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| <li class="toctree-l1"><a class="reference internal" href="../../tutorials/index.html">Python Tutorials</a><ul> |
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| <li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/1-ndarray.html">Manipulate data with <code class="docutils literal notranslate"><span class="pre">ndarray</span></code></a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/2-nn.html">Create a neural network</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/3-autograd.html">Automatic differentiation with <code class="docutils literal notranslate"><span class="pre">autograd</span></code></a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/4-train.html">Train the neural network</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/5-predict.html">Predict with a pre-trained model</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/6-use_gpus.html">Use GPUs</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l3"><a class="reference internal" href="../../tutorials/getting-started/to-mxnet/index.html">Moving to MXNet from Other Frameworks</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/to-mxnet/pytorch.html">PyTorch vs Apache MXNet</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l3"><a class="reference internal" href="../../tutorials/getting-started/gluon_from_experiment_to_deployment.html">Gluon: from experiment to deployment</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../tutorials/getting-started/logistic_regression_explained.html">Logistic regression explained</a></li> |
| <li class="toctree-l3"><a class="reference external" href="https://mxnet.apache.org/api/python/docs/tutorials/packages/gluon/image/mnist.html">MNIST</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l2"><a class="reference internal" href="../../tutorials/packages/index.html">Packages</a><ul> |
| <li class="toctree-l3"><a class="reference internal" href="../../tutorials/packages/autograd/index.html">Automatic Differentiation</a></li> |
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| <li class="toctree-l4"><a class="reference internal" href="../../tutorials/packages/gluon/blocks/index.html">Blocks</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../tutorials/packages/gluon/blocks/custom-layer.html">Custom Layers</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../tutorials/packages/gluon/blocks/custom_layer_beginners.html">Customer Layers (Beginners)</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../tutorials/packages/gluon/blocks/hybridize.html">Hybridize</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../tutorials/packages/gluon/blocks/init.html">Initialization</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../tutorials/packages/gluon/blocks/naming.html">Parameter and Block Naming</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../tutorials/packages/gluon/blocks/nn.html">Layers and Blocks</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../tutorials/packages/gluon/blocks/parameters.html">Parameter Management</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../tutorials/packages/gluon/blocks/save_load_params.html">Saving and Loading Gluon Models</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../tutorials/packages/gluon/blocks/activations/activations.html">Activation Blocks</a></li> |
| </ul> |
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| <li class="toctree-l4"><a class="reference internal" href="../../tutorials/packages/gluon/image/index.html">Image Tutorials</a><ul> |
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| <li class="toctree-l5"><a class="reference internal" href="../../tutorials/packages/gluon/image/mnist.html">Handwritten Digit Recognition</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../tutorials/packages/gluon/image/pretrained_models.html">Using pre-trained models in MXNet</a></li> |
| </ul> |
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| <li class="toctree-l4"><a class="reference internal" href="../../tutorials/packages/gluon/loss/index.html">Losses</a><ul> |
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| <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> |
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| <li class="toctree-l4"><a class="reference internal" href="../../tutorials/packages/gluon/training/index.html">Training</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../tutorials/packages/gluon/training/fit_api_tutorial.html">MXNet Gluon Fit API</a></li> |
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| <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/ndarray/index.html">NDArray</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../tutorials/packages/ndarray/01-ndarray-intro.html">An Intro: Manipulate Data the MXNet Way with NDArray</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../tutorials/packages/ndarray/02-ndarray-operations.html">NDArray Operations</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../tutorials/packages/ndarray/03-ndarray-contexts.html">NDArray Contexts</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../tutorials/packages/ndarray/gotchas_numpy_in_mxnet.html">Gotchas using NumPy in Apache MXNet</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../tutorials/packages/ndarray/sparse/index.html">Tutorials</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../tutorials/packages/ndarray/sparse/csr.html">CSRNDArray - NDArray in Compressed Sparse Row Storage Format</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../tutorials/packages/ndarray/sparse/row_sparse.html">RowSparseNDArray - NDArray for Sparse Gradient Updates</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../tutorials/packages/ndarray/sparse/train.html">Train a Linear Regression Model with Sparse Symbols</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../tutorials/packages/ndarray/sparse/train_gluon.html">Sparse NDArrays with Gluon</a></li> |
| </ul> |
| </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 internal" href="../../tutorials/packages/onnx/super_resolution.html">Importing an ONNX model into MXNet</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/mkldnn/index.html">Intel MKL-DNN</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../tutorials/performance/backend/mkldnn/mkldnn_quantization.html">Quantize with MKL-DNN backend</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../tutorials/performance/backend/mkldnn/mkldnn_readme.html">Install MXNet with MKL-DNN</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l4"><a class="reference internal" href="../../tutorials/performance/backend/tensorrt/index.html">TensorRT</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../tutorials/performance/backend/tensorrt/tensorrt.html">Optimized GPU Inference</a></li> |
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| <li class="toctree-l4"><a class="reference internal" href="../../tutorials/performance/backend/tvm.html">Use TVM</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../tutorials/performance/backend/profiler.html">Profiling MXNet Models</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../tutorials/performance/backend/amp.html">Using AMP: Automatic Mixed Precision</a></li> |
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| <li class="toctree-l2"><a class="reference internal" href="../../tutorials/deploy/index.html">Deployment</a><ul> |
| <li class="toctree-l3"><a class="reference internal" href="../../tutorials/deploy/export/index.html">Export</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../tutorials/deploy/export/onnx.html">Exporting to ONNX format</a></li> |
| <li class="toctree-l4"><a class="reference external" href="https://gluon-cv.mxnet.io/build/examples_deployment/export_network.html">Export Gluon CV Models</a></li> |
| <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> |
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| <li class="toctree-l4"><a class="reference internal" href="../../tutorials/deploy/inference/wine_detector.html">Real-time Object Detection with MXNet On The Raspberry Pi</a></li> |
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| <li class="toctree-l1"><a class="reference internal" href="../../tutorials/index.html">Python Tutorials</a><ul> |
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| <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/1-ndarray.html">Manipulate data with <code class="docutils literal notranslate"><span class="pre">ndarray</span></code></a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/2-nn.html">Create a neural network</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/3-autograd.html">Automatic differentiation with <code class="docutils literal notranslate"><span class="pre">autograd</span></code></a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/4-train.html">Train the neural network</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/5-predict.html">Predict with a pre-trained model</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/6-use_gpus.html">Use GPUs</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l3"><a class="reference internal" href="../../tutorials/getting-started/to-mxnet/index.html">Moving to MXNet from Other Frameworks</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/to-mxnet/pytorch.html">PyTorch vs Apache MXNet</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l3"><a class="reference internal" href="../../tutorials/getting-started/gluon_from_experiment_to_deployment.html">Gluon: from experiment to deployment</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../tutorials/getting-started/logistic_regression_explained.html">Logistic regression explained</a></li> |
| <li class="toctree-l3"><a class="reference external" href="https://mxnet.apache.org/api/python/docs/tutorials/packages/gluon/image/mnist.html">MNIST</a></li> |
| </ul> |
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| <li class="toctree-l2"><a class="reference internal" href="../../tutorials/packages/index.html">Packages</a><ul> |
| <li class="toctree-l3"><a class="reference internal" href="../../tutorials/packages/autograd/index.html">Automatic Differentiation</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../tutorials/packages/gluon/index.html">Gluon</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../tutorials/packages/gluon/blocks/index.html">Blocks</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../tutorials/packages/gluon/blocks/custom-layer.html">Custom Layers</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../tutorials/packages/gluon/blocks/custom_layer_beginners.html">Customer Layers (Beginners)</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../tutorials/packages/gluon/blocks/hybridize.html">Hybridize</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../tutorials/packages/gluon/blocks/init.html">Initialization</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../tutorials/packages/gluon/blocks/naming.html">Parameter and Block Naming</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../tutorials/packages/gluon/blocks/nn.html">Layers and Blocks</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../tutorials/packages/gluon/blocks/parameters.html">Parameter Management</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../tutorials/packages/gluon/blocks/save_load_params.html">Saving and Loading Gluon Models</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../tutorials/packages/gluon/blocks/activations/activations.html">Activation Blocks</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l4"><a class="reference internal" href="../../tutorials/packages/gluon/image/index.html">Image Tutorials</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../tutorials/packages/gluon/image/image-augmentation.html">Image Augmentation</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../tutorials/packages/gluon/image/mnist.html">Handwritten Digit Recognition</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../tutorials/packages/gluon/image/pretrained_models.html">Using pre-trained models in MXNet</a></li> |
| </ul> |
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| <li class="toctree-l4"><a class="reference internal" href="../../tutorials/packages/gluon/loss/index.html">Losses</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../tutorials/packages/gluon/loss/custom-loss.html">Custom Loss Blocks</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../tutorials/packages/gluon/loss/kl_divergence.html">Kullback-Leibler (KL) Divergence</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../tutorials/packages/gluon/loss/loss.html">Loss functions</a></li> |
| </ul> |
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| <li class="toctree-l4"><a class="reference internal" href="../../tutorials/packages/gluon/text/index.html">Text Tutorials</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../tutorials/packages/gluon/text/gnmt.html">Google Neural Machine Translation</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../tutorials/packages/gluon/text/transformer.html">Machine Translation with Transformer</a></li> |
| </ul> |
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| <li class="toctree-l4"><a class="reference internal" href="../../tutorials/packages/gluon/training/index.html">Training</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../tutorials/packages/gluon/training/fit_api_tutorial.html">MXNet Gluon Fit API</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../tutorials/packages/gluon/training/trainer.html">Trainer</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../tutorials/packages/gluon/training/learning_rates/index.html">Learning Rates</a><ul> |
| <li class="toctree-l6"><a class="reference internal" href="../../tutorials/packages/gluon/training/learning_rates/learning_rate_finder.html">Learning Rate Finder</a></li> |
| <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/ndarray/index.html">NDArray</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../tutorials/packages/ndarray/01-ndarray-intro.html">An Intro: Manipulate Data the MXNet Way with NDArray</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../tutorials/packages/ndarray/02-ndarray-operations.html">NDArray Operations</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../tutorials/packages/ndarray/03-ndarray-contexts.html">NDArray Contexts</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../tutorials/packages/ndarray/gotchas_numpy_in_mxnet.html">Gotchas using NumPy in Apache MXNet</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../tutorials/packages/ndarray/sparse/index.html">Tutorials</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../tutorials/packages/ndarray/sparse/csr.html">CSRNDArray - NDArray in Compressed Sparse Row Storage Format</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../tutorials/packages/ndarray/sparse/row_sparse.html">RowSparseNDArray - NDArray for Sparse Gradient Updates</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../tutorials/packages/ndarray/sparse/train.html">Train a Linear Regression Model with Sparse Symbols</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../tutorials/packages/ndarray/sparse/train_gluon.html">Sparse NDArrays with Gluon</a></li> |
| </ul> |
| </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 internal" href="../../tutorials/packages/onnx/super_resolution.html">Importing an ONNX model into MXNet</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> |
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| <div class="section" id="module-mxnet.metric"> |
| <span id="mxnet-metric"></span><h1>mxnet.metric<a class="headerlink" href="#module-mxnet.metric" title="Permalink to this headline">¶</a></h1> |
| <p>Online evaluation metric module.</p> |
| <p><strong>Classes</strong></p> |
| <table class="longtable docutils align-default"> |
| <colgroup> |
| <col style="width: 10%" /> |
| <col style="width: 90%" /> |
| </colgroup> |
| <tbody> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.metric.Accuracy" title="mxnet.metric.Accuracy"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Accuracy</span></code></a>([axis, name, output_names, label_names])</p></td> |
| <td><p>Computes accuracy classification score.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.metric.Caffe" title="mxnet.metric.Caffe"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Caffe</span></code></a>([name, output_names, label_names])</p></td> |
| <td><p>Dummy metric for caffe criterions.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.metric.CompositeEvalMetric" title="mxnet.metric.CompositeEvalMetric"><code class="xref py py-obj docutils literal notranslate"><span class="pre">CompositeEvalMetric</span></code></a>([metrics, name, …])</p></td> |
| <td><p>Manages multiple evaluation metrics.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.metric.CrossEntropy" title="mxnet.metric.CrossEntropy"><code class="xref py py-obj docutils literal notranslate"><span class="pre">CrossEntropy</span></code></a>([eps, name, output_names, …])</p></td> |
| <td><p>Computes Cross Entropy loss.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.metric.CustomMetric" title="mxnet.metric.CustomMetric"><code class="xref py py-obj docutils literal notranslate"><span class="pre">CustomMetric</span></code></a>(feval[, name, …])</p></td> |
| <td><p>Computes a customized evaluation metric.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.metric.EvalMetric" title="mxnet.metric.EvalMetric"><code class="xref py py-obj docutils literal notranslate"><span class="pre">EvalMetric</span></code></a>(name[, output_names, label_names])</p></td> |
| <td><p>Base class for all evaluation metrics.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.metric.F1" title="mxnet.metric.F1"><code class="xref py py-obj docutils literal notranslate"><span class="pre">F1</span></code></a>([name, output_names, label_names, average])</p></td> |
| <td><p>Computes the F1 score of a binary classification problem.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.metric.Loss" title="mxnet.metric.Loss"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Loss</span></code></a>([name, output_names, label_names])</p></td> |
| <td><p>Dummy metric for directly printing loss.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.metric.MAE" title="mxnet.metric.MAE"><code class="xref py py-obj docutils literal notranslate"><span class="pre">MAE</span></code></a>([name, output_names, label_names])</p></td> |
| <td><p>Computes Mean Absolute Error (MAE) loss.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.metric.MCC" title="mxnet.metric.MCC"><code class="xref py py-obj docutils literal notranslate"><span class="pre">MCC</span></code></a>([name, output_names, label_names, average])</p></td> |
| <td><p>Computes the Matthews Correlation Coefficient of a binary classification problem.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.metric.MSE" title="mxnet.metric.MSE"><code class="xref py py-obj docutils literal notranslate"><span class="pre">MSE</span></code></a>([name, output_names, label_names])</p></td> |
| <td><p>Computes Mean Squared Error (MSE) loss.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.metric.NegativeLogLikelihood" title="mxnet.metric.NegativeLogLikelihood"><code class="xref py py-obj docutils literal notranslate"><span class="pre">NegativeLogLikelihood</span></code></a>([eps, name, …])</p></td> |
| <td><p>Computes the negative log-likelihood loss.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.metric.PCC" title="mxnet.metric.PCC"><code class="xref py py-obj docutils literal notranslate"><span class="pre">PCC</span></code></a>([name, output_names, label_names, …])</p></td> |
| <td><p>PCC is a multiclass equivalent for the Matthews correlation coefficient derived from a discrete solution to the Pearson correlation coefficient.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.metric.PearsonCorrelation" title="mxnet.metric.PearsonCorrelation"><code class="xref py py-obj docutils literal notranslate"><span class="pre">PearsonCorrelation</span></code></a>([name, output_names, …])</p></td> |
| <td><p>Computes Pearson correlation.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.metric.Perplexity" title="mxnet.metric.Perplexity"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Perplexity</span></code></a>(ignore_label[, axis, name, …])</p></td> |
| <td><p>Computes perplexity.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.metric.RMSE" title="mxnet.metric.RMSE"><code class="xref py py-obj docutils literal notranslate"><span class="pre">RMSE</span></code></a>([name, output_names, label_names])</p></td> |
| <td><p>Computes Root Mean Squred Error (RMSE) loss.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.metric.TopKAccuracy" title="mxnet.metric.TopKAccuracy"><code class="xref py py-obj docutils literal notranslate"><span class="pre">TopKAccuracy</span></code></a>([top_k, name, output_names, …])</p></td> |
| <td><p>Computes top k predictions accuracy.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.metric.Torch" title="mxnet.metric.Torch"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Torch</span></code></a>([name, output_names, label_names])</p></td> |
| <td><p>Dummy metric for torch criterions.</p></td> |
| </tr> |
| </tbody> |
| </table> |
| <p><strong>Functions</strong></p> |
| <table class="longtable docutils align-default"> |
| <colgroup> |
| <col style="width: 10%" /> |
| <col style="width: 90%" /> |
| </colgroup> |
| <tbody> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.metric.check_label_shapes" title="mxnet.metric.check_label_shapes"><code class="xref py py-obj docutils literal notranslate"><span class="pre">check_label_shapes</span></code></a>(labels, preds[, wrap, shape])</p></td> |
| <td><p>Helper function for checking shape of label and prediction</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.metric.create" title="mxnet.metric.create"><code class="xref py py-obj docutils literal notranslate"><span class="pre">create</span></code></a>(metric, *args, **kwargs)</p></td> |
| <td><p>Creates evaluation metric from metric names or instances of EvalMetric or a custom metric function.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.metric.np" title="mxnet.metric.np"><code class="xref py py-obj docutils literal notranslate"><span class="pre">np</span></code></a>(numpy_feval[, name, allow_extra_outputs])</p></td> |
| <td><p>Creates a custom evaluation metric that receives its inputs as numpy arrays.</p></td> |
| </tr> |
| </tbody> |
| </table> |
| <dl class="class"> |
| <dt id="mxnet.metric.Accuracy"> |
| <em class="property">class </em><code class="sig-prename descclassname">mxnet.metric.</code><code class="sig-name descname">Accuracy</code><span class="sig-paren">(</span><em class="sig-param">axis=1</em>, <em class="sig-param">name='accuracy'</em>, <em class="sig-param">output_names=None</em>, <em class="sig-param">label_names=None</em><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/mxnet/metric.html#Accuracy"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.metric.Accuracy" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Bases: <a class="reference internal" href="#mxnet.metric.EvalMetric" title="mxnet.metric.EvalMetric"><code class="xref py py-class docutils literal notranslate"><span class="pre">mxnet.metric.EvalMetric</span></code></a></p> |
| <p>Computes accuracy classification score.</p> |
| <p>The accuracy score is defined as</p> |
| <div class="math notranslate nohighlight"> |
| \[\text{accuracy}(y, \hat{y}) = \frac{1}{n} \sum_{i=0}^{n-1} |
| \text{1}(\hat{y_i} == y_i)\]</div> |
| <p><strong>Methods</strong></p> |
| <table class="longtable docutils align-default"> |
| <colgroup> |
| <col style="width: 10%" /> |
| <col style="width: 90%" /> |
| </colgroup> |
| <tbody> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.metric.Accuracy.update" title="mxnet.metric.Accuracy.update"><code class="xref py py-obj docutils literal notranslate"><span class="pre">update</span></code></a>(labels, preds)</p></td> |
| <td><p>Updates the internal evaluation result.</p></td> |
| </tr> |
| </tbody> |
| </table> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>axis</strong> (<em>int</em><em>, </em><em>default=1</em>) – The axis that represents classes</p></li> |
| <li><p><strong>name</strong> (<em>str</em>) – Name of this metric instance for display.</p></li> |
| <li><p><strong>output_names</strong> (<em>list of str</em><em>, or </em><em>None</em>) – Name of predictions that should be used when updating with update_dict. |
| By default include all predictions.</p></li> |
| <li><p><strong>label_names</strong> (<em>list of str</em><em>, or </em><em>None</em>) – Name of labels that should be used when updating with update_dict. |
| By default include all labels.</p></li> |
| </ul> |
| </dd> |
| </dl> |
| <p class="rubric">Examples</p> |
| <div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">predicts</span> <span class="o">=</span> <span class="p">[</span><span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">array</span><span class="p">([[</span><span class="mf">0.3</span><span class="p">,</span> <span class="mf">0.7</span><span class="p">],</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mf">1.</span><span class="p">],</span> <span class="p">[</span><span class="mf">0.4</span><span class="p">,</span> <span class="mf">0.6</span><span class="p">]])]</span> |
| <span class="gp">>>> </span><span class="n">labels</span> <span class="o">=</span> <span class="p">[</span><span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">])]</span> |
| <span class="gp">>>> </span><span class="n">acc</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">metric</span><span class="o">.</span><span class="n">Accuracy</span><span class="p">()</span> |
| <span class="gp">>>> </span><span class="n">acc</span><span class="o">.</span><span class="n">update</span><span class="p">(</span><span class="n">preds</span> <span class="o">=</span> <span class="n">predicts</span><span class="p">,</span> <span class="n">labels</span> <span class="o">=</span> <span class="n">labels</span><span class="p">)</span> |
| <span class="gp">>>> </span><span class="nb">print</span> <span class="n">acc</span><span class="o">.</span><span class="n">get</span><span class="p">()</span> |
| <span class="go">('accuracy', 0.6666666666666666)</span> |
| </pre></div> |
| </div> |
| <dl class="method"> |
| <dt id="mxnet.metric.Accuracy.update"> |
| <code class="sig-name descname">update</code><span class="sig-paren">(</span><em class="sig-param">labels</em>, <em class="sig-param">preds</em><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/mxnet/metric.html#Accuracy.update"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.metric.Accuracy.update" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Updates the internal evaluation result.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>labels</strong> (list of <cite>NDArray</cite>) – The labels of the data with class indices as values, one per sample.</p></li> |
| <li><p><strong>preds</strong> (list of <cite>NDArray</cite>) – Prediction values for samples. Each prediction value can either be the class index, |
| or a vector of likelihoods for all classes.</p></li> |
| </ul> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| </dd></dl> |
| |
| <dl class="class"> |
| <dt id="mxnet.metric.Caffe"> |
| <em class="property">class </em><code class="sig-prename descclassname">mxnet.metric.</code><code class="sig-name descname">Caffe</code><span class="sig-paren">(</span><em class="sig-param">name='caffe'</em>, <em class="sig-param">output_names=None</em>, <em class="sig-param">label_names=None</em><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/mxnet/metric.html#Caffe"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.metric.Caffe" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Bases: <a class="reference internal" href="#mxnet.metric.Loss" title="mxnet.metric.Loss"><code class="xref py py-class docutils literal notranslate"><span class="pre">mxnet.metric.Loss</span></code></a></p> |
| <p>Dummy metric for caffe criterions.</p> |
| </dd></dl> |
| |
| <dl class="class"> |
| <dt id="mxnet.metric.CompositeEvalMetric"> |
| <em class="property">class </em><code class="sig-prename descclassname">mxnet.metric.</code><code class="sig-name descname">CompositeEvalMetric</code><span class="sig-paren">(</span><em class="sig-param">metrics=None</em>, <em class="sig-param">name='composite'</em>, <em class="sig-param">output_names=None</em>, <em class="sig-param">label_names=None</em><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/mxnet/metric.html#CompositeEvalMetric"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.metric.CompositeEvalMetric" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Bases: <a class="reference internal" href="#mxnet.metric.EvalMetric" title="mxnet.metric.EvalMetric"><code class="xref py py-class docutils literal notranslate"><span class="pre">mxnet.metric.EvalMetric</span></code></a></p> |
| <p>Manages multiple evaluation metrics.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>metrics</strong> (<em>list of EvalMetric</em>) – List of child metrics.</p></li> |
| <li><p><strong>name</strong> (<em>str</em>) – Name of this metric instance for display.</p></li> |
| <li><p><strong>output_names</strong> (<em>list of str</em><em>, or </em><em>None</em>) – Name of predictions that should be used when updating with update_dict. |
| By default include all predictions.</p></li> |
| <li><p><strong>label_names</strong> (<em>list of str</em><em>, or </em><em>None</em>) – Name of labels that should be used when updating with update_dict. |
| By default include all labels.</p></li> |
| </ul> |
| </dd> |
| </dl> |
| <p><strong>Methods</strong></p> |
| <table class="longtable docutils align-default"> |
| <colgroup> |
| <col style="width: 10%" /> |
| <col style="width: 90%" /> |
| </colgroup> |
| <tbody> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.metric.CompositeEvalMetric.add" title="mxnet.metric.CompositeEvalMetric.add"><code class="xref py py-obj docutils literal notranslate"><span class="pre">add</span></code></a>(metric)</p></td> |
| <td><p>Adds a child metric.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.metric.CompositeEvalMetric.get" title="mxnet.metric.CompositeEvalMetric.get"><code class="xref py py-obj docutils literal notranslate"><span class="pre">get</span></code></a>()</p></td> |
| <td><p>Returns the current evaluation result.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.metric.CompositeEvalMetric.get_config" title="mxnet.metric.CompositeEvalMetric.get_config"><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_config</span></code></a>()</p></td> |
| <td><p>Save configurations of metric.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.metric.CompositeEvalMetric.get_global" title="mxnet.metric.CompositeEvalMetric.get_global"><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_global</span></code></a>()</p></td> |
| <td><p>Returns the current evaluation result.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.metric.CompositeEvalMetric.get_metric" title="mxnet.metric.CompositeEvalMetric.get_metric"><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_metric</span></code></a>(index)</p></td> |
| <td><p>Returns a child metric.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.metric.CompositeEvalMetric.reset" title="mxnet.metric.CompositeEvalMetric.reset"><code class="xref py py-obj docutils literal notranslate"><span class="pre">reset</span></code></a>()</p></td> |
| <td><p>Resets the internal evaluation result to initial state.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.metric.CompositeEvalMetric.reset_local" title="mxnet.metric.CompositeEvalMetric.reset_local"><code class="xref py py-obj docutils literal notranslate"><span class="pre">reset_local</span></code></a>()</p></td> |
| <td><p>Resets the local portion of the internal evaluation results to initial state.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.metric.CompositeEvalMetric.update" title="mxnet.metric.CompositeEvalMetric.update"><code class="xref py py-obj docutils literal notranslate"><span class="pre">update</span></code></a>(labels, preds)</p></td> |
| <td><p>Updates the internal evaluation result.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.metric.CompositeEvalMetric.update_dict" title="mxnet.metric.CompositeEvalMetric.update_dict"><code class="xref py py-obj docutils literal notranslate"><span class="pre">update_dict</span></code></a>(labels, preds)</p></td> |
| <td><p>Update the internal evaluation with named label and pred</p></td> |
| </tr> |
| </tbody> |
| </table> |
| <p class="rubric">Examples</p> |
| <div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">predicts</span> <span class="o">=</span> <span class="p">[</span><span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">array</span><span class="p">([[</span><span class="mf">0.3</span><span class="p">,</span> <span class="mf">0.7</span><span class="p">],</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mf">1.</span><span class="p">],</span> <span class="p">[</span><span class="mf">0.4</span><span class="p">,</span> <span class="mf">0.6</span><span class="p">]])]</span> |
| <span class="gp">>>> </span><span class="n">labels</span> <span class="o">=</span> <span class="p">[</span><span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">])]</span> |
| <span class="gp">>>> </span><span class="n">eval_metrics_1</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">metric</span><span class="o">.</span><span class="n">Accuracy</span><span class="p">()</span> |
| <span class="gp">>>> </span><span class="n">eval_metrics_2</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">metric</span><span class="o">.</span><span class="n">F1</span><span class="p">()</span> |
| <span class="gp">>>> </span><span class="n">eval_metrics</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">metric</span><span class="o">.</span><span class="n">CompositeEvalMetric</span><span class="p">()</span> |
| <span class="gp">>>> </span><span class="k">for</span> <span class="n">child_metric</span> <span class="ow">in</span> <span class="p">[</span><span class="n">eval_metrics_1</span><span class="p">,</span> <span class="n">eval_metrics_2</span><span class="p">]:</span> |
| <span class="gp">>>> </span> <span class="n">eval_metrics</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">child_metric</span><span class="p">)</span> |
| <span class="gp">>>> </span><span class="n">eval_metrics</span><span class="o">.</span><span class="n">update</span><span class="p">(</span><span class="n">labels</span> <span class="o">=</span> <span class="n">labels</span><span class="p">,</span> <span class="n">preds</span> <span class="o">=</span> <span class="n">predicts</span><span class="p">)</span> |
| <span class="gp">>>> </span><span class="nb">print</span> <span class="n">eval_metrics</span><span class="o">.</span><span class="n">get</span><span class="p">()</span> |
| <span class="go">(['accuracy', 'f1'], [0.6666666666666666, 0.8])</span> |
| </pre></div> |
| </div> |
| <dl class="method"> |
| <dt id="mxnet.metric.CompositeEvalMetric.add"> |
| <code class="sig-name descname">add</code><span class="sig-paren">(</span><em class="sig-param">metric</em><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/mxnet/metric.html#CompositeEvalMetric.add"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.metric.CompositeEvalMetric.add" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Adds a child metric.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><p><strong>metric</strong> – A metric instance.</p> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.metric.CompositeEvalMetric.get"> |
| <code class="sig-name descname">get</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/mxnet/metric.html#CompositeEvalMetric.get"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.metric.CompositeEvalMetric.get" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Returns the current evaluation result.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Returns</dt> |
| <dd class="field-odd"><p><ul class="simple"> |
| <li><p><strong>names</strong> (<em>list of str</em>) – Name of the metrics.</p></li> |
| <li><p><strong>values</strong> (<em>list of float</em>) – Value of the evaluations.</p></li> |
| </ul> |
| </p> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.metric.CompositeEvalMetric.get_config"> |
| <code class="sig-name descname">get_config</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/mxnet/metric.html#CompositeEvalMetric.get_config"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.metric.CompositeEvalMetric.get_config" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Save configurations of metric. Can be recreated |
| from configs with metric.create(<code class="docutils literal notranslate"><span class="pre">**config</span></code>)</p> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.metric.CompositeEvalMetric.get_global"> |
| <code class="sig-name descname">get_global</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/mxnet/metric.html#CompositeEvalMetric.get_global"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.metric.CompositeEvalMetric.get_global" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Returns the current evaluation result.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Returns</dt> |
| <dd class="field-odd"><p><ul class="simple"> |
| <li><p><strong>names</strong> (<em>list of str</em>) – Name of the metrics.</p></li> |
| <li><p><strong>values</strong> (<em>list of float</em>) – Value of the evaluations.</p></li> |
| </ul> |
| </p> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.metric.CompositeEvalMetric.get_metric"> |
| <code class="sig-name descname">get_metric</code><span class="sig-paren">(</span><em class="sig-param">index</em><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/mxnet/metric.html#CompositeEvalMetric.get_metric"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.metric.CompositeEvalMetric.get_metric" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Returns a child metric.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><p><strong>index</strong> (<em>int</em>) – Index of child metric in the list of metrics.</p> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.metric.CompositeEvalMetric.reset"> |
| <code class="sig-name descname">reset</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/mxnet/metric.html#CompositeEvalMetric.reset"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.metric.CompositeEvalMetric.reset" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Resets the internal evaluation result to initial state.</p> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.metric.CompositeEvalMetric.reset_local"> |
| <code class="sig-name descname">reset_local</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/mxnet/metric.html#CompositeEvalMetric.reset_local"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.metric.CompositeEvalMetric.reset_local" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Resets the local portion of the internal evaluation results to initial state.</p> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.metric.CompositeEvalMetric.update"> |
| <code class="sig-name descname">update</code><span class="sig-paren">(</span><em class="sig-param">labels</em>, <em class="sig-param">preds</em><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/mxnet/metric.html#CompositeEvalMetric.update"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.metric.CompositeEvalMetric.update" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Updates the internal evaluation result.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>labels</strong> (list of <cite>NDArray</cite>) – The labels of the data.</p></li> |
| <li><p><strong>preds</strong> (list of <cite>NDArray</cite>) – Predicted values.</p></li> |
| </ul> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.metric.CompositeEvalMetric.update_dict"> |
| <code class="sig-name descname">update_dict</code><span class="sig-paren">(</span><em class="sig-param">labels</em>, <em class="sig-param">preds</em><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/mxnet/metric.html#CompositeEvalMetric.update_dict"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.metric.CompositeEvalMetric.update_dict" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Update the internal evaluation with named label and pred</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>labels</strong> (<em>OrderedDict of str -> NDArray</em>) – name to array mapping for labels.</p></li> |
| <li><p><strong>preds</strong> (<em>OrderedDict of str -> NDArray</em>) – name to array mapping of predicted outputs.</p></li> |
| </ul> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| </dd></dl> |
| |
| <dl class="class"> |
| <dt id="mxnet.metric.CrossEntropy"> |
| <em class="property">class </em><code class="sig-prename descclassname">mxnet.metric.</code><code class="sig-name descname">CrossEntropy</code><span class="sig-paren">(</span><em class="sig-param">eps=1e-12</em>, <em class="sig-param">name='cross-entropy'</em>, <em class="sig-param">output_names=None</em>, <em class="sig-param">label_names=None</em><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/mxnet/metric.html#CrossEntropy"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.metric.CrossEntropy" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Bases: <a class="reference internal" href="#mxnet.metric.EvalMetric" title="mxnet.metric.EvalMetric"><code class="xref py py-class docutils literal notranslate"><span class="pre">mxnet.metric.EvalMetric</span></code></a></p> |
| <p>Computes Cross Entropy loss.</p> |
| <p>The cross entropy over a batch of sample size <span class="math notranslate nohighlight">\(N\)</span> is given by</p> |
| <div class="math notranslate nohighlight"> |
| \[-\sum_{n=1}^{N}\sum_{k=1}^{K}t_{nk}\log (y_{nk}),\]</div> |
| <p><strong>Methods</strong></p> |
| <table class="longtable docutils align-default"> |
| <colgroup> |
| <col style="width: 10%" /> |
| <col style="width: 90%" /> |
| </colgroup> |
| <tbody> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.metric.CrossEntropy.update" title="mxnet.metric.CrossEntropy.update"><code class="xref py py-obj docutils literal notranslate"><span class="pre">update</span></code></a>(labels, preds)</p></td> |
| <td><p>Updates the internal evaluation result.</p></td> |
| </tr> |
| </tbody> |
| </table> |
| <p>where <span class="math notranslate nohighlight">\(t_{nk}=1\)</span> if and only if sample <span class="math notranslate nohighlight">\(n\)</span> belongs to class <span class="math notranslate nohighlight">\(k\)</span>. |
| <span class="math notranslate nohighlight">\(y_{nk}\)</span> denotes the probability of sample <span class="math notranslate nohighlight">\(n\)</span> belonging to |
| class <span class="math notranslate nohighlight">\(k\)</span>.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>eps</strong> (<em>float</em>) – Cross Entropy loss is undefined for predicted value is 0 or 1, |
| so predicted values are added with the small constant.</p></li> |
| <li><p><strong>name</strong> (<em>str</em>) – Name of this metric instance for display.</p></li> |
| <li><p><strong>output_names</strong> (<em>list of str</em><em>, or </em><em>None</em>) – Name of predictions that should be used when updating with update_dict. |
| By default include all predictions.</p></li> |
| <li><p><strong>label_names</strong> (<em>list of str</em><em>, or </em><em>None</em>) – Name of labels that should be used when updating with update_dict. |
| By default include all labels.</p></li> |
| </ul> |
| </dd> |
| </dl> |
| <p class="rubric">Examples</p> |
| <div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">predicts</span> <span class="o">=</span> <span class="p">[</span><span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">array</span><span class="p">([[</span><span class="mf">0.3</span><span class="p">,</span> <span class="mf">0.7</span><span class="p">],</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mf">1.</span><span class="p">],</span> <span class="p">[</span><span class="mf">0.4</span><span class="p">,</span> <span class="mf">0.6</span><span class="p">]])]</span> |
| <span class="gp">>>> </span><span class="n">labels</span> <span class="o">=</span> <span class="p">[</span><span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">])]</span> |
| <span class="gp">>>> </span><span class="n">ce</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">metric</span><span class="o">.</span><span class="n">CrossEntropy</span><span class="p">()</span> |
| <span class="gp">>>> </span><span class="n">ce</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">predicts</span><span class="p">)</span> |
| <span class="gp">>>> </span><span class="nb">print</span> <span class="n">ce</span><span class="o">.</span><span class="n">get</span><span class="p">()</span> |
| <span class="go">('cross-entropy', 0.57159948348999023)</span> |
| </pre></div> |
| </div> |
| <dl class="method"> |
| <dt id="mxnet.metric.CrossEntropy.update"> |
| <code class="sig-name descname">update</code><span class="sig-paren">(</span><em class="sig-param">labels</em>, <em class="sig-param">preds</em><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/mxnet/metric.html#CrossEntropy.update"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.metric.CrossEntropy.update" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Updates the internal evaluation result.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>labels</strong> (list of <cite>NDArray</cite>) – The labels of the data.</p></li> |
| <li><p><strong>preds</strong> (list of <cite>NDArray</cite>) – Predicted values.</p></li> |
| </ul> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| </dd></dl> |
| |
| <dl class="class"> |
| <dt id="mxnet.metric.CustomMetric"> |
| <em class="property">class </em><code class="sig-prename descclassname">mxnet.metric.</code><code class="sig-name descname">CustomMetric</code><span class="sig-paren">(</span><em class="sig-param">feval</em>, <em class="sig-param">name=None</em>, <em class="sig-param">allow_extra_outputs=False</em>, <em class="sig-param">output_names=None</em>, <em class="sig-param">label_names=None</em><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/mxnet/metric.html#CustomMetric"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.metric.CustomMetric" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Bases: <a class="reference internal" href="#mxnet.metric.EvalMetric" title="mxnet.metric.EvalMetric"><code class="xref py py-class docutils literal notranslate"><span class="pre">mxnet.metric.EvalMetric</span></code></a></p> |
| <p>Computes a customized evaluation metric.</p> |
| <p>The <cite>feval</cite> function can return a <cite>tuple</cite> of (sum_metric, num_inst) or return |
| an <cite>int</cite> sum_metric.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>feval</strong> (<em>callable</em><em>(</em><em>label</em><em>, </em><em>pred</em><em>)</em>) – Customized evaluation function.</p></li> |
| <li><p><strong>name</strong> (<em>str</em>) – The name of the metric. (the default is None).</p></li> |
| <li><p><strong>allow_extra_outputs</strong> (<em>bool</em><em>, </em><em>optional</em>) – If true, the prediction outputs can have extra outputs. |
| This is useful in RNN, where the states are also produced |
| in outputs for forwarding. (the default is False).</p></li> |
| <li><p><strong>name</strong> – Name of this metric instance for display.</p></li> |
| <li><p><strong>output_names</strong> (<em>list of str</em><em>, or </em><em>None</em>) – Name of predictions that should be used when updating with update_dict. |
| By default include all predictions.</p></li> |
| <li><p><strong>label_names</strong> (<em>list of str</em><em>, or </em><em>None</em>) – Name of labels that should be used when updating with update_dict. |
| By default include all labels.</p></li> |
| </ul> |
| </dd> |
| </dl> |
| <p><strong>Methods</strong></p> |
| <table class="longtable docutils align-default"> |
| <colgroup> |
| <col style="width: 10%" /> |
| <col style="width: 90%" /> |
| </colgroup> |
| <tbody> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.metric.CustomMetric.get_config" title="mxnet.metric.CustomMetric.get_config"><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_config</span></code></a>()</p></td> |
| <td><p>Save configurations of metric.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.metric.CustomMetric.update" title="mxnet.metric.CustomMetric.update"><code class="xref py py-obj docutils literal notranslate"><span class="pre">update</span></code></a>(labels, preds)</p></td> |
| <td><p>Updates the internal evaluation result.</p></td> |
| </tr> |
| </tbody> |
| </table> |
| <p class="rubric">Examples</p> |
| <div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">predicts</span> <span class="o">=</span> <span class="p">[</span><span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="mi">3</span><span class="p">,</span> <span class="o">-</span><span class="mf">0.5</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">7</span><span class="p">])</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="mi">4</span><span class="p">,</span><span class="mi">1</span><span class="p">))]</span> |
| <span class="gp">>>> </span><span class="n">labels</span> <span class="o">=</span> <span class="p">[</span><span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="mf">2.5</span><span class="p">,</span> <span class="mf">0.0</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">8</span><span class="p">])</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="mi">4</span><span class="p">,</span><span class="mi">1</span><span class="p">))]</span> |
| <span class="gp">>>> </span><span class="n">feval</span> <span class="o">=</span> <span class="k">lambda</span> <span class="n">x</span><span class="p">,</span> <span class="n">y</span> <span class="p">:</span> <span class="p">(</span><span class="n">x</span> <span class="o">+</span> <span class="n">y</span><span class="p">)</span><span class="o">.</span><span class="n">mean</span><span class="p">()</span> |
| <span class="gp">>>> </span><span class="n">eval_metrics</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">metric</span><span class="o">.</span><span class="n">CustomMetric</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="gp">>>> </span><span class="n">eval_metrics</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">predicts</span><span class="p">)</span> |
| <span class="gp">>>> </span><span class="nb">print</span> <span class="n">eval_metrics</span><span class="o">.</span><span class="n">get</span><span class="p">()</span> |
| <span class="go">('custom(<lambda>)', 6.0)</span> |
| </pre></div> |
| </div> |
| <dl class="method"> |
| <dt id="mxnet.metric.CustomMetric.get_config"> |
| <code class="sig-name descname">get_config</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/mxnet/metric.html#CustomMetric.get_config"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.metric.CustomMetric.get_config" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Save configurations of metric. Can be recreated |
| from configs with metric.create(<code class="docutils literal notranslate"><span class="pre">**config</span></code>)</p> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.metric.CustomMetric.update"> |
| <code class="sig-name descname">update</code><span class="sig-paren">(</span><em class="sig-param">labels</em>, <em class="sig-param">preds</em><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/mxnet/metric.html#CustomMetric.update"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.metric.CustomMetric.update" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Updates the internal evaluation result.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>labels</strong> (list of <cite>NDArray</cite>) – The labels of the data.</p></li> |
| <li><p><strong>preds</strong> (list of <cite>NDArray</cite>) – Predicted values.</p></li> |
| </ul> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| </dd></dl> |
| |
| <dl class="class"> |
| <dt id="mxnet.metric.EvalMetric"> |
| <em class="property">class </em><code class="sig-prename descclassname">mxnet.metric.</code><code class="sig-name descname">EvalMetric</code><span class="sig-paren">(</span><em class="sig-param">name</em>, <em class="sig-param">output_names=None</em>, <em class="sig-param">label_names=None</em>, <em class="sig-param">**kwargs</em><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/mxnet/metric.html#EvalMetric"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.metric.EvalMetric" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">object</span></code></p> |
| <p>Base class for all evaluation metrics.</p> |
| <div class="admonition note"> |
| <p class="admonition-title">Note</p> |
| <p>This is a base class that provides common metric interfaces. |
| One should not use this class directly, but instead create new metric |
| classes that extend it.</p> |
| </div> |
| <p><strong>Methods</strong></p> |
| <table class="longtable docutils align-default"> |
| <colgroup> |
| <col style="width: 10%" /> |
| <col style="width: 90%" /> |
| </colgroup> |
| <tbody> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.metric.EvalMetric.get" title="mxnet.metric.EvalMetric.get"><code class="xref py py-obj docutils literal notranslate"><span class="pre">get</span></code></a>()</p></td> |
| <td><p>Gets the current evaluation result.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.metric.EvalMetric.get_config" title="mxnet.metric.EvalMetric.get_config"><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_config</span></code></a>()</p></td> |
| <td><p>Save configurations of metric.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.metric.EvalMetric.get_global" title="mxnet.metric.EvalMetric.get_global"><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_global</span></code></a>()</p></td> |
| <td><p>Gets the current global evaluation result.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.metric.EvalMetric.get_global_name_value" title="mxnet.metric.EvalMetric.get_global_name_value"><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_global_name_value</span></code></a>()</p></td> |
| <td><p>Returns zipped name and value pairs for global results.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.metric.EvalMetric.get_name_value" title="mxnet.metric.EvalMetric.get_name_value"><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_name_value</span></code></a>()</p></td> |
| <td><p>Returns zipped name and value pairs.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.metric.EvalMetric.reset" title="mxnet.metric.EvalMetric.reset"><code class="xref py py-obj docutils literal notranslate"><span class="pre">reset</span></code></a>()</p></td> |
| <td><p>Resets the internal evaluation result to initial state.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.metric.EvalMetric.reset_local" title="mxnet.metric.EvalMetric.reset_local"><code class="xref py py-obj docutils literal notranslate"><span class="pre">reset_local</span></code></a>()</p></td> |
| <td><p>Resets the local portion of the internal evaluation results to initial state.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.metric.EvalMetric.update" title="mxnet.metric.EvalMetric.update"><code class="xref py py-obj docutils literal notranslate"><span class="pre">update</span></code></a>(labels, preds)</p></td> |
| <td><p>Updates the internal evaluation result.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.metric.EvalMetric.update_dict" title="mxnet.metric.EvalMetric.update_dict"><code class="xref py py-obj docutils literal notranslate"><span class="pre">update_dict</span></code></a>(label, pred)</p></td> |
| <td><p>Update the internal evaluation with named label and pred</p></td> |
| </tr> |
| </tbody> |
| </table> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>name</strong> (<em>str</em>) – Name of this metric instance for display.</p></li> |
| <li><p><strong>output_names</strong> (<em>list of str</em><em>, or </em><em>None</em>) – Name of predictions that should be used when updating with update_dict. |
| By default include all predictions.</p></li> |
| <li><p><strong>label_names</strong> (<em>list of str</em><em>, or </em><em>None</em>) – Name of labels that should be used when updating with update_dict. |
| By default include all labels.</p></li> |
| </ul> |
| </dd> |
| </dl> |
| <dl class="method"> |
| <dt id="mxnet.metric.EvalMetric.get"> |
| <code class="sig-name descname">get</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/mxnet/metric.html#EvalMetric.get"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.metric.EvalMetric.get" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Gets the current evaluation result.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Returns</dt> |
| <dd class="field-odd"><p><ul class="simple"> |
| <li><p><strong>names</strong> (<em>list of str</em>) – Name of the metrics.</p></li> |
| <li><p><strong>values</strong> (<em>list of float</em>) – Value of the evaluations.</p></li> |
| </ul> |
| </p> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.metric.EvalMetric.get_config"> |
| <code class="sig-name descname">get_config</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/mxnet/metric.html#EvalMetric.get_config"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.metric.EvalMetric.get_config" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Save configurations of metric. Can be recreated |
| from configs with metric.create(<code class="docutils literal notranslate"><span class="pre">**config</span></code>)</p> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.metric.EvalMetric.get_global"> |
| <code class="sig-name descname">get_global</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/mxnet/metric.html#EvalMetric.get_global"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.metric.EvalMetric.get_global" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Gets the current global evaluation result.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Returns</dt> |
| <dd class="field-odd"><p><ul class="simple"> |
| <li><p><strong>names</strong> (<em>list of str</em>) – Name of the metrics.</p></li> |
| <li><p><strong>values</strong> (<em>list of float</em>) – Value of the evaluations.</p></li> |
| </ul> |
| </p> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.metric.EvalMetric.get_global_name_value"> |
| <code class="sig-name descname">get_global_name_value</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/mxnet/metric.html#EvalMetric.get_global_name_value"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.metric.EvalMetric.get_global_name_value" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Returns zipped name and value pairs for global results.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Returns</dt> |
| <dd class="field-odd"><p>A (name, value) tuple list.</p> |
| </dd> |
| <dt class="field-even">Return type</dt> |
| <dd class="field-even"><p>list of tuples</p> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.metric.EvalMetric.get_name_value"> |
| <code class="sig-name descname">get_name_value</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/mxnet/metric.html#EvalMetric.get_name_value"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.metric.EvalMetric.get_name_value" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Returns zipped name and value pairs.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Returns</dt> |
| <dd class="field-odd"><p>A (name, value) tuple list.</p> |
| </dd> |
| <dt class="field-even">Return type</dt> |
| <dd class="field-even"><p>list of tuples</p> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.metric.EvalMetric.reset"> |
| <code class="sig-name descname">reset</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/mxnet/metric.html#EvalMetric.reset"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.metric.EvalMetric.reset" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Resets the internal evaluation result to initial state.</p> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.metric.EvalMetric.reset_local"> |
| <code class="sig-name descname">reset_local</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/mxnet/metric.html#EvalMetric.reset_local"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.metric.EvalMetric.reset_local" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Resets the local portion of the internal evaluation results to initial state.</p> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.metric.EvalMetric.update"> |
| <code class="sig-name descname">update</code><span class="sig-paren">(</span><em class="sig-param">labels</em>, <em class="sig-param">preds</em><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/mxnet/metric.html#EvalMetric.update"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.metric.EvalMetric.update" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Updates the internal evaluation result.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>labels</strong> (list of <cite>NDArray</cite>) – The labels of the data.</p></li> |
| <li><p><strong>preds</strong> (list of <cite>NDArray</cite>) – Predicted values.</p></li> |
| </ul> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.metric.EvalMetric.update_dict"> |
| <code class="sig-name descname">update_dict</code><span class="sig-paren">(</span><em class="sig-param">label</em>, <em class="sig-param">pred</em><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/mxnet/metric.html#EvalMetric.update_dict"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.metric.EvalMetric.update_dict" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Update the internal evaluation with named label and pred</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>labels</strong> (<em>OrderedDict of str -> NDArray</em>) – name to array mapping for labels.</p></li> |
| <li><p><strong>preds</strong> (<em>OrderedDict of str -> NDArray</em>) – name to array mapping of predicted outputs.</p></li> |
| </ul> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| </dd></dl> |
| |
| <dl class="class"> |
| <dt id="mxnet.metric.F1"> |
| <em class="property">class </em><code class="sig-prename descclassname">mxnet.metric.</code><code class="sig-name descname">F1</code><span class="sig-paren">(</span><em class="sig-param">name='f1'</em>, <em class="sig-param">output_names=None</em>, <em class="sig-param">label_names=None</em>, <em class="sig-param">average='macro'</em><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/mxnet/metric.html#F1"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.metric.F1" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Bases: <a class="reference internal" href="#mxnet.metric.EvalMetric" title="mxnet.metric.EvalMetric"><code class="xref py py-class docutils literal notranslate"><span class="pre">mxnet.metric.EvalMetric</span></code></a></p> |
| <p>Computes the F1 score of a binary classification problem.</p> |
| <p>The F1 score is equivalent to harmonic mean of the precision and recall, |
| where the best value is 1.0 and the worst value is 0.0. The formula for F1 score is:</p> |
| <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">F1</span> <span class="o">=</span> <span class="mi">2</span> <span class="o">*</span> <span class="p">(</span><span class="n">precision</span> <span class="o">*</span> <span class="n">recall</span><span class="p">)</span> <span class="o">/</span> <span class="p">(</span><span class="n">precision</span> <span class="o">+</span> <span class="n">recall</span><span class="p">)</span> |
| </pre></div> |
| </div> |
| <p><strong>Methods</strong></p> |
| <table class="longtable docutils align-default"> |
| <colgroup> |
| <col style="width: 10%" /> |
| <col style="width: 90%" /> |
| </colgroup> |
| <tbody> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.metric.F1.reset" title="mxnet.metric.F1.reset"><code class="xref py py-obj docutils literal notranslate"><span class="pre">reset</span></code></a>()</p></td> |
| <td><p>Resets the internal evaluation result to initial state.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.metric.F1.reset_local" title="mxnet.metric.F1.reset_local"><code class="xref py py-obj docutils literal notranslate"><span class="pre">reset_local</span></code></a>()</p></td> |
| <td><p>Resets the internal evaluation result to initial state.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.metric.F1.update" title="mxnet.metric.F1.update"><code class="xref py py-obj docutils literal notranslate"><span class="pre">update</span></code></a>(labels, preds)</p></td> |
| <td><p>Updates the internal evaluation result.</p></td> |
| </tr> |
| </tbody> |
| </table> |
| <p>The formula for precision and recall is:</p> |
| <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">precision</span> <span class="o">=</span> <span class="n">true_positives</span> <span class="o">/</span> <span class="p">(</span><span class="n">true_positives</span> <span class="o">+</span> <span class="n">false_positives</span><span class="p">)</span> |
| <span class="n">recall</span> <span class="o">=</span> <span class="n">true_positives</span> <span class="o">/</span> <span class="p">(</span><span class="n">true_positives</span> <span class="o">+</span> <span class="n">false_negatives</span><span class="p">)</span> |
| </pre></div> |
| </div> |
| <div class="admonition note"> |
| <p class="admonition-title">Note</p> |
| <p>This F1 score only supports binary classification.</p> |
| </div> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>name</strong> (<em>str</em>) – Name of this metric instance for display.</p></li> |
| <li><p><strong>output_names</strong> (<em>list of str</em><em>, or </em><em>None</em>) – Name of predictions that should be used when updating with update_dict. |
| By default include all predictions.</p></li> |
| <li><p><strong>label_names</strong> (<em>list of str</em><em>, or </em><em>None</em>) – Name of labels that should be used when updating with update_dict. |
| By default include all labels.</p></li> |
| <li><p><strong>average</strong> (<em>str</em><em>, </em><em>default 'macro'</em>) – <dl class="simple"> |
| <dt>Strategy to be used for aggregating across mini-batches.</dt><dd><p>”macro”: average the F1 scores for each batch. |
| “micro”: compute a single F1 score across all batches.</p> |
| </dd> |
| </dl> |
| </p></li> |
| </ul> |
| </dd> |
| </dl> |
| <p class="rubric">Examples</p> |
| <div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">predicts</span> <span class="o">=</span> <span class="p">[</span><span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">array</span><span class="p">([[</span><span class="mf">0.3</span><span class="p">,</span> <span class="mf">0.7</span><span class="p">],</span> <span class="p">[</span><span class="mf">0.</span><span class="p">,</span> <span class="mf">1.</span><span class="p">],</span> <span class="p">[</span><span class="mf">0.4</span><span class="p">,</span> <span class="mf">0.6</span><span class="p">]])]</span> |
| <span class="gp">>>> </span><span class="n">labels</span> <span class="o">=</span> <span class="p">[</span><span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="mf">0.</span><span class="p">,</span> <span class="mf">1.</span><span class="p">,</span> <span class="mf">1.</span><span class="p">])]</span> |
| <span class="gp">>>> </span><span class="n">f1</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">metric</span><span class="o">.</span><span class="n">F1</span><span class="p">()</span> |
| <span class="gp">>>> </span><span class="n">f1</span><span class="o">.</span><span class="n">update</span><span class="p">(</span><span class="n">preds</span> <span class="o">=</span> <span class="n">predicts</span><span class="p">,</span> <span class="n">labels</span> <span class="o">=</span> <span class="n">labels</span><span class="p">)</span> |
| <span class="gp">>>> </span><span class="nb">print</span> <span class="n">f1</span><span class="o">.</span><span class="n">get</span><span class="p">()</span> |
| <span class="go">('f1', 0.8)</span> |
| </pre></div> |
| </div> |
| <dl class="method"> |
| <dt id="mxnet.metric.F1.reset"> |
| <code class="sig-name descname">reset</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/mxnet/metric.html#F1.reset"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.metric.F1.reset" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Resets the internal evaluation result to initial state.</p> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.metric.F1.reset_local"> |
| <code class="sig-name descname">reset_local</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/mxnet/metric.html#F1.reset_local"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.metric.F1.reset_local" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Resets the internal evaluation result to initial state.</p> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.metric.F1.update"> |
| <code class="sig-name descname">update</code><span class="sig-paren">(</span><em class="sig-param">labels</em>, <em class="sig-param">preds</em><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/mxnet/metric.html#F1.update"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.metric.F1.update" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Updates the internal evaluation result.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>labels</strong> (list of <cite>NDArray</cite>) – The labels of the data.</p></li> |
| <li><p><strong>preds</strong> (list of <cite>NDArray</cite>) – Predicted values.</p></li> |
| </ul> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| </dd></dl> |
| |
| <dl class="class"> |
| <dt id="mxnet.metric.Loss"> |
| <em class="property">class </em><code class="sig-prename descclassname">mxnet.metric.</code><code class="sig-name descname">Loss</code><span class="sig-paren">(</span><em class="sig-param">name='loss'</em>, <em class="sig-param">output_names=None</em>, <em class="sig-param">label_names=None</em><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/mxnet/metric.html#Loss"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.metric.Loss" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Bases: <a class="reference internal" href="#mxnet.metric.EvalMetric" title="mxnet.metric.EvalMetric"><code class="xref py py-class docutils literal notranslate"><span class="pre">mxnet.metric.EvalMetric</span></code></a></p> |
| <p>Dummy metric for directly printing loss.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>name</strong> (<em>str</em>) – Name of this metric instance for display.</p></li> |
| <li><p><strong>output_names</strong> (<em>list of str</em><em>, or </em><em>None</em>) – Name of predictions that should be used when updating with update_dict. |
| By default include all predictions.</p></li> |
| <li><p><strong>label_names</strong> (<em>list of str</em><em>, or </em><em>None</em>) – Name of labels that should be used when updating with update_dict. |
| By default include all labels.</p></li> |
| </ul> |
| </dd> |
| </dl> |
| <p><strong>Methods</strong></p> |
| <table class="longtable docutils align-default"> |
| <colgroup> |
| <col style="width: 10%" /> |
| <col style="width: 90%" /> |
| </colgroup> |
| <tbody> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.metric.Loss.update" title="mxnet.metric.Loss.update"><code class="xref py py-obj docutils literal notranslate"><span class="pre">update</span></code></a>(_, preds)</p></td> |
| <td><p>Updates the internal evaluation result.</p></td> |
| </tr> |
| </tbody> |
| </table> |
| <dl class="method"> |
| <dt id="mxnet.metric.Loss.update"> |
| <code class="sig-name descname">update</code><span class="sig-paren">(</span><em class="sig-param">_</em>, <em class="sig-param">preds</em><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/mxnet/metric.html#Loss.update"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.metric.Loss.update" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Updates the internal evaluation result.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>labels</strong> (list of <cite>NDArray</cite>) – The labels of the data.</p></li> |
| <li><p><strong>preds</strong> (list of <cite>NDArray</cite>) – Predicted values.</p></li> |
| </ul> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| </dd></dl> |
| |
| <dl class="class"> |
| <dt id="mxnet.metric.MAE"> |
| <em class="property">class </em><code class="sig-prename descclassname">mxnet.metric.</code><code class="sig-name descname">MAE</code><span class="sig-paren">(</span><em class="sig-param">name='mae'</em>, <em class="sig-param">output_names=None</em>, <em class="sig-param">label_names=None</em><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/mxnet/metric.html#MAE"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.metric.MAE" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Bases: <a class="reference internal" href="#mxnet.metric.EvalMetric" title="mxnet.metric.EvalMetric"><code class="xref py py-class docutils literal notranslate"><span class="pre">mxnet.metric.EvalMetric</span></code></a></p> |
| <p>Computes Mean Absolute Error (MAE) loss.</p> |
| <p>The mean absolute error is given by</p> |
| <div class="math notranslate nohighlight"> |
| \[\frac{\sum_i^n |y_i - \hat{y}_i|}{n}\]</div> |
| <p><strong>Methods</strong></p> |
| <table class="longtable docutils align-default"> |
| <colgroup> |
| <col style="width: 10%" /> |
| <col style="width: 90%" /> |
| </colgroup> |
| <tbody> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.metric.MAE.update" title="mxnet.metric.MAE.update"><code class="xref py py-obj docutils literal notranslate"><span class="pre">update</span></code></a>(labels, preds)</p></td> |
| <td><p>Updates the internal evaluation result.</p></td> |
| </tr> |
| </tbody> |
| </table> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>name</strong> (<em>str</em>) – Name of this metric instance for display.</p></li> |
| <li><p><strong>output_names</strong> (<em>list of str</em><em>, or </em><em>None</em>) – Name of predictions that should be used when updating with update_dict. |
| By default include all predictions.</p></li> |
| <li><p><strong>label_names</strong> (<em>list of str</em><em>, or </em><em>None</em>) – Name of labels that should be used when updating with update_dict. |
| By default include all labels.</p></li> |
| </ul> |
| </dd> |
| </dl> |
| <p class="rubric">Examples</p> |
| <div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">predicts</span> <span class="o">=</span> <span class="p">[</span><span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="mi">3</span><span class="p">,</span> <span class="o">-</span><span class="mf">0.5</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">7</span><span class="p">])</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="mi">4</span><span class="p">,</span><span class="mi">1</span><span class="p">))]</span> |
| <span class="gp">>>> </span><span class="n">labels</span> <span class="o">=</span> <span class="p">[</span><span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="mf">2.5</span><span class="p">,</span> <span class="mf">0.0</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">8</span><span class="p">])</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="mi">4</span><span class="p">,</span><span class="mi">1</span><span class="p">))]</span> |
| <span class="gp">>>> </span><span class="n">mean_absolute_error</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">metric</span><span class="o">.</span><span class="n">MAE</span><span class="p">()</span> |
| <span class="gp">>>> </span><span class="n">mean_absolute_error</span><span class="o">.</span><span class="n">update</span><span class="p">(</span><span class="n">labels</span> <span class="o">=</span> <span class="n">labels</span><span class="p">,</span> <span class="n">preds</span> <span class="o">=</span> <span class="n">predicts</span><span class="p">)</span> |
| <span class="gp">>>> </span><span class="nb">print</span> <span class="n">mean_absolute_error</span><span class="o">.</span><span class="n">get</span><span class="p">()</span> |
| <span class="go">('mae', 0.5)</span> |
| </pre></div> |
| </div> |
| <dl class="method"> |
| <dt id="mxnet.metric.MAE.update"> |
| <code class="sig-name descname">update</code><span class="sig-paren">(</span><em class="sig-param">labels</em>, <em class="sig-param">preds</em><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/mxnet/metric.html#MAE.update"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.metric.MAE.update" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Updates the internal evaluation result.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>labels</strong> (list of <cite>NDArray</cite>) – The labels of the data.</p></li> |
| <li><p><strong>preds</strong> (list of <cite>NDArray</cite>) – Predicted values.</p></li> |
| </ul> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| </dd></dl> |
| |
| <dl class="class"> |
| <dt id="mxnet.metric.MCC"> |
| <em class="property">class </em><code class="sig-prename descclassname">mxnet.metric.</code><code class="sig-name descname">MCC</code><span class="sig-paren">(</span><em class="sig-param">name='mcc'</em>, <em class="sig-param">output_names=None</em>, <em class="sig-param">label_names=None</em>, <em class="sig-param">average='macro'</em><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/mxnet/metric.html#MCC"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.metric.MCC" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Bases: <a class="reference internal" href="#mxnet.metric.EvalMetric" title="mxnet.metric.EvalMetric"><code class="xref py py-class docutils literal notranslate"><span class="pre">mxnet.metric.EvalMetric</span></code></a></p> |
| <p>Computes the Matthews Correlation Coefficient of a binary classification problem.</p> |
| <p>While slower to compute than F1 the MCC can give insight that F1 or Accuracy cannot. |
| For instance, if the network always predicts the same result |
| then the MCC will immeadiately show this. The MCC is also symetric with respect |
| to positive and negative categorization, however, there needs to be both |
| positive and negative examples in the labels or it will always return 0. |
| MCC of 0 is uncorrelated, 1 is completely correlated, and -1 is negatively correlated.</p> |
| <div class="math notranslate nohighlight"> |
| \[\text{MCC} = \frac{ TP \times TN - FP \times FN } |
| {\sqrt{ (TP + FP) ( TP + FN ) ( TN + FP ) ( TN + FN ) } }\]</div> |
| <p><strong>Methods</strong></p> |
| <table class="longtable docutils align-default"> |
| <colgroup> |
| <col style="width: 10%" /> |
| <col style="width: 90%" /> |
| </colgroup> |
| <tbody> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.metric.MCC.reset" title="mxnet.metric.MCC.reset"><code class="xref py py-obj docutils literal notranslate"><span class="pre">reset</span></code></a>()</p></td> |
| <td><p>Resets the internal evaluation result to initial state.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.metric.MCC.reset_local" title="mxnet.metric.MCC.reset_local"><code class="xref py py-obj docutils literal notranslate"><span class="pre">reset_local</span></code></a>()</p></td> |
| <td><p>Resets the internal evaluation result to initial state.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.metric.MCC.update" title="mxnet.metric.MCC.update"><code class="xref py py-obj docutils literal notranslate"><span class="pre">update</span></code></a>(labels, preds)</p></td> |
| <td><p>Updates the internal evaluation result.</p></td> |
| </tr> |
| </tbody> |
| </table> |
| <p>where 0 terms in the denominator are replaced by 1.</p> |
| <div class="admonition note"> |
| <p class="admonition-title">Note</p> |
| <p>This version of MCC only supports binary classification. See PCC.</p> |
| </div> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>name</strong> (<em>str</em>) – Name of this metric instance for display.</p></li> |
| <li><p><strong>output_names</strong> (<em>list of str</em><em>, or </em><em>None</em>) – Name of predictions that should be used when updating with update_dict. |
| By default include all predictions.</p></li> |
| <li><p><strong>label_names</strong> (<em>list of str</em><em>, or </em><em>None</em>) – Name of labels that should be used when updating with update_dict. |
| By default include all labels.</p></li> |
| <li><p><strong>average</strong> (<em>str</em><em>, </em><em>default 'macro'</em>) – <dl class="simple"> |
| <dt>Strategy to be used for aggregating across mini-batches.</dt><dd><p>”macro”: average the MCC for each batch. |
| “micro”: compute a single MCC across all batches.</p> |
| </dd> |
| </dl> |
| </p></li> |
| </ul> |
| </dd> |
| </dl> |
| <p class="rubric">Examples</p> |
| <div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="c1"># In this example the network almost always predicts positive</span> |
| <span class="gp">>>> </span><span class="n">false_positives</span> <span class="o">=</span> <span class="mi">1000</span> |
| <span class="gp">>>> </span><span class="n">false_negatives</span> <span class="o">=</span> <span class="mi">1</span> |
| <span class="gp">>>> </span><span class="n">true_positives</span> <span class="o">=</span> <span class="mi">10000</span> |
| <span class="gp">>>> </span><span class="n">true_negatives</span> <span class="o">=</span> <span class="mi">1</span> |
| <span class="gp">>>> </span><span class="n">predicts</span> <span class="o">=</span> <span class="p">[</span><span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">array</span><span class="p">(</span> |
| <span class="go"> [[.3, .7]]*false_positives +</span> |
| <span class="go"> [[.7, .3]]*true_negatives +</span> |
| <span class="go"> [[.7, .3]]*false_negatives +</span> |
| <span class="go"> [[.3, .7]]*true_positives</span> |
| <span class="go">)]</span> |
| <span class="gp">>>> </span><span class="n">labels</span> <span class="o">=</span> <span class="p">[</span><span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">array</span><span class="p">(</span> |
| <span class="go"> [0.]*(false_positives + true_negatives) +</span> |
| <span class="go"> [1.]*(false_negatives + true_positives)</span> |
| <span class="go">)]</span> |
| <span class="gp">>>> </span><span class="n">f1</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">metric</span><span class="o">.</span><span class="n">F1</span><span class="p">()</span> |
| <span class="gp">>>> </span><span class="n">f1</span><span class="o">.</span><span class="n">update</span><span class="p">(</span><span class="n">preds</span> <span class="o">=</span> <span class="n">predicts</span><span class="p">,</span> <span class="n">labels</span> <span class="o">=</span> <span class="n">labels</span><span class="p">)</span> |
| <span class="gp">>>> </span><span class="n">mcc</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">metric</span><span class="o">.</span><span class="n">MCC</span><span class="p">()</span> |
| <span class="gp">>>> </span><span class="n">mcc</span><span class="o">.</span><span class="n">update</span><span class="p">(</span><span class="n">preds</span> <span class="o">=</span> <span class="n">predicts</span><span class="p">,</span> <span class="n">labels</span> <span class="o">=</span> <span class="n">labels</span><span class="p">)</span> |
| <span class="gp">>>> </span><span class="nb">print</span> <span class="n">f1</span><span class="o">.</span><span class="n">get</span><span class="p">()</span> |
| <span class="go">('f1', 0.95233560306652054)</span> |
| <span class="gp">>>> </span><span class="nb">print</span> <span class="n">mcc</span><span class="o">.</span><span class="n">get</span><span class="p">()</span> |
| <span class="go">('mcc', 0.01917751877733392)</span> |
| </pre></div> |
| </div> |
| <dl class="method"> |
| <dt id="mxnet.metric.MCC.reset"> |
| <code class="sig-name descname">reset</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/mxnet/metric.html#MCC.reset"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.metric.MCC.reset" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Resets the internal evaluation result to initial state.</p> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.metric.MCC.reset_local"> |
| <code class="sig-name descname">reset_local</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/mxnet/metric.html#MCC.reset_local"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.metric.MCC.reset_local" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Resets the internal evaluation result to initial state.</p> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.metric.MCC.update"> |
| <code class="sig-name descname">update</code><span class="sig-paren">(</span><em class="sig-param">labels</em>, <em class="sig-param">preds</em><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/mxnet/metric.html#MCC.update"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.metric.MCC.update" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Updates the internal evaluation result.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>labels</strong> (list of <cite>NDArray</cite>) – The labels of the data.</p></li> |
| <li><p><strong>preds</strong> (list of <cite>NDArray</cite>) – Predicted values.</p></li> |
| </ul> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| </dd></dl> |
| |
| <dl class="class"> |
| <dt id="mxnet.metric.MSE"> |
| <em class="property">class </em><code class="sig-prename descclassname">mxnet.metric.</code><code class="sig-name descname">MSE</code><span class="sig-paren">(</span><em class="sig-param">name='mse'</em>, <em class="sig-param">output_names=None</em>, <em class="sig-param">label_names=None</em><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/mxnet/metric.html#MSE"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.metric.MSE" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Bases: <a class="reference internal" href="#mxnet.metric.EvalMetric" title="mxnet.metric.EvalMetric"><code class="xref py py-class docutils literal notranslate"><span class="pre">mxnet.metric.EvalMetric</span></code></a></p> |
| <p>Computes Mean Squared Error (MSE) loss.</p> |
| <p>The mean squared error is given by</p> |
| <div class="math notranslate nohighlight"> |
| \[\frac{\sum_i^n (y_i - \hat{y}_i)^2}{n}\]</div> |
| <p><strong>Methods</strong></p> |
| <table class="longtable docutils align-default"> |
| <colgroup> |
| <col style="width: 10%" /> |
| <col style="width: 90%" /> |
| </colgroup> |
| <tbody> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.metric.MSE.update" title="mxnet.metric.MSE.update"><code class="xref py py-obj docutils literal notranslate"><span class="pre">update</span></code></a>(labels, preds)</p></td> |
| <td><p>Updates the internal evaluation result.</p></td> |
| </tr> |
| </tbody> |
| </table> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>name</strong> (<em>str</em>) – Name of this metric instance for display.</p></li> |
| <li><p><strong>output_names</strong> (<em>list of str</em><em>, or </em><em>None</em>) – Name of predictions that should be used when updating with update_dict. |
| By default include all predictions.</p></li> |
| <li><p><strong>label_names</strong> (<em>list of str</em><em>, or </em><em>None</em>) – Name of labels that should be used when updating with update_dict. |
| By default include all labels.</p></li> |
| </ul> |
| </dd> |
| </dl> |
| <p class="rubric">Examples</p> |
| <div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">predicts</span> <span class="o">=</span> <span class="p">[</span><span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="mi">3</span><span class="p">,</span> <span class="o">-</span><span class="mf">0.5</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">7</span><span class="p">])</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="mi">4</span><span class="p">,</span><span class="mi">1</span><span class="p">))]</span> |
| <span class="gp">>>> </span><span class="n">labels</span> <span class="o">=</span> <span class="p">[</span><span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="mf">2.5</span><span class="p">,</span> <span class="mf">0.0</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">8</span><span class="p">])</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="mi">4</span><span class="p">,</span><span class="mi">1</span><span class="p">))]</span> |
| <span class="gp">>>> </span><span class="n">mean_squared_error</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">metric</span><span class="o">.</span><span class="n">MSE</span><span class="p">()</span> |
| <span class="gp">>>> </span><span class="n">mean_squared_error</span><span class="o">.</span><span class="n">update</span><span class="p">(</span><span class="n">labels</span> <span class="o">=</span> <span class="n">labels</span><span class="p">,</span> <span class="n">preds</span> <span class="o">=</span> <span class="n">predicts</span><span class="p">)</span> |
| <span class="gp">>>> </span><span class="nb">print</span> <span class="n">mean_squared_error</span><span class="o">.</span><span class="n">get</span><span class="p">()</span> |
| <span class="go">('mse', 0.375)</span> |
| </pre></div> |
| </div> |
| <dl class="method"> |
| <dt id="mxnet.metric.MSE.update"> |
| <code class="sig-name descname">update</code><span class="sig-paren">(</span><em class="sig-param">labels</em>, <em class="sig-param">preds</em><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/mxnet/metric.html#MSE.update"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.metric.MSE.update" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Updates the internal evaluation result.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>labels</strong> (list of <cite>NDArray</cite>) – The labels of the data.</p></li> |
| <li><p><strong>preds</strong> (list of <cite>NDArray</cite>) – Predicted values.</p></li> |
| </ul> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| </dd></dl> |
| |
| <dl class="class"> |
| <dt id="mxnet.metric.NegativeLogLikelihood"> |
| <em class="property">class </em><code class="sig-prename descclassname">mxnet.metric.</code><code class="sig-name descname">NegativeLogLikelihood</code><span class="sig-paren">(</span><em class="sig-param">eps=1e-12</em>, <em class="sig-param">name='nll-loss'</em>, <em class="sig-param">output_names=None</em>, <em class="sig-param">label_names=None</em><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/mxnet/metric.html#NegativeLogLikelihood"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.metric.NegativeLogLikelihood" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Bases: <a class="reference internal" href="#mxnet.metric.EvalMetric" title="mxnet.metric.EvalMetric"><code class="xref py py-class docutils literal notranslate"><span class="pre">mxnet.metric.EvalMetric</span></code></a></p> |
| <p>Computes the negative log-likelihood loss.</p> |
| <p>The negative log-likelihoodd loss over a batch of sample size <span class="math notranslate nohighlight">\(N\)</span> is given by</p> |
| <div class="math notranslate nohighlight"> |
| \[-\sum_{n=1}^{N}\sum_{k=1}^{K}t_{nk}\log (y_{nk}),\]</div> |
| <p><strong>Methods</strong></p> |
| <table class="longtable docutils align-default"> |
| <colgroup> |
| <col style="width: 10%" /> |
| <col style="width: 90%" /> |
| </colgroup> |
| <tbody> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.metric.NegativeLogLikelihood.update" title="mxnet.metric.NegativeLogLikelihood.update"><code class="xref py py-obj docutils literal notranslate"><span class="pre">update</span></code></a>(labels, preds)</p></td> |
| <td><p>Updates the internal evaluation result.</p></td> |
| </tr> |
| </tbody> |
| </table> |
| <p>where <span class="math notranslate nohighlight">\(K\)</span> is the number of classes, <span class="math notranslate nohighlight">\(y_{nk}\)</span> is the prediceted probability for |
| <span class="math notranslate nohighlight">\(k\)</span>-th class for <span class="math notranslate nohighlight">\(n\)</span>-th sample. <span class="math notranslate nohighlight">\(t_{nk}=1\)</span> if and only if sample |
| <span class="math notranslate nohighlight">\(n\)</span> belongs to class <span class="math notranslate nohighlight">\(k\)</span>.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>eps</strong> (<em>float</em>) – Negative log-likelihood loss is undefined for predicted value is 0, |
| so predicted values are added with the small constant.</p></li> |
| <li><p><strong>name</strong> (<em>str</em>) – Name of this metric instance for display.</p></li> |
| <li><p><strong>output_names</strong> (<em>list of str</em><em>, or </em><em>None</em>) – Name of predictions that should be used when updating with update_dict. |
| By default include all predictions.</p></li> |
| <li><p><strong>label_names</strong> (<em>list of str</em><em>, or </em><em>None</em>) – Name of labels that should be used when updating with update_dict. |
| By default include all labels.</p></li> |
| </ul> |
| </dd> |
| </dl> |
| <p class="rubric">Examples</p> |
| <div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">predicts</span> <span class="o">=</span> <span class="p">[</span><span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">array</span><span class="p">([[</span><span class="mf">0.3</span><span class="p">,</span> <span class="mf">0.7</span><span class="p">],</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mf">1.</span><span class="p">],</span> <span class="p">[</span><span class="mf">0.4</span><span class="p">,</span> <span class="mf">0.6</span><span class="p">]])]</span> |
| <span class="gp">>>> </span><span class="n">labels</span> <span class="o">=</span> <span class="p">[</span><span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">])]</span> |
| <span class="gp">>>> </span><span class="n">nll_loss</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">metric</span><span class="o">.</span><span class="n">NegativeLogLikelihood</span><span class="p">()</span> |
| <span class="gp">>>> </span><span class="n">nll_loss</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">predicts</span><span class="p">)</span> |
| <span class="gp">>>> </span><span class="nb">print</span> <span class="n">nll_loss</span><span class="o">.</span><span class="n">get</span><span class="p">()</span> |
| <span class="go">('nll-loss', 0.57159948348999023)</span> |
| </pre></div> |
| </div> |
| <dl class="method"> |
| <dt id="mxnet.metric.NegativeLogLikelihood.update"> |
| <code class="sig-name descname">update</code><span class="sig-paren">(</span><em class="sig-param">labels</em>, <em class="sig-param">preds</em><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/mxnet/metric.html#NegativeLogLikelihood.update"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.metric.NegativeLogLikelihood.update" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Updates the internal evaluation result.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>labels</strong> (list of <cite>NDArray</cite>) – The labels of the data.</p></li> |
| <li><p><strong>preds</strong> (list of <cite>NDArray</cite>) – Predicted values.</p></li> |
| </ul> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| </dd></dl> |
| |
| <dl class="class"> |
| <dt id="mxnet.metric.PCC"> |
| <em class="property">class </em><code class="sig-prename descclassname">mxnet.metric.</code><code class="sig-name descname">PCC</code><span class="sig-paren">(</span><em class="sig-param">name='pcc'</em>, <em class="sig-param">output_names=None</em>, <em class="sig-param">label_names=None</em>, <em class="sig-param">has_global_stats=True</em><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/mxnet/metric.html#PCC"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.metric.PCC" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Bases: <a class="reference internal" href="#mxnet.metric.EvalMetric" title="mxnet.metric.EvalMetric"><code class="xref py py-class docutils literal notranslate"><span class="pre">mxnet.metric.EvalMetric</span></code></a></p> |
| <p>PCC is a multiclass equivalent for the Matthews correlation coefficient derived |
| from a discrete solution to the Pearson correlation coefficient.</p> |
| <div class="math notranslate nohighlight"> |
| \[\text{PCC} = \frac {\sum _{k}\sum _{l}\sum _{m}C_{kk}C_{lm}-C_{kl}C_{mk}} |
| {{\sqrt {\sum _{k}(\sum _{l}C_{kl})(\sum _{k'|k'\neq k}\sum _{l'}C_{k'l'})}} |
| {\sqrt {\sum _{k}(\sum _{l}C_{lk})(\sum _{k'|k'\neq k}\sum _{l'}C_{l'k'})}}}\]</div> |
| <p><strong>Methods</strong></p> |
| <table class="longtable docutils align-default"> |
| <colgroup> |
| <col style="width: 10%" /> |
| <col style="width: 90%" /> |
| </colgroup> |
| <tbody> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.metric.PCC.reset" title="mxnet.metric.PCC.reset"><code class="xref py py-obj docutils literal notranslate"><span class="pre">reset</span></code></a>()</p></td> |
| <td><p>Resets the internal evaluation result to initial state.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.metric.PCC.reset_local" title="mxnet.metric.PCC.reset_local"><code class="xref py py-obj docutils literal notranslate"><span class="pre">reset_local</span></code></a>()</p></td> |
| <td><p>Resets the local portion of the internal evaluation results to initial state.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.metric.PCC.update" title="mxnet.metric.PCC.update"><code class="xref py py-obj docutils literal notranslate"><span class="pre">update</span></code></a>(labels, preds)</p></td> |
| <td><p>Updates the internal evaluation result.</p></td> |
| </tr> |
| </tbody> |
| </table> |
| <p>defined in terms of a K x K confusion matrix C.</p> |
| <p>When there are more than two labels the PCC will no longer range between -1 and +1. |
| Instead the minimum value will be between -1 and 0 depending on the true distribution. |
| The maximum value is always +1.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>name</strong> (<em>str</em>) – Name of this metric instance for display.</p></li> |
| <li><p><strong>output_names</strong> (<em>list of str</em><em>, or </em><em>None</em>) – Name of predictions that should be used when updating with update_dict. |
| By default include all predictions.</p></li> |
| <li><p><strong>label_names</strong> (<em>list of str</em><em>, or </em><em>None</em>) – Name of labels that should be used when updating with update_dict. |
| By default include all labels.</p></li> |
| </ul> |
| </dd> |
| </dl> |
| <p class="rubric">Examples</p> |
| <div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="c1"># In this example the network almost always predicts positive</span> |
| <span class="gp">>>> </span><span class="n">false_positives</span> <span class="o">=</span> <span class="mi">1000</span> |
| <span class="gp">>>> </span><span class="n">false_negatives</span> <span class="o">=</span> <span class="mi">1</span> |
| <span class="gp">>>> </span><span class="n">true_positives</span> <span class="o">=</span> <span class="mi">10000</span> |
| <span class="gp">>>> </span><span class="n">true_negatives</span> <span class="o">=</span> <span class="mi">1</span> |
| <span class="gp">>>> </span><span class="n">predicts</span> <span class="o">=</span> <span class="p">[</span><span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">array</span><span class="p">(</span> |
| <span class="go"> [[.3, .7]]*false_positives +</span> |
| <span class="go"> [[.7, .3]]*true_negatives +</span> |
| <span class="go"> [[.7, .3]]*false_negatives +</span> |
| <span class="go"> [[.3, .7]]*true_positives</span> |
| <span class="go">)]</span> |
| <span class="gp">>>> </span><span class="n">labels</span> <span class="o">=</span> <span class="p">[</span><span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">array</span><span class="p">(</span> |
| <span class="go"> [0]*(false_positives + true_negatives) +</span> |
| <span class="go"> [1]*(false_negatives + true_positives)</span> |
| <span class="go">)]</span> |
| <span class="gp">>>> </span><span class="n">f1</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">metric</span><span class="o">.</span><span class="n">F1</span><span class="p">()</span> |
| <span class="gp">>>> </span><span class="n">f1</span><span class="o">.</span><span class="n">update</span><span class="p">(</span><span class="n">preds</span> <span class="o">=</span> <span class="n">predicts</span><span class="p">,</span> <span class="n">labels</span> <span class="o">=</span> <span class="n">labels</span><span class="p">)</span> |
| <span class="gp">>>> </span><span class="n">pcc</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">metric</span><span class="o">.</span><span class="n">PCC</span><span class="p">()</span> |
| <span class="gp">>>> </span><span class="n">pcc</span><span class="o">.</span><span class="n">update</span><span class="p">(</span><span class="n">preds</span> <span class="o">=</span> <span class="n">predicts</span><span class="p">,</span> <span class="n">labels</span> <span class="o">=</span> <span class="n">labels</span><span class="p">)</span> |
| <span class="gp">>>> </span><span class="nb">print</span> <span class="n">f1</span><span class="o">.</span><span class="n">get</span><span class="p">()</span> |
| <span class="go">('f1', 0.95233560306652054)</span> |
| <span class="gp">>>> </span><span class="nb">print</span> <span class="n">pcc</span><span class="o">.</span><span class="n">get</span><span class="p">()</span> |
| <span class="go">('pcc', 0.01917751877733392)</span> |
| </pre></div> |
| </div> |
| <dl class="method"> |
| <dt id="mxnet.metric.PCC.reset"> |
| <code class="sig-name descname">reset</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/mxnet/metric.html#PCC.reset"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.metric.PCC.reset" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Resets the internal evaluation result to initial state.</p> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.metric.PCC.reset_local"> |
| <code class="sig-name descname">reset_local</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/mxnet/metric.html#PCC.reset_local"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.metric.PCC.reset_local" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Resets the local portion of the internal evaluation results to initial state.</p> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.metric.PCC.update"> |
| <code class="sig-name descname">update</code><span class="sig-paren">(</span><em class="sig-param">labels</em>, <em class="sig-param">preds</em><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/mxnet/metric.html#PCC.update"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.metric.PCC.update" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Updates the internal evaluation result.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>labels</strong> (list of <cite>NDArray</cite>) – The labels of the data.</p></li> |
| <li><p><strong>preds</strong> (list of <cite>NDArray</cite>) – Predicted values.</p></li> |
| </ul> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| </dd></dl> |
| |
| <dl class="class"> |
| <dt id="mxnet.metric.PearsonCorrelation"> |
| <em class="property">class </em><code class="sig-prename descclassname">mxnet.metric.</code><code class="sig-name descname">PearsonCorrelation</code><span class="sig-paren">(</span><em class="sig-param">name='pearsonr'</em>, <em class="sig-param">output_names=None</em>, <em class="sig-param">label_names=None</em>, <em class="sig-param">average='macro'</em><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/mxnet/metric.html#PearsonCorrelation"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.metric.PearsonCorrelation" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Bases: <a class="reference internal" href="#mxnet.metric.EvalMetric" title="mxnet.metric.EvalMetric"><code class="xref py py-class docutils literal notranslate"><span class="pre">mxnet.metric.EvalMetric</span></code></a></p> |
| <p>Computes Pearson correlation.</p> |
| <p>The pearson correlation is given by</p> |
| <div class="math notranslate nohighlight"> |
| \[\frac{cov(y, \hat{y})}{\sigma{y}\sigma{\hat{y}}}\]</div> |
| <p><strong>Methods</strong></p> |
| <table class="longtable docutils align-default"> |
| <colgroup> |
| <col style="width: 10%" /> |
| <col style="width: 90%" /> |
| </colgroup> |
| <tbody> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.metric.PearsonCorrelation.get" title="mxnet.metric.PearsonCorrelation.get"><code class="xref py py-obj docutils literal notranslate"><span class="pre">get</span></code></a>()</p></td> |
| <td><p>Gets the current evaluation result.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.metric.PearsonCorrelation.reset" title="mxnet.metric.PearsonCorrelation.reset"><code class="xref py py-obj docutils literal notranslate"><span class="pre">reset</span></code></a>()</p></td> |
| <td><p>Resets the internal evaluation result to initial state.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.metric.PearsonCorrelation.update" title="mxnet.metric.PearsonCorrelation.update"><code class="xref py py-obj docutils literal notranslate"><span class="pre">update</span></code></a>(labels, preds)</p></td> |
| <td><p>Updates the internal evaluation result.</p></td> |
| </tr> |
| </tbody> |
| </table> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>name</strong> (<em>str</em>) – Name of this metric instance for display.</p></li> |
| <li><p><strong>output_names</strong> (<em>list of str</em><em>, or </em><em>None</em>) – Name of predictions that should be used when updating with update_dict. |
| By default include all predictions.</p></li> |
| <li><p><strong>label_names</strong> (<em>list of str</em><em>, or </em><em>None</em>) – Name of labels that should be used when updating with update_dict. |
| By default include all labels.</p></li> |
| <li><p><strong>average</strong> (<em>str</em><em>, </em><em>default 'macro'</em>) – <dl class="simple"> |
| <dt>Strategy to be used for aggregating across mini-batches.</dt><dd><p>”macro”: average the pearsonr scores for each batch. |
| “micro”: compute a single pearsonr score across all batches.</p> |
| </dd> |
| </dl> |
| </p></li> |
| </ul> |
| </dd> |
| </dl> |
| <p class="rubric">Examples</p> |
| <div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">predicts</span> <span class="o">=</span> <span class="p">[</span><span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">array</span><span class="p">([[</span><span class="mf">0.3</span><span class="p">,</span> <span class="mf">0.7</span><span class="p">],</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mf">1.</span><span class="p">],</span> <span class="p">[</span><span class="mf">0.4</span><span class="p">,</span> <span class="mf">0.6</span><span class="p">]])]</span> |
| <span class="gp">>>> </span><span class="n">labels</span> <span class="o">=</span> <span class="p">[</span><span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">array</span><span class="p">([[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">],</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">],</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">]])]</span> |
| <span class="gp">>>> </span><span class="n">pr</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">metric</span><span class="o">.</span><span class="n">PearsonCorrelation</span><span class="p">()</span> |
| <span class="gp">>>> </span><span class="n">pr</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">predicts</span><span class="p">)</span> |
| <span class="gp">>>> </span><span class="nb">print</span> <span class="n">pr</span><span class="o">.</span><span class="n">get</span><span class="p">()</span> |
| <span class="go">('pearsonr', 0.42163704544016178)</span> |
| </pre></div> |
| </div> |
| <dl class="method"> |
| <dt id="mxnet.metric.PearsonCorrelation.get"> |
| <code class="sig-name descname">get</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/mxnet/metric.html#PearsonCorrelation.get"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.metric.PearsonCorrelation.get" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Gets the current evaluation result.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Returns</dt> |
| <dd class="field-odd"><p><ul class="simple"> |
| <li><p><strong>names</strong> (<em>list of str</em>) – Name of the metrics.</p></li> |
| <li><p><strong>values</strong> (<em>list of float</em>) – Value of the evaluations.</p></li> |
| </ul> |
| </p> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.metric.PearsonCorrelation.reset"> |
| <code class="sig-name descname">reset</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/mxnet/metric.html#PearsonCorrelation.reset"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.metric.PearsonCorrelation.reset" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Resets the internal evaluation result to initial state.</p> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.metric.PearsonCorrelation.update"> |
| <code class="sig-name descname">update</code><span class="sig-paren">(</span><em class="sig-param">labels</em>, <em class="sig-param">preds</em><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/mxnet/metric.html#PearsonCorrelation.update"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.metric.PearsonCorrelation.update" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Updates the internal evaluation result.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>labels</strong> (list of <cite>NDArray</cite>) – The labels of the data.</p></li> |
| <li><p><strong>preds</strong> (list of <cite>NDArray</cite>) – Predicted values.</p></li> |
| </ul> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| </dd></dl> |
| |
| <dl class="class"> |
| <dt id="mxnet.metric.Perplexity"> |
| <em class="property">class </em><code class="sig-prename descclassname">mxnet.metric.</code><code class="sig-name descname">Perplexity</code><span class="sig-paren">(</span><em class="sig-param">ignore_label</em>, <em class="sig-param">axis=-1</em>, <em class="sig-param">name='perplexity'</em>, <em class="sig-param">output_names=None</em>, <em class="sig-param">label_names=None</em><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/mxnet/metric.html#Perplexity"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.metric.Perplexity" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Bases: <a class="reference internal" href="#mxnet.metric.EvalMetric" title="mxnet.metric.EvalMetric"><code class="xref py py-class docutils literal notranslate"><span class="pre">mxnet.metric.EvalMetric</span></code></a></p> |
| <p>Computes perplexity.</p> |
| <p>Perplexity is a measurement of how well a probability distribution |
| or model predicts a sample. A low perplexity indicates the model |
| is good at predicting the sample.</p> |
| <p>The perplexity of a model q is defined as</p> |
| <div class="math notranslate nohighlight"> |
| \[b^{\big(-\frac{1}{N} \sum_{i=1}^N \log_b q(x_i) \big)} |
| = \exp \big(-\frac{1}{N} \sum_{i=1}^N \log q(x_i)\big)\]</div> |
| <p><strong>Methods</strong></p> |
| <table class="longtable docutils align-default"> |
| <colgroup> |
| <col style="width: 10%" /> |
| <col style="width: 90%" /> |
| </colgroup> |
| <tbody> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.metric.Perplexity.get" title="mxnet.metric.Perplexity.get"><code class="xref py py-obj docutils literal notranslate"><span class="pre">get</span></code></a>()</p></td> |
| <td><p>Returns the current evaluation result.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.metric.Perplexity.get_global" title="mxnet.metric.Perplexity.get_global"><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_global</span></code></a>()</p></td> |
| <td><p>Returns the current global evaluation result.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.metric.Perplexity.update" title="mxnet.metric.Perplexity.update"><code class="xref py py-obj docutils literal notranslate"><span class="pre">update</span></code></a>(labels, preds)</p></td> |
| <td><p>Updates the internal evaluation result.</p></td> |
| </tr> |
| </tbody> |
| </table> |
| <p>where we let <cite>b = e</cite>.</p> |
| <p><span class="math notranslate nohighlight">\(q(x_i)\)</span> is the predicted value of its ground truth |
| label on sample <span class="math notranslate nohighlight">\(x_i\)</span>.</p> |
| <p>For example, we have three samples <span class="math notranslate nohighlight">\(x_1, x_2, x_3\)</span> and their labels |
| are <span class="math notranslate nohighlight">\([0, 1, 1]\)</span>. |
| Suppose our model predicts <span class="math notranslate nohighlight">\(q(x_1) = p(y_1 = 0 | x_1) = 0.3\)</span> |
| and <span class="math notranslate nohighlight">\(q(x_2) = 1.0\)</span>, |
| <span class="math notranslate nohighlight">\(q(x_3) = 0.6\)</span>. The perplexity of model q is |
| <span class="math notranslate nohighlight">\(exp\big(-(\log 0.3 + \log 1.0 + \log 0.6) / 3\big) = 1.77109762852\)</span>.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>ignore_label</strong> (<em>int</em><em> or </em><em>None</em>) – Index of invalid label to ignore when |
| counting. By default, sets to -1. |
| If set to <cite>None</cite>, it will include all entries.</p></li> |
| <li><p><strong>axis</strong> (<em>int</em><em> (</em><em>default -1</em><em>)</em>) – The axis from prediction that was used to |
| compute softmax. By default use the last |
| axis.</p></li> |
| <li><p><strong>name</strong> (<em>str</em>) – Name of this metric instance for display.</p></li> |
| <li><p><strong>output_names</strong> (<em>list of str</em><em>, or </em><em>None</em>) – Name of predictions that should be used when updating with update_dict. |
| By default include all predictions.</p></li> |
| <li><p><strong>label_names</strong> (<em>list of str</em><em>, or </em><em>None</em>) – Name of labels that should be used when updating with update_dict. |
| By default include all labels.</p></li> |
| </ul> |
| </dd> |
| </dl> |
| <p class="rubric">Examples</p> |
| <div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">predicts</span> <span class="o">=</span> <span class="p">[</span><span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">array</span><span class="p">([[</span><span class="mf">0.3</span><span class="p">,</span> <span class="mf">0.7</span><span class="p">],</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mf">1.</span><span class="p">],</span> <span class="p">[</span><span class="mf">0.4</span><span class="p">,</span> <span class="mf">0.6</span><span class="p">]])]</span> |
| <span class="gp">>>> </span><span class="n">labels</span> <span class="o">=</span> <span class="p">[</span><span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">])]</span> |
| <span class="gp">>>> </span><span class="n">perp</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">metric</span><span class="o">.</span><span class="n">Perplexity</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="gp">>>> </span><span class="n">perp</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">predicts</span><span class="p">)</span> |
| <span class="gp">>>> </span><span class="nb">print</span> <span class="n">perp</span><span class="o">.</span><span class="n">get</span><span class="p">()</span> |
| <span class="go">('Perplexity', 1.7710976285155853)</span> |
| </pre></div> |
| </div> |
| <dl class="method"> |
| <dt id="mxnet.metric.Perplexity.get"> |
| <code class="sig-name descname">get</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/mxnet/metric.html#Perplexity.get"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.metric.Perplexity.get" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Returns the current evaluation result.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Returns</dt> |
| <dd class="field-odd"><p>Representing name of the metric and evaluation result.</p> |
| </dd> |
| <dt class="field-even">Return type</dt> |
| <dd class="field-even"><p>Tuple of (str, float)</p> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.metric.Perplexity.get_global"> |
| <code class="sig-name descname">get_global</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/mxnet/metric.html#Perplexity.get_global"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.metric.Perplexity.get_global" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Returns the current global evaluation result.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Returns</dt> |
| <dd class="field-odd"><p>Representing name of the metric and evaluation result.</p> |
| </dd> |
| <dt class="field-even">Return type</dt> |
| <dd class="field-even"><p>Tuple of (str, float)</p> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.metric.Perplexity.update"> |
| <code class="sig-name descname">update</code><span class="sig-paren">(</span><em class="sig-param">labels</em>, <em class="sig-param">preds</em><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/mxnet/metric.html#Perplexity.update"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.metric.Perplexity.update" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Updates the internal evaluation result.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>labels</strong> (list of <cite>NDArray</cite>) – The labels of the data.</p></li> |
| <li><p><strong>preds</strong> (list of <cite>NDArray</cite>) – Predicted values.</p></li> |
| </ul> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| </dd></dl> |
| |
| <dl class="class"> |
| <dt id="mxnet.metric.RMSE"> |
| <em class="property">class </em><code class="sig-prename descclassname">mxnet.metric.</code><code class="sig-name descname">RMSE</code><span class="sig-paren">(</span><em class="sig-param">name='rmse'</em>, <em class="sig-param">output_names=None</em>, <em class="sig-param">label_names=None</em><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/mxnet/metric.html#RMSE"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.metric.RMSE" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Bases: <a class="reference internal" href="#mxnet.metric.EvalMetric" title="mxnet.metric.EvalMetric"><code class="xref py py-class docutils literal notranslate"><span class="pre">mxnet.metric.EvalMetric</span></code></a></p> |
| <p>Computes Root Mean Squred Error (RMSE) loss.</p> |
| <p>The root mean squared error is given by</p> |
| <div class="math notranslate nohighlight"> |
| \[\sqrt{\frac{\sum_i^n (y_i - \hat{y}_i)^2}{n}}\]</div> |
| <p><strong>Methods</strong></p> |
| <table class="longtable docutils align-default"> |
| <colgroup> |
| <col style="width: 10%" /> |
| <col style="width: 90%" /> |
| </colgroup> |
| <tbody> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.metric.RMSE.update" title="mxnet.metric.RMSE.update"><code class="xref py py-obj docutils literal notranslate"><span class="pre">update</span></code></a>(labels, preds)</p></td> |
| <td><p>Updates the internal evaluation result.</p></td> |
| </tr> |
| </tbody> |
| </table> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>name</strong> (<em>str</em>) – Name of this metric instance for display.</p></li> |
| <li><p><strong>output_names</strong> (<em>list of str</em><em>, or </em><em>None</em>) – Name of predictions that should be used when updating with update_dict. |
| By default include all predictions.</p></li> |
| <li><p><strong>label_names</strong> (<em>list of str</em><em>, or </em><em>None</em>) – Name of labels that should be used when updating with update_dict. |
| By default include all labels.</p></li> |
| </ul> |
| </dd> |
| </dl> |
| <p class="rubric">Examples</p> |
| <div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">predicts</span> <span class="o">=</span> <span class="p">[</span><span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="mi">3</span><span class="p">,</span> <span class="o">-</span><span class="mf">0.5</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">7</span><span class="p">])</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="mi">4</span><span class="p">,</span><span class="mi">1</span><span class="p">))]</span> |
| <span class="gp">>>> </span><span class="n">labels</span> <span class="o">=</span> <span class="p">[</span><span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="mf">2.5</span><span class="p">,</span> <span class="mf">0.0</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">8</span><span class="p">])</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="mi">4</span><span class="p">,</span><span class="mi">1</span><span class="p">))]</span> |
| <span class="gp">>>> </span><span class="n">root_mean_squared_error</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">metric</span><span class="o">.</span><span class="n">RMSE</span><span class="p">()</span> |
| <span class="gp">>>> </span><span class="n">root_mean_squared_error</span><span class="o">.</span><span class="n">update</span><span class="p">(</span><span class="n">labels</span> <span class="o">=</span> <span class="n">labels</span><span class="p">,</span> <span class="n">preds</span> <span class="o">=</span> <span class="n">predicts</span><span class="p">)</span> |
| <span class="gp">>>> </span><span class="nb">print</span> <span class="n">root_mean_squared_error</span><span class="o">.</span><span class="n">get</span><span class="p">()</span> |
| <span class="go">('rmse', 0.612372457981)</span> |
| </pre></div> |
| </div> |
| <dl class="method"> |
| <dt id="mxnet.metric.RMSE.update"> |
| <code class="sig-name descname">update</code><span class="sig-paren">(</span><em class="sig-param">labels</em>, <em class="sig-param">preds</em><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/mxnet/metric.html#RMSE.update"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.metric.RMSE.update" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Updates the internal evaluation result.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>labels</strong> (list of <cite>NDArray</cite>) – The labels of the data.</p></li> |
| <li><p><strong>preds</strong> (list of <cite>NDArray</cite>) – Predicted values.</p></li> |
| </ul> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| </dd></dl> |
| |
| <dl class="class"> |
| <dt id="mxnet.metric.TopKAccuracy"> |
| <em class="property">class </em><code class="sig-prename descclassname">mxnet.metric.</code><code class="sig-name descname">TopKAccuracy</code><span class="sig-paren">(</span><em class="sig-param">top_k=1</em>, <em class="sig-param">name='top_k_accuracy'</em>, <em class="sig-param">output_names=None</em>, <em class="sig-param">label_names=None</em><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/mxnet/metric.html#TopKAccuracy"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.metric.TopKAccuracy" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Bases: <a class="reference internal" href="#mxnet.metric.EvalMetric" title="mxnet.metric.EvalMetric"><code class="xref py py-class docutils literal notranslate"><span class="pre">mxnet.metric.EvalMetric</span></code></a></p> |
| <p>Computes top k predictions accuracy.</p> |
| <p><cite>TopKAccuracy</cite> differs from Accuracy in that it considers the prediction |
| to be <code class="docutils literal notranslate"><span class="pre">True</span></code> as long as the ground truth label is in the top K |
| predicated labels.</p> |
| <p>If <cite>top_k</cite> = <code class="docutils literal notranslate"><span class="pre">1</span></code>, then <cite>TopKAccuracy</cite> is identical to <cite>Accuracy</cite>.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>top_k</strong> (<em>int</em>) – Whether targets are in top k predictions.</p></li> |
| <li><p><strong>name</strong> (<em>str</em>) – Name of this metric instance for display.</p></li> |
| <li><p><strong>output_names</strong> (<em>list of str</em><em>, or </em><em>None</em>) – Name of predictions that should be used when updating with update_dict. |
| By default include all predictions.</p></li> |
| <li><p><strong>label_names</strong> (<em>list of str</em><em>, or </em><em>None</em>) – Name of labels that should be used when updating with update_dict. |
| By default include all labels.</p></li> |
| </ul> |
| </dd> |
| </dl> |
| <p><strong>Methods</strong></p> |
| <table class="longtable docutils align-default"> |
| <colgroup> |
| <col style="width: 10%" /> |
| <col style="width: 90%" /> |
| </colgroup> |
| <tbody> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.metric.TopKAccuracy.update" title="mxnet.metric.TopKAccuracy.update"><code class="xref py py-obj docutils literal notranslate"><span class="pre">update</span></code></a>(labels, preds)</p></td> |
| <td><p>Updates the internal evaluation result.</p></td> |
| </tr> |
| </tbody> |
| </table> |
| <p class="rubric">Examples</p> |
| <div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">seed</span><span class="p">(</span><span class="mi">999</span><span class="p">)</span> |
| <span class="gp">>>> </span><span class="n">top_k</span> <span class="o">=</span> <span class="mi">3</span> |
| <span class="gp">>>> </span><span class="n">labels</span> <span class="o">=</span> <span class="p">[</span><span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="mi">2</span><span class="p">,</span> <span class="mi">6</span><span class="p">,</span> <span class="mi">9</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="mi">7</span><span class="p">,</span> <span class="mi">8</span><span class="p">,</span> <span class="mi">9</span><span class="p">,</span> <span class="mi">6</span><span class="p">])]</span> |
| <span class="gp">>>> </span><span class="n">predicts</span> <span class="o">=</span> <span class="p">[</span><span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">rand</span><span class="p">(</span><span class="mi">10</span><span class="p">,</span> <span class="mi">10</span><span class="p">))]</span> |
| <span class="gp">>>> </span><span class="n">acc</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">metric</span><span class="o">.</span><span class="n">TopKAccuracy</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="gp">>>> </span><span class="n">acc</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">predicts</span><span class="p">)</span> |
| <span class="gp">>>> </span><span class="nb">print</span> <span class="n">acc</span><span class="o">.</span><span class="n">get</span><span class="p">()</span> |
| <span class="go">('top_k_accuracy', 0.3)</span> |
| </pre></div> |
| </div> |
| <dl class="method"> |
| <dt id="mxnet.metric.TopKAccuracy.update"> |
| <code class="sig-name descname">update</code><span class="sig-paren">(</span><em class="sig-param">labels</em>, <em class="sig-param">preds</em><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/mxnet/metric.html#TopKAccuracy.update"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.metric.TopKAccuracy.update" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Updates the internal evaluation result.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>labels</strong> (list of <cite>NDArray</cite>) – The labels of the data.</p></li> |
| <li><p><strong>preds</strong> (list of <cite>NDArray</cite>) – Predicted values.</p></li> |
| </ul> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| </dd></dl> |
| |
| <dl class="class"> |
| <dt id="mxnet.metric.Torch"> |
| <em class="property">class </em><code class="sig-prename descclassname">mxnet.metric.</code><code class="sig-name descname">Torch</code><span class="sig-paren">(</span><em class="sig-param">name='torch'</em>, <em class="sig-param">output_names=None</em>, <em class="sig-param">label_names=None</em><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/mxnet/metric.html#Torch"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.metric.Torch" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Bases: <a class="reference internal" href="#mxnet.metric.Loss" title="mxnet.metric.Loss"><code class="xref py py-class docutils literal notranslate"><span class="pre">mxnet.metric.Loss</span></code></a></p> |
| <p>Dummy metric for torch criterions.</p> |
| </dd></dl> |
| |
| <dl class="function"> |
| <dt id="mxnet.metric.check_label_shapes"> |
| <code class="sig-prename descclassname">mxnet.metric.</code><code class="sig-name descname">check_label_shapes</code><span class="sig-paren">(</span><em class="sig-param">labels</em>, <em class="sig-param">preds</em>, <em class="sig-param">wrap=False</em>, <em class="sig-param">shape=False</em><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/mxnet/metric.html#check_label_shapes"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.metric.check_label_shapes" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Helper function for checking shape of label and prediction</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>labels</strong> (list of <cite>NDArray</cite>) – The labels of the data.</p></li> |
| <li><p><strong>preds</strong> (list of <cite>NDArray</cite>) – Predicted values.</p></li> |
| <li><p><strong>wrap</strong> (<em>boolean</em>) – If True, wrap labels/preds in a list if they are single NDArray</p></li> |
| <li><p><strong>shape</strong> (<em>boolean</em>) – If True, check the shape of labels and preds; |
| Otherwise only check their length.</p></li> |
| </ul> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="function"> |
| <dt id="mxnet.metric.create"> |
| <code class="sig-prename descclassname">mxnet.metric.</code><code class="sig-name descname">create</code><span class="sig-paren">(</span><em class="sig-param">metric</em>, <em class="sig-param">*args</em>, <em class="sig-param">**kwargs</em><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/mxnet/metric.html#create"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.metric.create" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Creates evaluation metric from metric names or instances of EvalMetric |
| or a custom metric function.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>metric</strong> (<em>str</em><em> or </em><em>callable</em>) – <p>Specifies the metric to create. |
| This argument must be one of the below:</p> |
| <ul> |
| <li><p>Name of a metric.</p></li> |
| <li><p>An instance of <cite>EvalMetric</cite>.</p></li> |
| <li><p>A list, each element of which is a metric or a metric name.</p></li> |
| <li><p>An evaluation function that computes custom metric for a given batch of |
| labels and predictions.</p></li> |
| </ul> |
| </p></li> |
| <li><p><strong>*args</strong> (<em>list</em>) – Additional arguments to metric constructor. |
| Only used when metric is str.</p></li> |
| <li><p><strong>**kwargs</strong> (<em>dict</em>) – Additional arguments to metric constructor. |
| Only used when metric is str</p></li> |
| </ul> |
| </dd> |
| </dl> |
| <p class="rubric">Examples</p> |
| <div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="k">def</span> <span class="nf">custom_metric</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="gp">... </span> <span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">np</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="gp">...</span> |
| <span class="gp">>>> </span><span class="n">metric1</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">metric</span><span class="o">.</span><span class="n">create</span><span class="p">(</span><span class="s1">'acc'</span><span class="p">)</span> |
| <span class="gp">>>> </span><span class="n">metric2</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">metric</span><span class="o">.</span><span class="n">create</span><span class="p">(</span><span class="n">custom_metric</span><span class="p">)</span> |
| <span class="gp">>>> </span><span class="n">metric3</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">metric</span><span class="o">.</span><span class="n">create</span><span class="p">([</span><span class="n">metric1</span><span class="p">,</span> <span class="n">metric2</span><span class="p">,</span> <span class="s1">'rmse'</span><span class="p">])</span> |
| </pre></div> |
| </div> |
| </dd></dl> |
| |
| <dl class="function"> |
| <dt id="mxnet.metric.np"> |
| <code class="sig-prename descclassname">mxnet.metric.</code><code class="sig-name descname">np</code><span class="sig-paren">(</span><em class="sig-param">numpy_feval</em>, <em class="sig-param">name=None</em>, <em class="sig-param">allow_extra_outputs=False</em><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/mxnet/metric.html#np"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.metric.np" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Creates a custom evaluation metric that receives its inputs as numpy arrays.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>numpy_feval</strong> (<em>callable</em><em>(</em><em>label</em><em>, </em><em>pred</em><em>)</em>) – Custom evaluation function that receives labels and predictions for a minibatch |
| as numpy arrays and returns the corresponding custom metric as a floating point number.</p></li> |
| <li><p><strong>name</strong> (<em>str</em><em>, </em><em>optional</em>) – Name of the custom metric.</p></li> |
| <li><p><strong>allow_extra_outputs</strong> (<em>bool</em><em>, </em><em>optional</em>) – Whether prediction output is allowed to have extra outputs. This is useful in cases |
| like RNN where states are also part of output which can then be fed back to the RNN |
| in the next step. By default, extra outputs are not allowed.</p></li> |
| </ul> |
| </dd> |
| <dt class="field-even">Returns</dt> |
| <dd class="field-even"><p>Custom metric corresponding to the provided labels and predictions.</p> |
| </dd> |
| <dt class="field-odd">Return type</dt> |
| <dd class="field-odd"><p>float</p> |
| </dd> |
| </dl> |
| <p class="rubric">Example</p> |
| <div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="k">def</span> <span class="nf">custom_metric</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="gp">... </span> <span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">np</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="gp">...</span> |
| <span class="gp">>>> </span><span class="n">metric</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">metric</span><span class="o">.</span><span class="n">np</span><span class="p">(</span><span class="n">custom_metric</span><span class="p">)</span> |
| </pre></div> |
| </div> |
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