictions.</span> |
| <span class="sd"> label_names : list of str, or None</span> |
| <span class="sd"> Name of labels that should be used when updating with update_dict.</span> |
| <span class="sd"> By default include all labels.</span> |
| |
| <span class="sd"> Examples</span> |
| <span class="sd"> --------</span> |
| <span class="sd"> >>> predicts = [mx.np.array([3, -0.5, 2, 7])]</span> |
| <span class="sd"> >>> labels = [mx.np.array([2.5, 0.0, 2, 8])]</span> |
| <span class="sd"> >>> mean_squared_error = mx.gluon.metric.MSE()</span> |
| <span class="sd"> >>> mean_squared_error.update(labels = labels, preds = predicts)</span> |
| <span class="sd"> >>> mean_squared_error.get()</span> |
| <span class="sd"> ('mse', 0.375)</span> |
| <span class="sd"> """</span> |
| <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">name</span><span class="o">=</span><span class="s1">'mse'</span><span class="p">,</span> |
| <span class="n">output_names</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">label_names</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> |
| <span class="nb">super</span><span class="p">(</span><span class="n">MSE</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span> |
| <span class="n">name</span><span class="p">,</span> <span class="n">output_names</span><span class="o">=</span><span class="n">output_names</span><span class="p">,</span> <span class="n">label_names</span><span class="o">=</span><span class="n">label_names</span><span class="p">)</span> |
| |
| <div class="viewcode-block" id="MSE.update"><a class="viewcode-back" href="../../../api/gluon/metric/index.html#mxnet.gluon.metric.MSE.update">[docs]</a> <span class="k">def</span> <span class="nf">update</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">labels</span><span class="p">,</span> <span class="n">preds</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""Updates the internal evaluation result.</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> labels : list of `NDArray`</span> |
| <span class="sd"> The labels of the data.</span> |
| |
| <span class="sd"> preds : list of `NDArray`</span> |
| <span class="sd"> Predicted values.</span> |
| <span class="sd"> """</span> |
| <span class="n">labels</span><span class="p">,</span> <span class="n">preds</span> <span class="o">=</span> <span class="n">check_label_shapes</span><span class="p">(</span><span class="n">labels</span><span class="p">,</span> <span class="n">preds</span><span class="p">,</span> <span class="kc">True</span><span class="p">)</span> |
| |
| <span class="k">for</span> <span class="n">label</span><span class="p">,</span> <span class="n">pred</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="n">labels</span><span class="p">,</span> <span class="n">preds</span><span class="p">):</span> |
| <span class="n">label</span> <span class="o">=</span> <span class="n">label</span><span class="o">.</span><span class="n">as_np_ndarray</span><span class="p">()</span> |
| <span class="n">pred</span> <span class="o">=</span> <span class="n">pred</span><span class="o">.</span><span class="n">as_np_ndarray</span><span class="p">()</span><span class="o">.</span><span class="n">to_device</span><span class="p">(</span><span class="n">label</span><span class="o">.</span><span class="n">device</span><span class="p">)</span> |
| |
| <span class="n">num_inst</span> <span class="o">=</span> <span class="n">label</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> |
| <span class="n">mse</span> <span class="o">=</span> <span class="p">((</span><span class="n">label</span> <span class="o">-</span> <span class="n">pred</span><span class="p">)</span><span class="o">**</span><span class="mf">2.0</span><span class="p">)</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="n">num_inst</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">)</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">axis</span><span class="o">=-</span><span class="mi">1</span><span class="p">)</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span> |
| |
| <span class="bp">self</span><span class="o">.</span><span class="n">sum_metric</span> <span class="o">+=</span> <span class="n">mse</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">num_inst</span> <span class="o">+=</span> <span class="n">num_inst</span></div></div> |
| |
| |
| <div class="viewcode-block" id="RMSE"><a class="viewcode-back" href="../../../api/gluon/metric/index.html#mxnet.gluon.metric.RMSE">[docs]</a><span class="nd">@register</span> |
| <span class="nd">@use_np</span> |
| <span class="k">class</span> <span class="nc">RMSE</span><span class="p">(</span><span class="n">MSE</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""Computes Root Mean Squred Error (RMSE) loss.</span> |
| |
| <span class="sd"> The root mean squared error is given by</span> |
| |
| <span class="sd"> .. math::</span> |
| |
| <span class="sd"> \\sqrt{\\frac{\\sum_i^n (y_i - \\hat{y}_i)^2}{n}}</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> name : str</span> |
| <span class="sd"> Name of this metric instance for display.</span> |
| <span class="sd"> output_names : list of str, or None</span> |
| <span class="sd"> Name of predictions that should be used when updating with update_dict.</span> |
| <span class="sd"> By default include all predictions.</span> |
| <span class="sd"> label_names : list of str, or None</span> |
| <span class="sd"> Name of labels that should be used when updating with update_dict.</span> |
| <span class="sd"> By default include all labels.</span> |
| |
| <span class="sd"> Examples</span> |
| <span class="sd"> --------</span> |
| <span class="sd"> >>> predicts = [mx.np.array([3, -0.5, 2, 7])]</span> |
| <span class="sd"> >>> labels = [mx.np.array([2.5, 0.0, 2, 8])]</span> |
| <span class="sd"> >>> root_mean_squared_error = mx.gluon.metric.RMSE()</span> |
| <span class="sd"> >>> root_mean_squared_error.update(labels = labels, preds = predicts)</span> |
| <span class="sd"> >>> root_mean_squared_error.get()</span> |
| <span class="sd"> ('rmse', 0.612372457981)</span> |
| <span class="sd"> """</span> |
| <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">name</span><span class="o">=</span><span class="s1">'rmse'</span><span class="p">,</span> |
| <span class="n">output_names</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">label_names</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> |
| <span class="nb">super</span><span class="p">(</span><span class="n">RMSE</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span> |
| <span class="n">name</span><span class="p">,</span> <span class="n">output_names</span><span class="o">=</span><span class="n">output_names</span><span class="p">,</span> <span class="n">label_names</span><span class="o">=</span><span class="n">label_names</span><span class="p">)</span> |
| |
| <div class="viewcode-block" id="RMSE.get"><a class="viewcode-back" href="../../../api/gluon/metric/index.html#mxnet.gluon.metric.RMSE.get">[docs]</a> <span class="k">def</span> <span class="nf">get</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span> |
| <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">num_inst</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span> |
| <span class="k">return</span> <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">name</span><span class="p">,</span> <span class="nb">float</span><span class="p">(</span><span class="s1">'nan'</span><span class="p">))</span> |
| <span class="k">else</span><span class="p">:</span> |
| <span class="k">return</span> <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">name</span><span class="p">,</span> <span class="n">math</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">sum_metric</span> <span class="o">/</span> <span class="bp">self</span><span class="o">.</span><span class="n">num_inst</span><span class="p">))</span></div></div> |
| |
| |
| <div class="viewcode-block" id="MeanPairwiseDistance"><a class="viewcode-back" href="../../../api/gluon/metric/index.html#mxnet.gluon.metric.MeanPairwiseDistance">[docs]</a><span class="nd">@register</span> |
| <span class="nd">@use_np</span> |
| <span class="k">class</span> <span class="nc">MeanPairwiseDistance</span><span class="p">(</span><span class="n">EvalMetric</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""Computes Mean Pairwise Distance.</span> |
| |
| <span class="sd"> The mean pairwise distance is given by</span> |
| |
| <span class="sd"> .. math::</span> |
| |
| <span class="sd"> \\sqrt{\\frac{(\\sum_i^n (y_i - \\hat{y}_i)^p)^\\frac{1}{p}}{n}}</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> name : str</span> |
| <span class="sd"> Name of this metric instance for display.</span> |
| <span class="sd"> output_names : list of str, or None</span> |
| <span class="sd"> Name of predictions that should be used when updating with update_dict.</span> |
| <span class="sd"> By default include all predictions.</span> |
| <span class="sd"> label_names : list of str, or None</span> |
| <span class="sd"> Name of labels that should be used when updating with update_dict.</span> |
| <span class="sd"> By default include all labels.</span> |
| <span class="sd"> p : float, default 2</span> |
| <span class="sd"> calculating distance using the p-norm</span> |
| |
| <span class="sd"> Examples</span> |
| <span class="sd"> --------</span> |
| <span class="sd"> >>> predicts = [mx.np.array([[1., 2.], [3., 4.]])]</span> |
| <span class="sd"> >>> labels = [mx.np.array([[1., 0.], [4., 2.]])]</span> |
| <span class="sd"> >>> mpd = mx.gluon.metric.MeanPairwiseDistance()</span> |
| <span class="sd"> >>> mpd.update(labels = labels, preds = predicts)</span> |
| <span class="sd"> >>> mpd.get()</span> |
| <span class="sd"> ('mpd', 2.1180338859558105)</span> |
| <span class="sd"> """</span> |
| <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">name</span><span class="o">=</span><span class="s1">'mpd'</span><span class="p">,</span> |
| <span class="n">output_names</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">label_names</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">p</span><span class="o">=</span><span class="mi">2</span><span class="p">):</span> |
| <span class="nb">super</span><span class="p">(</span><span class="n">MeanPairwiseDistance</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span> |
| <span class="n">name</span><span class="p">,</span> <span class="n">output_names</span><span class="o">=</span><span class="n">output_names</span><span class="p">,</span> <span class="n">label_names</span><span class="o">=</span><span class="n">label_names</span><span class="p">)</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">p</span> <span class="o">=</span> <span class="n">p</span> |
| |
| <div class="viewcode-block" id="MeanPairwiseDistance.update"><a class="viewcode-back" href="../../../api/gluon/metric/index.html#mxnet.gluon.metric.MeanPairwiseDistance.update">[docs]</a> <span class="k">def</span> <span class="nf">update</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">labels</span><span class="p">,</span> <span class="n">preds</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""Updates the internal evaluation result.</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> labels : list of `NDArray`</span> |
| <span class="sd"> The labels of the data.</span> |
| |
| <span class="sd"> preds : list of `NDArray`</span> |
| <span class="sd"> Predicted values.</span> |
| <span class="sd"> """</span> |
| <span class="n">labels</span><span class="p">,</span> <span class="n">preds</span> <span class="o">=</span> <span class="n">check_label_shapes</span><span class="p">(</span><span class="n">labels</span><span class="p">,</span> <span class="n">preds</span><span class="p">,</span> <span class="kc">True</span><span class="p">)</span> |
| |
| <span class="k">for</span> <span class="n">label</span><span class="p">,</span> <span class="n">pred</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="n">labels</span><span class="p">,</span> <span class="n">preds</span><span class="p">):</span> |
| <span class="n">label</span> <span class="o">=</span> <span class="n">label</span><span class="o">.</span><span class="n">as_np_ndarray</span><span class="p">()</span> |
| <span class="n">pred</span> <span class="o">=</span> <span class="n">pred</span><span class="o">.</span><span class="n">as_np_ndarray</span><span class="p">()</span><span class="o">.</span><span class="n">to_device</span><span class="p">(</span><span class="n">label</span><span class="o">.</span><span class="n">device</span><span class="p">)</span> |
| |
| <span class="n">label</span> <span class="o">=</span> <span class="n">label</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="n">label</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="o">-</span><span class="mi">1</span><span class="p">)</span> |
| <span class="n">pred</span> <span class="o">=</span> <span class="n">pred</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="n">pred</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="o">-</span><span class="mi">1</span><span class="p">)</span> |
| |
| <span class="n">dis</span> <span class="o">=</span> <span class="p">(((</span><span class="n">label</span> <span class="o">-</span> <span class="n">pred</span><span class="p">)</span> <span class="o">**</span> <span class="bp">self</span><span class="o">.</span><span class="n">p</span><span class="p">)</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">axis</span><span class="o">=-</span><span class="mi">1</span><span class="p">))</span> <span class="o">**</span> <span class="p">(</span><span class="mf">1.</span><span class="o">/</span><span class="bp">self</span><span class="o">.</span><span class="n">p</span><span class="p">)</span> |
| <span class="n">dis</span> <span class="o">=</span> <span class="n">dis</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span> |
| <span class="n">num_inst</span> <span class="o">=</span> <span class="n">label</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> |
| |
| <span class="bp">self</span><span class="o">.</span><span class="n">sum_metric</span> <span class="o">+=</span> <span class="n">dis</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">num_inst</span> <span class="o">+=</span> <span class="n">num_inst</span></div></div> |
| |
| |
| <div class="viewcode-block" id="MeanCosineSimilarity"><a class="viewcode-back" href="../../../api/gluon/metric/index.html#mxnet.gluon.metric.MeanCosineSimilarity">[docs]</a><span class="nd">@register</span> |
| <span class="nd">@use_np</span> |
| <span class="k">class</span> <span class="nc">MeanCosineSimilarity</span><span class="p">(</span><span class="n">EvalMetric</span><span class="p">):</span> |
| <span class="w"> </span><span class="sa">r</span><span class="sd">"""Computes Mean Cosine Similarity.</span> |
| |
| <span class="sd"> The mean cosine similarity is given by</span> |
| |
| <span class="sd"> .. math::</span> |
| |
| <span class="sd"> cos_sim(label, pred) = \frac{{label}.{pred}}{max(||label||.||pred||, eps)}</span> |
| |
| <span class="sd"> Calculation happens on the last dimension of label and pred.</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> name : str</span> |
| <span class="sd"> Name of this metric instance for display.</span> |
| <span class="sd"> output_names : list of str, or None</span> |
| <span class="sd"> Name of predictions that should be used when updating with update_dict.</span> |
| <span class="sd"> By default include all predictions.</span> |
| <span class="sd"> label_names : list of str, or None</span> |
| <span class="sd"> Name of labels that should be used when updating with update_dict.</span> |
| <span class="sd"> By default include all labels.</span> |
| <span class="sd"> eps : float, default 1e-8</span> |
| <span class="sd"> small vale to avoid division by zero.</span> |
| |
| <span class="sd"> Examples</span> |
| <span class="sd"> --------</span> |
| <span class="sd"> >>> predicts = [mx.np.array([[1., 0.], [1., 1.]])]</span> |
| <span class="sd"> >>> labels = [mx.np.array([[3., 4.], [2., 2.]])]</span> |
| <span class="sd"> >>> mcs = mx.gluon.metric.MeanCosineSimilarity()</span> |
| <span class="sd"> >>> mcs.update(labels = labels, preds = predicts)</span> |
| <span class="sd"> >>> mcs.get()</span> |
| <span class="sd"> ('cos_sim', 0.8)</span> |
| <span class="sd"> """</span> |
| <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">name</span><span class="o">=</span><span class="s1">'cos_sim'</span><span class="p">,</span> |
| <span class="n">output_names</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">label_names</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">eps</span><span class="o">=</span><span class="mf">1e-8</span><span class="p">):</span> |
| <span class="nb">super</span><span class="p">(</span><span class="n">MeanCosineSimilarity</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span> |
| <span class="n">name</span><span class="p">,</span> <span class="n">output_names</span><span class="o">=</span><span class="n">output_names</span><span class="p">,</span> <span class="n">label_names</span><span class="o">=</span><span class="n">label_names</span><span class="p">)</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">eps</span> <span class="o">=</span> <span class="n">eps</span> |
| |
| <div class="viewcode-block" id="MeanCosineSimilarity.update"><a class="viewcode-back" href="../../../api/gluon/metric/index.html#mxnet.gluon.metric.MeanCosineSimilarity.update">[docs]</a> <span class="k">def</span> <span class="nf">update</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">labels</span><span class="p">,</span> <span class="n">preds</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""Updates the internal evaluation result.</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> labels : list of `NDArray`</span> |
| <span class="sd"> The labels of the data.</span> |
| |
| <span class="sd"> preds : list of `NDArray`</span> |
| <span class="sd"> Predicted values.</span> |
| <span class="sd"> """</span> |
| <span class="n">labels</span><span class="p">,</span> <span class="n">preds</span> <span class="o">=</span> <span class="n">check_label_shapes</span><span class="p">(</span><span class="n">labels</span><span class="p">,</span> <span class="n">preds</span><span class="p">,</span> <span class="kc">True</span><span class="p">)</span> |
| |
| <span class="k">for</span> <span class="n">label</span><span class="p">,</span> <span class="n">pred</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="n">labels</span><span class="p">,</span> <span class="n">preds</span><span class="p">):</span> |
| <span class="n">label</span> <span class="o">=</span> <span class="n">label</span><span class="o">.</span><span class="n">as_np_ndarray</span><span class="p">()</span> |
| <span class="n">pred</span> <span class="o">=</span> <span class="n">pred</span><span class="o">.</span><span class="n">as_np_ndarray</span><span class="p">()</span><span class="o">.</span><span class="n">to_device</span><span class="p">(</span><span class="n">label</span><span class="o">.</span><span class="n">device</span><span class="p">)</span> |
| |
| <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">label</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span> |
| <span class="n">label</span> <span class="o">=</span> <span class="n">label</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="n">label</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span> |
| <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">pred</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span> |
| <span class="n">pred</span> <span class="o">=</span> <span class="n">pred</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="n">pred</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span> |
| |
| <span class="n">sim</span> <span class="o">=</span> <span class="p">(</span><span class="n">label</span> <span class="o">*</span> <span class="n">pred</span><span class="p">)</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">axis</span><span class="o">=-</span><span class="mi">1</span><span class="p">)</span> |
| <span class="n">n_p</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">linalg</span><span class="o">.</span><span class="n">norm</span><span class="p">(</span><span class="n">pred</span><span class="p">,</span> <span class="n">axis</span><span class="o">=-</span><span class="mi">1</span><span class="p">)</span> |
| <span class="n">n_l</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">linalg</span><span class="o">.</span><span class="n">norm</span><span class="p">(</span><span class="n">label</span><span class="p">,</span> <span class="n">axis</span><span class="o">=-</span><span class="mi">1</span><span class="p">)</span> |
| <span class="n">sim</span> <span class="o">=</span> <span class="n">sim</span> <span class="o">/</span> <span class="n">numpy</span><span class="o">.</span><span class="n">maximum</span><span class="p">(</span><span class="n">n_l</span> <span class="o">*</span> <span class="n">n_p</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">eps</span><span class="p">)</span> |
| <span class="n">sim</span> <span class="o">=</span> <span class="n">sim</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span> |
| <span class="n">num_inst</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">label</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="n">label</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]))</span> <span class="c1"># numpy.prod(label.shape[:-1]) is not supported</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">sum_metric</span> <span class="o">+=</span> <span class="n">sim</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">num_inst</span> <span class="o">+=</span> <span class="n">num_inst</span></div></div> |
| |
| |
| <div class="viewcode-block" id="CrossEntropy"><a class="viewcode-back" href="../../../api/gluon/metric/index.html#mxnet.gluon.metric.CrossEntropy">[docs]</a><span class="nd">@register</span> |
| <span class="nd">@alias</span><span class="p">(</span><span class="s1">'ce'</span><span class="p">)</span> |
| <span class="nd">@use_np</span> |
| <span class="k">class</span> <span class="nc">CrossEntropy</span><span class="p">(</span><span class="n">EvalMetric</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""Computes Cross Entropy loss.</span> |
| |
| <span class="sd"> The cross entropy over a batch of sample size :math:`N` is given by</span> |
| |
| <span class="sd"> .. math::</span> |
| |
| <span class="sd"> -\\sum_{n=1}^{N}\\sum_{k=1}^{K}t_{nk}\\log (y_{nk}),</span> |
| |
| <span class="sd"> where :math:`t_{nk}=1` if and only if sample :math:`n` belongs to class :math:`k`.</span> |
| <span class="sd"> :math:`y_{nk}` denotes the probability of sample :math:`n` belonging to</span> |
| <span class="sd"> class :math:`k`.</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> eps : float, default 1e-12</span> |
| <span class="sd"> Use small constant for the case that predicted value is 0.</span> |
| <span class="sd"> ignore_label : int or None, default None</span> |
| <span class="sd"> Index of invalid label to ignore when</span> |
| <span class="sd"> counting. By default, sets to -1.</span> |
| <span class="sd"> If set to `None`, it will include all entries.</span> |
| <span class="sd"> axis : int, default -1</span> |
| <span class="sd"> The axis from prediction that was used to</span> |
| <span class="sd"> compute softmax. By default use the last axis.</span> |
| <span class="sd"> from_logits : boolean, default False</span> |
| <span class="sd"> Whether `pred` is expected to be a logits tensor.</span> |
| <span class="sd"> By default, we assume that `pred` encodes a probability distribution.</span> |
| <span class="sd"> name : str</span> |
| <span class="sd"> Name of this metric instance for display.</span> |
| <span class="sd"> output_names : list of str, or None</span> |
| <span class="sd"> Name of predictions that should be used when updating with update_dict.</span> |
| <span class="sd"> By default include all predictions.</span> |
| <span class="sd"> label_names : list of str, or None</span> |
| <span class="sd"> Name of labels that should be used when updating with update_dict.</span> |
| <span class="sd"> By default include all labels.</span> |
| |
| <span class="sd"> Examples</span> |
| <span class="sd"> --------</span> |
| <span class="sd"> >>> predicts = [mx.np.array([[0.3, 0.7], [0, 1.], [0.4, 0.6]])]</span> |
| <span class="sd"> >>> labels = [mx.np.array([0, 1, 1])]</span> |
| <span class="sd"> >>> ce = mx.gluon.metric.CrossEntropy()</span> |
| <span class="sd"> >>> ce.update(labels, predicts)</span> |
| <span class="sd"> >>> ce.get()</span> |
| <span class="sd"> ('cross-entropy', 0.57159948348999023)</span> |
| <span class="sd"> """</span> |
| <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">eps</span><span class="o">=</span><span class="mf">1e-12</span><span class="p">,</span> <span class="n">ignore_label</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">axis</span><span class="o">=-</span><span class="mi">1</span><span class="p">,</span> <span class="n">from_logits</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> |
| <span class="n">name</span><span class="o">=</span><span class="s1">'cross-entropy'</span><span class="p">,</span> <span class="n">output_names</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">label_names</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> |
| <span class="nb">super</span><span class="p">(</span><span class="n">CrossEntropy</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span> |
| <span class="n">name</span><span class="p">,</span> <span class="n">output_names</span><span class="o">=</span><span class="n">output_names</span><span class="p">,</span> <span class="n">label_names</span><span class="o">=</span><span class="n">label_names</span><span class="p">)</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">ignore_label</span> <span class="o">=</span> <span class="n">ignore_label</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">axis</span> <span class="o">=</span> <span class="n">axis</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">from_logits</span> <span class="o">=</span> <span class="n">from_logits</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">eps</span> <span class="o">=</span> <span class="n">eps</span> |
| |
| <div class="viewcode-block" id="CrossEntropy.update"><a class="viewcode-back" href="../../../api/gluon/metric/index.html#mxnet.gluon.metric.CrossEntropy.update">[docs]</a> <span class="k">def</span> <span class="nf">update</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">labels</span><span class="p">,</span> <span class="n">preds</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""Updates the internal evaluation result.</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> labels : list of `NDArray`</span> |
| <span class="sd"> The labels of the data.</span> |
| |
| <span class="sd"> preds : list of `NDArray`</span> |
| <span class="sd"> Predicted values.</span> |
| <span class="sd"> """</span> |
| <span class="n">labels</span><span class="p">,</span> <span class="n">preds</span> <span class="o">=</span> <span class="n">check_label_shapes</span><span class="p">(</span><span class="n">labels</span><span class="p">,</span> <span class="n">preds</span><span class="p">,</span> <span class="kc">True</span><span class="p">)</span> |
| |
| <span class="n">loss</span> <span class="o">=</span> <span class="mf">0.</span> |
| <span class="n">num</span> <span class="o">=</span> <span class="mi">0</span> |
| <span class="k">for</span> <span class="n">label</span><span class="p">,</span> <span class="n">pred</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="n">labels</span><span class="p">,</span> <span class="n">preds</span><span class="p">):</span> |
| <span class="k">assert</span> <span class="n">label</span><span class="o">.</span><span class="n">size</span> <span class="o">==</span> <span class="n">pred</span><span class="o">.</span><span class="n">size</span><span class="o">/</span><span class="n">pred</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">],</span> \ |
| <span class="sa">f</span><span class="s2">"shape mismatch: </span><span class="si">{</span><span class="n">label</span><span class="o">.</span><span class="n">shape</span><span class="si">}</span><span class="s2"> vs. </span><span class="si">{</span><span class="n">pred</span><span class="o">.</span><span class="n">shape</span><span class="si">}</span><span class="s2">"</span> |
| <span class="n">label</span> <span class="o">=</span> <span class="n">label</span><span class="o">.</span><span class="n">reshape</span><span class="p">((</span><span class="n">label</span><span class="o">.</span><span class="n">size</span><span class="p">,))</span> |
| <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">from_logits</span><span class="p">:</span> |
| <span class="n">pred</span> <span class="o">=</span> <span class="n">npx</span><span class="o">.</span><span class="n">softmax</span><span class="p">(</span><span class="n">pred</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">axis</span><span class="p">)</span> |
| <span class="n">pred</span> <span class="o">=</span> <span class="n">npx</span><span class="o">.</span><span class="n">pick</span><span class="p">(</span><span class="n">pred</span><span class="o">.</span><span class="n">to_device</span><span class="p">(</span><span class="n">label</span><span class="o">.</span><span class="n">device</span><span class="p">),</span> <span class="n">label</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="n">dtype</span><span class="o">=</span><span class="s1">'int32'</span><span class="p">),</span> <span class="n">axis</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">axis</span><span class="p">)</span> |
| <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">ignore_label</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span> |
| <span class="n">ignore</span> <span class="o">=</span> <span class="p">(</span><span class="n">label</span> <span class="o">==</span> <span class="bp">self</span><span class="o">.</span><span class="n">ignore_label</span><span class="p">)</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="n">pred</span><span class="o">.</span><span class="n">dtype</span><span class="p">)</span> |
| <span class="n">num</span> <span class="o">-=</span> <span class="n">ignore</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span> |
| <span class="n">pred</span> <span class="o">=</span> <span class="n">pred</span> <span class="o">*</span> <span class="p">(</span><span class="mi">1</span> <span class="o">-</span> <span class="n">ignore</span><span class="p">)</span> <span class="o">+</span> <span class="n">ignore</span> |
| <span class="n">loss</span> <span class="o">-=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">log</span><span class="p">(</span><span class="n">numpy</span><span class="o">.</span><span class="n">maximum</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">eps</span><span class="p">,</span> <span class="n">pred</span><span class="p">))</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span> |
| <span class="n">num</span> <span class="o">+=</span> <span class="n">pred</span><span class="o">.</span><span class="n">size</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">sum_metric</span> <span class="o">+=</span> <span class="n">loss</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">num_inst</span> <span class="o">+=</span> <span class="n">num</span></div></div> |
| |
| |
| <div class="viewcode-block" id="Perplexity"><a class="viewcode-back" href="../../../api/gluon/metric/index.html#mxnet.gluon.metric.Perplexity">[docs]</a><span class="nd">@register</span> |
| <span class="nd">@use_np</span> |
| <span class="k">class</span> <span class="nc">Perplexity</span><span class="p">(</span><span class="n">CrossEntropy</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""Computes perplexity.</span> |
| |
| <span class="sd"> Perplexity is a measurement of how well a probability distribution</span> |
| <span class="sd"> or model predicts a sample. A low perplexity indicates the model</span> |
| <span class="sd"> is good at predicting the sample.</span> |
| |
| <span class="sd"> The perplexity of a model q is defined as</span> |
| |
| <span class="sd"> .. math::</span> |
| |
| <span class="sd"> b^{\\big(-\\frac{1}{N} \\sum_{i=1}^N \\log_b q(x_i) \\big)}</span> |
| <span class="sd"> = \\exp \\big(-\\frac{1}{N} \\sum_{i=1}^N \\log q(x_i)\\big)</span> |
| |
| <span class="sd"> where we let `b = e`.</span> |
| |
| <span class="sd"> :math:`q(x_i)` is the predicted value of its ground truth</span> |
| <span class="sd"> label on sample :math:`x_i`.</span> |
| |
| <span class="sd"> For example, we have three samples :math:`x_1, x_2, x_3` and their labels</span> |
| <span class="sd"> are :math:`[0, 1, 1]`.</span> |
| <span class="sd"> Suppose our model predicts :math:`q(x_1) = p(y_1 = 0 | x_1) = 0.3`</span> |
| <span class="sd"> and :math:`q(x_2) = 1.0`,</span> |
| <span class="sd"> :math:`q(x_3) = 0.6`. The perplexity of model q is</span> |
| <span class="sd"> :math:`exp\\big(-(\\log 0.3 + \\log 1.0 + \\log 0.6) / 3\\big) = 1.77109762852`.</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> eps : float, default 1e-12</span> |
| <span class="sd"> Use small constant for the case that predicted value is 0.</span> |
| <span class="sd"> ignore_label : int or None, default None</span> |
| <span class="sd"> Index of invalid label to ignore when</span> |
| <span class="sd"> counting. By default, sets to -1.</span> |
| <span class="sd"> If set to `None`, it will include all entries.</span> |
| <span class="sd"> axis : int (default -1)</span> |
| <span class="sd"> The axis from prediction that was used to</span> |
| <span class="sd"> compute softmax. By default use the last axis.</span> |
| <span class="sd"> name : str</span> |
| <span class="sd"> Name of this metric instance for display.</span> |
| <span class="sd"> output_names : list of str, or None</span> |
| <span class="sd"> Name of predictions that should be used when updating with update_dict.</span> |
| <span class="sd"> By default include all predictions.</span> |
| <span class="sd"> label_names : list of str, or None</span> |
| <span class="sd"> Name of labels that should be used when updating with update_dict.</span> |
| <span class="sd"> By default include all labels.</span> |
| |
| <span class="sd"> Examples</span> |
| <span class="sd"> --------</span> |
| <span class="sd"> >>> predicts = [mx.np.array([[0.3, 0.7], [0, 1.], [0.4, 0.6]])]</span> |
| <span class="sd"> >>> labels = [mx.np.array([0, 1, 1])]</span> |
| <span class="sd"> >>> perp = mx.gluon.metric.Perplexity(ignore_label=None)</span> |
| <span class="sd"> >>> perp.update(labels, predicts)</span> |
| <span class="sd"> >>> perp.get()</span> |
| <span class="sd"> ('Perplexity', 1.7710976285155853)</span> |
| <span class="sd"> """</span> |
| <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">eps</span><span class="o">=</span><span class="mf">1e-12</span><span class="p">,</span> <span class="n">ignore_label</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">axis</span><span class="o">=-</span><span class="mi">1</span><span class="p">,</span> <span class="n">from_logits</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> |
| <span class="n">name</span><span class="o">=</span><span class="s1">'perplexity'</span><span class="p">,</span> <span class="n">output_names</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">label_names</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> |
| <span class="nb">super</span><span class="p">(</span><span class="n">Perplexity</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span> |
| <span class="n">eps</span><span class="o">=</span><span class="n">eps</span><span class="p">,</span> <span class="n">ignore_label</span><span class="o">=</span><span class="n">ignore_label</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="n">axis</span><span class="p">,</span> <span class="n">from_logits</span><span class="o">=</span><span class="n">from_logits</span><span class="p">,</span> |
| <span class="n">name</span><span class="o">=</span><span class="n">name</span><span class="p">,</span> <span class="n">output_names</span><span class="o">=</span><span class="n">output_names</span><span class="p">,</span> <span class="n">label_names</span><span class="o">=</span><span class="n">label_names</span><span class="p">)</span> |
| |
| <div class="viewcode-block" id="Perplexity.get"><a class="viewcode-back" href="../../../api/gluon/metric/index.html#mxnet.gluon.metric.Perplexity.get">[docs]</a> <span class="k">def</span> <span class="nf">get</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span> |
| <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">num_inst</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span> |
| <span class="k">return</span> <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">name</span><span class="p">,</span> <span class="nb">float</span><span class="p">(</span><span class="s1">'nan'</span><span class="p">))</span> |
| <span class="k">else</span><span class="p">:</span> |
| <span class="k">return</span> <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">name</span><span class="p">,</span> <span class="n">math</span><span class="o">.</span><span class="n">exp</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">sum_metric</span><span class="o">/</span><span class="bp">self</span><span class="o">.</span><span class="n">num_inst</span><span class="p">))</span></div></div> |
| |
| |
| <div class="viewcode-block" id="PearsonCorrelation"><a class="viewcode-back" href="../../../api/gluon/metric/index.html#mxnet.gluon.metric.PearsonCorrelation">[docs]</a><span class="nd">@register</span> |
| <span class="nd">@alias</span><span class="p">(</span><span class="s1">'pearsonr'</span><span class="p">)</span> |
| <span class="nd">@use_np</span> |
| <span class="k">class</span> <span class="nc">PearsonCorrelation</span><span class="p">(</span><span class="n">EvalMetric</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""Computes Pearson correlation.</span> |
| |
| <span class="sd"> The pearson correlation is given by</span> |
| |
| <span class="sd"> .. math::</span> |
| |
| <span class="sd"> \\frac{cov(y, \\hat{y})}{\\sigma{y}\\sigma{\\hat{y}}}</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> name : str</span> |
| <span class="sd"> Name of this metric instance for display.</span> |
| <span class="sd"> output_names : list of str, or None</span> |
| <span class="sd"> Name of predictions that should be used when updating with update_dict.</span> |
| <span class="sd"> By default include all predictions.</span> |
| <span class="sd"> label_names : list of str, or None</span> |
| <span class="sd"> Name of labels that should be used when updating with update_dict.</span> |
| <span class="sd"> By default include all labels.</span> |
| |
| <span class="sd"> Examples</span> |
| <span class="sd"> --------</span> |
| <span class="sd"> >>> predicts = [mx.np.array([[0.3, 0.7], [0, 1.], [0.4, 0.6]])]</span> |
| <span class="sd"> >>> labels = [mx.np.array([[1, 0], [0, 1], [0, 1]])]</span> |
| <span class="sd"> >>> pr = mx.gluon.metric.PearsonCorrelation()</span> |
| <span class="sd"> >>> pr.update(labels, predicts)</span> |
| <span class="sd"> >>> pr.get()</span> |
| <span class="sd"> ('pearsonr', 0.42163704544016178)</span> |
| <span class="sd"> """</span> |
| <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">name</span><span class="o">=</span><span class="s1">'pearsonr'</span><span class="p">,</span> |
| <span class="n">output_names</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">label_names</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> |
| <span class="nb">super</span><span class="p">(</span><span class="n">PearsonCorrelation</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span> |
| <span class="n">name</span><span class="p">,</span> <span class="n">output_names</span><span class="o">=</span><span class="n">output_names</span><span class="p">,</span> <span class="n">label_names</span><span class="o">=</span><span class="n">label_names</span><span class="p">)</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">reset</span><span class="p">()</span> |
| |
| <div class="viewcode-block" id="PearsonCorrelation.reset"><a class="viewcode-back" href="../../../api/gluon/metric/index.html#mxnet.gluon.metric.PearsonCorrelation.reset">[docs]</a> <span class="k">def</span> <span class="nf">reset</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_sse_p</span> <span class="o">=</span> <span class="mi">0</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_mean_p</span> <span class="o">=</span> <span class="mi">0</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_sse_l</span> <span class="o">=</span> <span class="mi">0</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_mean_l</span> <span class="o">=</span> <span class="mi">0</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_pred_nums</span> <span class="o">=</span> <span class="mi">0</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_label_nums</span> <span class="o">=</span> <span class="mi">0</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_conv</span> <span class="o">=</span> <span class="mi">0</span> |
| |
| <span class="bp">self</span><span class="o">.</span><span class="n">num_inst</span> <span class="o">=</span> <span class="mi">0</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">sum_metric</span> <span class="o">=</span> <span class="mf">0.0</span></div> |
| |
| <span class="k">def</span> <span class="nf">update_variance</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">new_values</span><span class="p">,</span> <span class="o">*</span><span class="n">aggregate</span><span class="p">):</span> |
| <span class="c1">#Welford's online algorithm for variance update</span> |
| <span class="n">count</span><span class="p">,</span> <span class="n">mean</span><span class="p">,</span> <span class="n">m_2</span> <span class="o">=</span> <span class="n">aggregate</span> |
| <span class="n">count</span> <span class="o">+=</span> <span class="nb">len</span><span class="p">(</span><span class="n">new_values</span><span class="p">)</span> |
| <span class="n">delta</span> <span class="o">=</span> <span class="n">new_values</span> <span class="o">-</span> <span class="n">mean</span> |
| <span class="n">mean</span> <span class="o">+=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">delta</span> <span class="o">/</span> <span class="n">count</span><span class="p">)</span> |
| <span class="n">delta_2</span> <span class="o">=</span> <span class="n">new_values</span> <span class="o">-</span> <span class="n">mean</span> |
| <span class="n">m_2</span> <span class="o">+=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">delta</span> <span class="o">*</span> <span class="n">delta_2</span><span class="p">)</span> |
| <span class="k">return</span> <span class="n">count</span><span class="p">,</span> <span class="n">mean</span><span class="p">,</span> <span class="n">m_2</span> |
| |
| <span class="k">def</span> <span class="nf">update_cov</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">label</span><span class="p">,</span> <span class="n">pred</span><span class="p">):</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_conv</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_conv</span> <span class="o">+</span> <span class="n">numpy</span><span class="o">.</span><span class="n">sum</span><span class="p">((</span><span class="n">label</span> <span class="o">-</span> <span class="bp">self</span><span class="o">.</span><span class="n">_mean_l</span><span class="p">)</span> <span class="o">*</span> <span class="p">(</span><span class="n">pred</span> <span class="o">-</span> <span class="bp">self</span><span class="o">.</span><span class="n">_mean_p</span><span class="p">))</span> |
| |
| <div class="viewcode-block" id="PearsonCorrelation.update"><a class="viewcode-back" href="../../../api/gluon/metric/index.html#mxnet.gluon.metric.PearsonCorrelation.update">[docs]</a> <span class="k">def</span> <span class="nf">update</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">labels</span><span class="p">,</span> <span class="n">preds</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""Updates the internal evaluation result.</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> labels : list of `NDArray`</span> |
| <span class="sd"> The labels of the data.</span> |
| <span class="sd"> preds : list of `NDArray`</span> |
| <span class="sd"> Predicted values.</span> |
| <span class="sd"> """</span> |
| <span class="n">labels</span><span class="p">,</span> <span class="n">preds</span> <span class="o">=</span> <span class="n">check_label_shapes</span><span class="p">(</span><span class="n">labels</span><span class="p">,</span> <span class="n">preds</span><span class="p">,</span> <span class="kc">True</span><span class="p">)</span> |
| <span class="k">for</span> <span class="n">label</span><span class="p">,</span> <span class="n">pred</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="n">labels</span><span class="p">,</span> <span class="n">preds</span><span class="p">):</span> |
| <span class="n">check_label_shapes</span><span class="p">(</span><span class="n">label</span><span class="p">,</span> <span class="n">pred</span><span class="p">,</span> <span class="kc">False</span><span class="p">,</span> <span class="kc">True</span><span class="p">)</span> |
| <span class="n">label</span> <span class="o">=</span> <span class="n">label</span><span class="o">.</span><span class="n">as_np_ndarray</span><span class="p">()</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">)</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="n">numpy</span><span class="o">.</span><span class="n">float64</span><span class="p">)</span> |
| <span class="n">pred</span> <span class="o">=</span> <span class="n">pred</span><span class="o">.</span><span class="n">as_np_ndarray</span><span class="p">()</span><span class="o">.</span><span class="n">to_device</span><span class="p">(</span><span class="n">label</span><span class="o">.</span><span class="n">device</span><span class="p">)</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">)</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="n">numpy</span><span class="o">.</span><span class="n">float64</span><span class="p">)</span> |
| |
| <span class="bp">self</span><span class="o">.</span><span class="n">num_inst</span> <span class="o">+=</span> <span class="mi">1</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_label_nums</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">_mean_l</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">_sse_l</span> <span class="o">=</span> \ |
| <span class="bp">self</span><span class="o">.</span><span class="n">update_variance</span><span class="p">(</span><span class="n">label</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">_label_nums</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">_mean_l</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">_sse_l</span><span class="p">)</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">update_cov</span><span class="p">(</span><span class="n">label</span><span class="p">,</span> <span class="n">pred</span><span class="p">)</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_pred_nums</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">_mean_p</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">_sse_p</span> <span class="o">=</span> \ |
| <span class="bp">self</span><span class="o">.</span><span class="n">update_variance</span><span class="p">(</span><span class="n">pred</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">_pred_nums</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">_mean_p</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">_sse_p</span><span class="p">)</span></div> |
| |
| <div class="viewcode-block" id="PearsonCorrelation.get"><a class="viewcode-back" href="../../../api/gluon/metric/index.html#mxnet.gluon.metric.PearsonCorrelation.get">[docs]</a> <span class="k">def</span> <span class="nf">get</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span> |
| <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">num_inst</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span> |
| <span class="k">return</span> <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">name</span><span class="p">,</span> <span class="nb">float</span><span class="p">(</span><span class="s1">'nan'</span><span class="p">))</span> |
| |
| <span class="n">n</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_label_nums</span> |
| <span class="n">pearsonr</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_conv</span> <span class="o">/</span> <span class="p">((</span><span class="n">n</span><span class="o">-</span><span class="mi">1</span><span class="p">)</span> <span class="o">*</span> <span class="n">numpy</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_sse_p</span> <span class="o">/</span> <span class="p">(</span><span class="n">n</span> <span class="o">-</span> <span class="mi">1</span><span class="p">))</span> <span class="o">*</span> <span class="n">numpy</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_sse_l</span> <span class="o">/</span> <span class="p">(</span><span class="n">n</span> <span class="o">-</span> <span class="mi">1</span><span class="p">)))</span> |
| <span class="k">return</span> <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">name</span><span class="p">,</span> <span class="nb">float</span><span class="p">(</span><span class="n">pearsonr</span><span class="p">))</span></div></div> |
| |
| <div class="viewcode-block" id="PCC"><a class="viewcode-back" href="../../../api/gluon/metric/index.html#mxnet.gluon.metric.PCC">[docs]</a><span class="nd">@register</span> |
| <span class="nd">@use_np</span> |
| <span class="k">class</span> <span class="nc">PCC</span><span class="p">(</span><span class="n">EvalMetric</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""PCC is a multiclass equivalent for the Matthews correlation coefficient derived</span> |
| <span class="sd"> from a discrete solution to the Pearson correlation coefficient.</span> |
| |
| <span class="sd"> .. math::</span> |
| |
| <span class="sd"> \\text{PCC} = \\frac {\\sum _{k}\\sum _{l}\\sum _{m}C_{kk}C_{lm}-C_{kl}C_{mk}}</span> |
| <span class="sd"> {{\\sqrt {\\sum _{k}(\\sum _{l}C_{kl})(\\sum _{k'|k'\\neq k}\\sum _{l'}C_{k'l'})}}</span> |
| <span class="sd"> {\\sqrt {\\sum _{k}(\\sum _{l}C_{lk})(\\sum _{k'|k'\\neq k}\\sum _{l'}C_{l'k'})}}}</span> |
| |
| <span class="sd"> defined in terms of a K x K confusion matrix C.</span> |
| |
| <span class="sd"> When there are more than two labels the PCC will no longer range between -1 and +1.</span> |
| <span class="sd"> Instead the minimum value will be between -1 and 0 depending on the true distribution.</span> |
| <span class="sd"> The maximum value is always +1.</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> name : str</span> |
| <span class="sd"> Name of this metric instance for display.</span> |
| <span class="sd"> output_names : list of str, or None</span> |
| <span class="sd"> Name of predictions that should be used when updating with update_dict.</span> |
| <span class="sd"> By default include all predictions.</span> |
| <span class="sd"> label_names : list of str, or None</span> |
| <span class="sd"> Name of labels that should be used when updating with update_dict.</span> |
| <span class="sd"> By default include all labels.</span> |
| |
| <span class="sd"> Examples</span> |
| <span class="sd"> --------</span> |
| <span class="sd"> >>> # In this example the network almost always predicts positive</span> |
| <span class="sd"> >>> false_positives = 1000</span> |
| <span class="sd"> >>> false_negatives = 1</span> |
| <span class="sd"> >>> true_positives = 10000</span> |
| <span class="sd"> >>> true_negatives = 1</span> |
| <span class="sd"> >>> predicts = [mx.np.array(</span> |
| <span class="sd"> [[.3, .7]]*false_positives +</span> |
| <span class="sd"> [[.7, .3]]*true_negatives +</span> |
| <span class="sd"> [[.7, .3]]*false_negatives +</span> |
| <span class="sd"> [[.3, .7]]*true_positives</span> |
| <span class="sd"> )]</span> |
| <span class="sd"> >>> labels = [mx.np.array(</span> |
| <span class="sd"> [0]*(false_positives + true_negatives) +</span> |
| <span class="sd"> [1]*(false_negatives + true_positives)</span> |
| <span class="sd"> )]</span> |
| <span class="sd"> >>> f1 = mx.gluon.metric.F1()</span> |
| <span class="sd"> >>> f1.update(preds = predicts, labels = labels)</span> |
| <span class="sd"> >>> pcc = mx.gluon.metric.PCC()</span> |
| <span class="sd"> >>> pcc.update(preds = predicts, labels = labels)</span> |
| <span class="sd"> >>> f1.get()</span> |
| <span class="sd"> ('f1', 0.95233560306652054)</span> |
| <span class="sd"> >>> pcc.get()</span> |
| <span class="sd"> ('pcc', 0.01917751877733392)</span> |
| <span class="sd"> """</span> |
| <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">name</span><span class="o">=</span><span class="s1">'pcc'</span><span class="p">,</span> |
| <span class="n">output_names</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">label_names</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">k</span> <span class="o">=</span> <span class="mi">2</span> |
| <span class="nb">super</span><span class="p">(</span><span class="n">PCC</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span> |
| <span class="n">name</span><span class="o">=</span><span class="n">name</span><span class="p">,</span> <span class="n">output_names</span><span class="o">=</span><span class="n">output_names</span><span class="p">,</span> <span class="n">label_names</span><span class="o">=</span><span class="n">label_names</span><span class="p">)</span> |
| |
| <span class="k">def</span> <span class="nf">_grow</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">inc</span><span class="p">):</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">lcm</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">pad</span><span class="p">(</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">lcm</span><span class="p">,</span> <span class="p">((</span><span class="mi">0</span><span class="p">,</span> <span class="n">inc</span><span class="p">),</span> <span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">inc</span><span class="p">)),</span> <span class="s1">'constant'</span><span class="p">,</span> <span class="n">constant_values</span><span class="o">=</span><span class="p">(</span><span class="mi">0</span><span class="p">))</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">k</span> <span class="o">+=</span> <span class="n">inc</span> |
| |
| <span class="k">def</span> <span class="nf">_calc_mcc</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">cmat</span><span class="p">):</span> |
| <span class="n">n</span> <span class="o">=</span> <span class="n">cmat</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span> |
| <span class="n">x</span> <span class="o">=</span> <span class="n">cmat</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span> |
| <span class="n">y</span> <span class="o">=</span> <span class="n">cmat</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span> |
| <span class="n">cov_xx</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">x</span> <span class="o">*</span> <span class="p">(</span><span class="n">n</span> <span class="o">-</span> <span class="n">x</span><span class="p">))</span> |
| <span class="n">cov_yy</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">y</span> <span class="o">*</span> <span class="p">(</span><span class="n">n</span> <span class="o">-</span> <span class="n">y</span><span class="p">))</span> |
| <span class="k">if</span> <span class="n">cov_xx</span> <span class="o">==</span> <span class="mi">0</span> <span class="ow">or</span> <span class="n">cov_yy</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span> |
| <span class="k">return</span> <span class="nb">float</span><span class="p">(</span><span class="s1">'nan'</span><span class="p">)</span> |
| <span class="c1"># i = cmat.diagonal() # mxnet.numpy.ndarray.diagonal() is currently not available.</span> |
| <span class="n">i</span> <span class="o">=</span> <span class="n">cmat</span><span class="p">[</span><span class="n">numpy</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">k</span><span class="p">),</span> <span class="n">numpy</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">k</span><span class="p">)]</span> |
| <span class="n">cov_xy</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">i</span> <span class="o">*</span> <span class="n">n</span> <span class="o">-</span> <span class="n">x</span> <span class="o">*</span> <span class="n">y</span><span class="p">)</span> |
| <span class="k">return</span> <span class="n">cov_xy</span> <span class="o">/</span> <span class="p">(</span><span class="n">cov_xx</span> <span class="o">*</span> <span class="n">cov_yy</span><span class="p">)</span> <span class="o">**</span> <span class="mf">0.5</span> |
| |
| <div class="viewcode-block" id="PCC.update"><a class="viewcode-back" href="../../../api/gluon/metric/index.html#mxnet.gluon.metric.PCC.update">[docs]</a> <span class="k">def</span> <span class="nf">update</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">labels</span><span class="p">,</span> <span class="n">preds</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""Updates the internal evaluation result.</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> labels : list of `NDArray`</span> |
| <span class="sd"> The labels of the data.</span> |
| |
| <span class="sd"> preds : list of `NDArray`</span> |
| <span class="sd"> Predicted values.</span> |
| <span class="sd"> """</span> |
| <span class="n">labels</span><span class="p">,</span> <span class="n">preds</span> <span class="o">=</span> <span class="n">check_label_shapes</span><span class="p">(</span><span class="n">labels</span><span class="p">,</span> <span class="n">preds</span><span class="p">,</span> <span class="kc">True</span><span class="p">)</span> |
| |
| <span class="c1"># update the confusion matrix</span> |
| <span class="k">for</span> <span class="n">label</span><span class="p">,</span> <span class="n">pred</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="n">labels</span><span class="p">,</span> <span class="n">preds</span><span class="p">):</span> |
| <span class="n">label</span> <span class="o">=</span> <span class="n">label</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="s1">'int32'</span><span class="p">,</span> <span class="n">copy</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span><span class="o">.</span><span class="n">as_np_ndarray</span><span class="p">()</span> |
| <span class="n">pred</span> <span class="o">=</span> <span class="n">pred</span><span class="o">.</span><span class="n">as_np_ndarray</span><span class="p">()</span><span class="o">.</span><span class="n">to_device</span><span class="p">(</span><span class="n">label</span><span class="o">.</span><span class="n">device</span><span class="p">)</span> |
| <span class="k">if</span> <span class="n">pred</span><span class="o">.</span><span class="n">shape</span> <span class="o">!=</span> <span class="n">label</span><span class="o">.</span><span class="n">shape</span><span class="p">:</span> |
| <span class="n">pred</span> <span class="o">=</span> <span class="n">pred</span><span class="o">.</span><span class="n">argmax</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="n">label</span><span class="p">,</span> <span class="n">copy</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span> |
| <span class="k">else</span><span class="p">:</span> |
| <span class="n">pred</span> <span class="o">=</span> <span class="n">pred</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="s1">'int32'</span><span class="p">,</span> <span class="n">copy</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span> |
| <span class="n">n</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="nb">max</span><span class="p">(</span><span class="n">pred</span><span class="o">.</span><span class="n">max</span><span class="p">(),</span> <span class="n">label</span><span class="o">.</span><span class="n">max</span><span class="p">()))</span> |
| <span class="k">if</span> <span class="n">n</span> <span class="o">>=</span> <span class="bp">self</span><span class="o">.</span><span class="n">k</span><span class="p">:</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_grow</span><span class="p">(</span><span class="n">n</span> <span class="o">+</span> <span class="mi">1</span> <span class="o">-</span> <span class="bp">self</span><span class="o">.</span><span class="n">k</span><span class="p">)</span> |
| <span class="n">bcm</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">zeros</span><span class="p">((</span><span class="bp">self</span><span class="o">.</span><span class="n">k</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">k</span><span class="p">),</span> <span class="n">dtype</span><span class="o">=</span><span class="s1">'float64'</span><span class="p">)</span> |
| <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">j</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="n">pred</span><span class="p">,</span> <span class="n">label</span><span class="p">):</span> |
| <span class="n">bcm</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">]</span> <span class="o">+=</span> <span class="mi">1</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">lcm</span> <span class="o">+=</span> <span class="n">bcm</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">num_inst</span> <span class="o">+=</span> <span class="mi">1</span></div> |
| |
| <span class="nd">@property</span> |
| <span class="k">def</span> <span class="nf">sum_metric</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span> |
| <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_calc_mcc</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">lcm</span><span class="p">)</span> <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">num_inst</span> |
| |
| <div class="viewcode-block" id="PCC.reset"><a class="viewcode-back" href="../../../api/gluon/metric/index.html#mxnet.gluon.metric.PCC.reset">[docs]</a> <span class="k">def</span> <span class="nf">reset</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""Resets the internal evaluation result to initial state."""</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">num_inst</span> <span class="o">=</span> <span class="mf">0.</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">lcm</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">zeros</span><span class="p">((</span><span class="bp">self</span><span class="o">.</span><span class="n">k</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">k</span><span class="p">),</span> <span class="n">dtype</span><span class="o">=</span><span class="s1">'float64'</span><span class="p">)</span></div></div> |
| |
| |
| <div class="viewcode-block" id="Loss"><a class="viewcode-back" href="../../../api/gluon/metric/index.html#mxnet.gluon.metric.Loss">[docs]</a><span class="nd">@register</span> |
| <span class="nd">@use_np</span> |
| <span class="k">class</span> <span class="nc">Loss</span><span class="p">(</span><span class="n">EvalMetric</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""Dummy metric for directly printing loss.</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> name : str</span> |
| <span class="sd"> Name of this metric instance for display.</span> |
| <span class="sd"> output_names : list of str, or None</span> |
| <span class="sd"> Name of predictions that should be used when updating with update_dict.</span> |
| <span class="sd"> By default include all predictions.</span> |
| <span class="sd"> label_names : list of str, or None</span> |
| <span class="sd"> Name of labels that should be used when updating with update_dict.</span> |
| <span class="sd"> By default include all labels.</span> |
| <span class="sd"> """</span> |
| <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">name</span><span class="o">=</span><span class="s1">'loss'</span><span class="p">,</span> |
| <span class="n">output_names</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">label_names</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> |
| <span class="nb">super</span><span class="p">(</span><span class="n">Loss</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span> |
| <span class="n">name</span><span class="p">,</span> <span class="n">output_names</span><span class="o">=</span><span class="n">output_names</span><span class="p">,</span> <span class="n">label_names</span><span class="o">=</span><span class="n">label_names</span><span class="p">)</span> |
| |
| <div class="viewcode-block" id="Loss.update"><a class="viewcode-back" href="../../../api/gluon/metric/index.html#mxnet.gluon.metric.Loss.update">[docs]</a> <span class="k">def</span> <span class="nf">update</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">_</span><span class="p">,</span> <span class="n">preds</span><span class="p">):</span> |
| |
| <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">preds</span><span class="p">,</span> <span class="n">ndarray</span><span class="o">.</span><span class="n">ndarray</span><span class="o">.</span><span class="n">NDArray</span><span class="p">):</span> |
| <span class="n">preds</span> <span class="o">=</span> <span class="p">[</span><span class="n">preds</span><span class="p">]</span> |
| |
| <span class="k">for</span> <span class="n">pred</span> <span class="ow">in</span> <span class="n">preds</span><span class="p">:</span> |
| <span class="n">loss</span> <span class="o">=</span> <span class="n">pred</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span><span class="o">.</span><span class="n">item</span><span class="p">()</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">sum_metric</span> <span class="o">+=</span> <span class="n">loss</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">num_inst</span> <span class="o">+=</span> <span class="n">pred</span><span class="o">.</span><span class="n">size</span></div></div> |
| |
| |
| <div class="viewcode-block" id="Torch"><a class="viewcode-back" href="../../../api/gluon/metric/index.html#mxnet.gluon.metric.Torch">[docs]</a><span class="nd">@register</span> |
| <span class="k">class</span> <span class="nc">Torch</span><span class="p">(</span><span class="n">Loss</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""Dummy metric for torch criterions."""</span> |
| <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">name</span><span class="o">=</span><span class="s1">'torch'</span><span class="p">,</span> |
| <span class="n">output_names</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">label_names</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> |
| <span class="nb">super</span><span class="p">(</span><span class="n">Torch</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span> |
| <span class="n">name</span><span class="p">,</span> <span class="n">output_names</span><span class="o">=</span><span class="n">output_names</span><span class="p">,</span> <span class="n">label_names</span><span class="o">=</span><span class="n">label_names</span><span class="p">)</span></div> |
| |
| |
| <div class="viewcode-block" id="CustomMetric"><a class="viewcode-back" href="../../../api/gluon/metric/index.html#mxnet.gluon.metric.CustomMetric">[docs]</a><span class="nd">@register</span> |
| <span class="nd">@use_np</span> |
| <span class="k">class</span> <span class="nc">CustomMetric</span><span class="p">(</span><span class="n">EvalMetric</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""Computes a customized evaluation metric.</span> |
| |
| <span class="sd"> The `feval` function can return a `tuple` of (sum_metric, num_inst) or return</span> |
| <span class="sd"> an `int` sum_metric.</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> feval : callable(label, pred)</span> |
| <span class="sd"> Customized evaluation function.</span> |
| <span class="sd"> name : str, optional</span> |
| <span class="sd"> The name of the metric. (the default is None).</span> |
| <span class="sd"> allow_extra_outputs : bool, optional</span> |
| <span class="sd"> If true, the prediction outputs can have extra outputs.</span> |
| <span class="sd"> This is useful in RNN, where the states are also produced</span> |
| <span class="sd"> in outputs for forwarding. (the default is False).</span> |
| <span class="sd"> name : str</span> |
| <span class="sd"> Name of this metric instance for display.</span> |
| <span class="sd"> output_names : list of str, or None</span> |
| <span class="sd"> Name of predictions that should be used when updating with update_dict.</span> |
| <span class="sd"> By default include all predictions.</span> |
| <span class="sd"> label_names : list of str, or None</span> |
| <span class="sd"> Name of labels that should be used when updating with update_dict.</span> |
| <span class="sd"> By default include all labels.</span> |
| |
| <span class="sd"> Examples</span> |
| <span class="sd"> --------</span> |
| <span class="sd"> >>> predicts = [mx.np.array(np.array([3, -0.5, 2, 7]).reshape(4,1))]</span> |
| <span class="sd"> >>> labels = [mx.np.array(np.array([2.5, 0.0, 2, 8]).reshape(4,1))]</span> |
| <span class="sd"> >>> feval = lambda x, y : (x + y).mean()</span> |
| <span class="sd"> >>> eval_metrics = mx.gluon.metric.CustomMetric(feval=feval)</span> |
| <span class="sd"> >>> eval_metrics.update(labels, predicts)</span> |
| <span class="sd"> >>> eval_metrics.get()</span> |
| <span class="sd"> ('custom(<lambda>)', 6.0)</span> |
| <span class="sd"> """</span> |
| <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">feval</span><span class="p">,</span> <span class="n">name</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">allow_extra_outputs</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> |
| <span class="n">output_names</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">label_names</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> |
| <span class="k">if</span> <span class="n">name</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span> |
| <span class="n">name</span> <span class="o">=</span> <span class="n">feval</span><span class="o">.</span><span class="vm">__name__</span> |
| <span class="k">if</span> <span class="n">name</span><span class="o">.</span><span class="n">find</span><span class="p">(</span><span class="s1">'<'</span><span class="p">)</span> <span class="o">!=</span> <span class="o">-</span><span class="mi">1</span><span class="p">:</span> |
| <span class="n">name</span> <span class="o">=</span> <span class="sa">f</span><span class="s1">'custom(</span><span class="si">{</span><span class="n">name</span><span class="si">}</span><span class="s1">)'</span> |
| <span class="nb">super</span><span class="p">(</span><span class="n">CustomMetric</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span> |
| <span class="n">name</span><span class="p">,</span> <span class="n">feval</span><span class="o">=</span><span class="n">feval</span><span class="p">,</span> |
| <span class="n">allow_extra_outputs</span><span class="o">=</span><span class="n">allow_extra_outputs</span><span class="p">,</span> |
| <span class="n">output_names</span><span class="o">=</span><span class="n">output_names</span><span class="p">,</span> <span class="n">label_names</span><span class="o">=</span><span class="n">label_names</span><span class="p">)</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_feval</span> <span class="o">=</span> <span class="n">feval</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_allow_extra_outputs</span> <span class="o">=</span> <span class="n">allow_extra_outputs</span> |
| |
| <div class="viewcode-block" id="CustomMetric.update"><a class="viewcode-back" href="../../../api/gluon/metric/index.html#mxnet.gluon.metric.CustomMetric.update">[docs]</a> <span class="k">def</span> <span class="nf">update</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">labels</span><span class="p">,</span> <span class="n">preds</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""Updates the internal evaluation result.</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> labels : list of `NDArray`</span> |
| <span class="sd"> The labels of the data.</span> |
| |
| <span class="sd"> preds : list of `NDArray`</span> |
| <span class="sd"> Predicted values.</span> |
| <span class="sd"> """</span> |
| <span class="k">if</span> <span class="ow">not</span> <span class="bp">self</span><span class="o">.</span><span class="n">_allow_extra_outputs</span><span class="p">:</span> |
| <span class="n">labels</span><span class="p">,</span> <span class="n">preds</span> <span class="o">=</span> <span class="n">check_label_shapes</span><span class="p">(</span><span class="n">labels</span><span class="p">,</span> <span class="n">preds</span><span class="p">,</span> <span class="kc">True</span><span class="p">)</span> |
| |
| <span class="k">for</span> <span class="n">pred</span><span class="p">,</span> <span class="n">label</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="n">preds</span><span class="p">,</span> <span class="n">labels</span><span class="p">):</span> |
| <span class="n">label</span> <span class="o">=</span> <span class="n">label</span><span class="o">.</span><span class="n">as_np_ndarray</span><span class="p">()</span> |
| <span class="n">pred</span> <span class="o">=</span> <span class="n">pred</span><span class="o">.</span><span class="n">as_np_ndarray</span><span class="p">()</span><span class="o">.</span><span class="n">to_device</span><span class="p">(</span><span class="n">label</span><span class="o">.</span><span class="n">device</span><span class="p">)</span> |
| |
| <span class="n">reval</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_feval</span><span class="p">(</span><span class="n">label</span><span class="p">,</span> <span class="n">pred</span><span class="p">)</span> |
| <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">reval</span><span class="p">,</span> <span class="nb">tuple</span><span class="p">):</span> |
| <span class="p">(</span><span class="n">sum_metric</span><span class="p">,</span> <span class="n">num_inst</span><span class="p">)</span> <span class="o">=</span> <span class="n">reval</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">sum_metric</span> <span class="o">+=</span> <span class="n">sum_metric</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">num_inst</span> <span class="o">+=</span> <span class="n">num_inst</span> |
| <span class="k">else</span><span class="p">:</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">sum_metric</span> <span class="o">+=</span> <span class="n">reval</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">num_inst</span> <span class="o">+=</span> <span class="mi">1</span></div> |
| |
| <div class="viewcode-block" id="CustomMetric.get_config"><a class="viewcode-back" href="../../../api/gluon/metric/index.html#mxnet.gluon.metric.CustomMetric.get_config">[docs]</a> <span class="k">def</span> <span class="nf">get_config</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span> |
| <span class="k">raise</span> <span class="ne">NotImplementedError</span><span class="p">(</span><span class="s2">"CustomMetric cannot be serialized"</span><span class="p">)</span></div></div> |
| |
| |
| <span class="c1"># pylint: disable=invalid-name</span> |
| <div class="viewcode-block" id="np"><a class="viewcode-back" href="../../../api/gluon/metric/index.html#mxnet.gluon.metric.np">[docs]</a><span class="k">def</span> <span class="nf">np</span><span class="p">(</span><span class="n">numpy_feval</span><span class="p">,</span> <span class="n">name</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">allow_extra_outputs</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""Creates a custom evaluation metric that receives its inputs as numpy arrays.</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> numpy_feval : callable(label, pred)</span> |
| <span class="sd"> Custom evaluation function that receives labels and predictions for a minibatch</span> |
| <span class="sd"> as numpy arrays and returns the corresponding custom metric as a floating point number.</span> |
| <span class="sd"> name : str, optional</span> |
| <span class="sd"> Name of the custom metric.</span> |
| <span class="sd"> allow_extra_outputs : bool, optional</span> |
| <span class="sd"> Whether prediction output is allowed to have extra outputs. This is useful in cases</span> |
| <span class="sd"> like RNN where states are also part of output which can then be fed back to the RNN</span> |
| <span class="sd"> in the next step. By default, extra outputs are not allowed.</span> |
| |
| <span class="sd"> Returns</span> |
| <span class="sd"> -------</span> |
| <span class="sd"> float</span> |
| <span class="sd"> Custom metric corresponding to the provided labels and predictions.</span> |
| |
| <span class="sd"> Example</span> |
| <span class="sd"> -------</span> |
| <span class="sd"> >>> def custom_metric(label, pred):</span> |
| <span class="sd"> ... return np.mean(np.abs(label-pred))</span> |
| <span class="sd"> ...</span> |
| <span class="sd"> >>> metric = mx.gluon.metric.np(custom_metric)</span> |
| <span class="sd"> """</span> |
| <span class="k">def</span> <span class="nf">feval</span><span class="p">(</span><span class="n">label</span><span class="p">,</span> <span class="n">pred</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""Internal eval function."""</span> |
| <span class="k">return</span> <span class="n">numpy_feval</span><span class="p">(</span><span class="n">label</span><span class="p">,</span> <span class="n">pred</span><span class="p">)</span> |
| <span class="n">feval</span><span class="o">.</span><span class="vm">__name__</span> <span class="o">=</span> <span class="n">numpy_feval</span><span class="o">.</span><span class="vm">__name__</span> |
| <span class="k">return</span> <span class="n">CustomMetric</span><span class="p">(</span><span class="n">feval</span><span class="p">,</span> <span class="n">name</span><span class="p">,</span> <span class="n">allow_extra_outputs</span><span class="p">)</span></div> |
| <span class="c1"># pylint: enable=invalid-name</span> |
| </pre></div> |
| |
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