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| <span class="mdl-layout-title toc">Table Of Contents</span> |
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| <li class="toctree-l1"><a class="reference internal" href="../../../tutorials/index.html">Python Tutorials</a><ul> |
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| <li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/index.html">Getting started with NP on MXNet</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/1-ndarray.html">Step 1: Manipulate data with NP on MXNet</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/2-nn.html">Step 2: Create a neural network</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/3-autograd.html">Step 3: Automatic differentiation with autograd</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/4-train.html">Step 4: Train the neural network</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-predict.html">Step 5: Predict with a pretrained model</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/6-use_gpus.html">Step 6: Use GPUs to increase efficiency</a></li> |
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| <li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/to-mxnet/index.html">Moving to MXNet from Other Frameworks</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/to-mxnet/pytorch.html">PyTorch vs Apache MXNet</a></li> |
| </ul> |
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| <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> |
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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> |
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| <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> |
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| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/packages/gluon/data/index.html">Data Tutorials</a><ul> |
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| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/data/data_augmentation.html#Spatial-Augmentation">Spatial Augmentation</a></li> |
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| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/data/data_augmentation.html#Composed-Augmentations">Composed Augmentations</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/data/datasets.html">Gluon <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/data/datasets.html#Using-own-data-with-included-Datasets">Using own data with included <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/data/datasets.html#Using-own-data-with-custom-Datasets">Using own data with custom <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/data/datasets.html#Appendix:-Upgrading-from-Module-DataIter-to-Gluon-DataLoader">Appendix: Upgrading from Module <code class="docutils literal notranslate"><span class="pre">DataIter</span></code> to Gluon <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li> |
| </ul> |
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| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/image/info_gan.html">Image similarity search with InfoGAN</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/image/mnist.html">Handwritten Digit Recognition</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/packages/gluon/loss/index.html">Losses</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/loss/custom-loss.html">Custom Loss Blocks</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/loss/kl_divergence.html">Kullback-Leibler (KL) Divergence</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/loss/loss.html">Loss functions</a></li> |
| </ul> |
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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/legacy/index.html">Legacy</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/packages/legacy/ndarray/index.html">NDArray</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/legacy/ndarray/01-ndarray-intro.html">An Intro: Manipulate Data the MXNet Way with NDArray</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/legacy/ndarray/02-ndarray-operations.html">NDArray Operations</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/legacy/ndarray/03-ndarray-contexts.html">NDArray Contexts</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/legacy/ndarray/gotchas_numpy_in_mxnet.html">Gotchas using NumPy in Apache MXNet</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/legacy/ndarray/sparse/index.html">Tutorials</a><ul> |
| <li class="toctree-l6"><a class="reference internal" href="../../../tutorials/packages/legacy/ndarray/sparse/csr.html">CSRNDArray - NDArray in Compressed Sparse Row Storage Format</a></li> |
| <li class="toctree-l6"><a class="reference internal" href="../../../tutorials/packages/legacy/ndarray/sparse/row_sparse.html">RowSparseNDArray - NDArray for Sparse Gradient Updates</a></li> |
| <li class="toctree-l6"><a class="reference internal" href="../../../tutorials/packages/legacy/ndarray/sparse/train_gluon.html">Sparse NDArrays with Gluon</a></li> |
| </ul> |
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| </ul> |
| </li> |
| </ul> |
| </li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../tutorials/packages/np/index.html">What is NP on MXNet</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/packages/np/cheat-sheet.html">The NP on MXNet cheat sheet</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/packages/np/np-vs-numpy.html">Differences between NP on MXNet and NumPy</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../tutorials/packages/onnx/index.html">ONNX</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/packages/onnx/fine_tuning_gluon.html">Fine-tuning an ONNX model</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/packages/onnx/inference_on_onnx_model.html">Running inference on MXNet/Gluon from an ONNX model</a></li> |
| <li class="toctree-l4"><a class="reference external" href="https://mxnet.apache.org/api/python/docs/tutorials/deploy/export/onnx.html">Export ONNX Models</a></li> |
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| <li class="toctree-l4"><a class="reference external" href="https://mxnet.apache.org/api/faq/gradient_compression">Gradient Compression</a></li> |
| <li class="toctree-l4"><a class="reference external" href="https://gluon-cv.mxnet.io/build/examples_deployment/int8_inference.html">GluonCV with Quantized Models</a></li> |
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| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/performance/backend/mkldnn/mkldnn_readme.html">Install MXNet with MKL-DNN</a></li> |
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| <li class="toctree-l4"><a class="reference internal" href="../../np/routines.io.html">Input and output</a><ul> |
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| <li class="toctree-l4"><a class="reference internal" href="../../np/routines.linalg.html">Linear algebra (<code class="xref py py-mod docutils literal notranslate"><span class="pre">numpy.linalg</span></code>)</a><ul> |
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| <span class="mdl-layout-title toc">Table Of Contents</span> |
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| <li class="toctree-l1"><a class="reference internal" href="../../../tutorials/index.html">Python Tutorials</a><ul> |
| <li class="toctree-l2"><a class="reference internal" href="../../../tutorials/getting-started/index.html">Getting Started</a><ul> |
| <li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/index.html">Getting started with NP on MXNet</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/1-ndarray.html">Step 1: Manipulate data with NP on MXNet</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/2-nn.html">Step 2: Create a neural network</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/3-autograd.html">Step 3: Automatic differentiation with autograd</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/4-train.html">Step 4: Train the neural network</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-predict.html">Step 5: Predict with a pretrained model</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/6-use_gpus.html">Step 6: Use GPUs to increase efficiency</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/to-mxnet/index.html">Moving to MXNet from Other Frameworks</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/to-mxnet/pytorch.html">PyTorch vs Apache MXNet</a></li> |
| </ul> |
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| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/data/datasets.html#Appendix:-Upgrading-from-Module-DataIter-to-Gluon-DataLoader">Appendix: Upgrading from Module <code class="docutils literal notranslate"><span class="pre">DataIter</span></code> to Gluon <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li> |
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| <li class="toctree-l5"><a class="reference internal" href="../../np/generated/mxnet.np.split.html">mxnet.np.split</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../np/generated/mxnet.np.hsplit.html">mxnet.np.hsplit</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../np/generated/mxnet.np.vsplit.html">mxnet.np.vsplit</a></li> |
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| <li class="toctree-l5"><a class="reference internal" href="../../np/generated/mxnet.np.unique.html">mxnet.np.unique</a></li> |
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| <li class="toctree-l5"><a class="reference internal" href="../../np/generated/mxnet.np.flip.html">mxnet.np.flip</a></li> |
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| <li class="toctree-l4"><a class="reference internal" href="../../np/routines.io.html">Input and output</a><ul> |
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| </ul> |
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| <li class="toctree-l4"><a class="reference internal" href="../../np/routines.linalg.html">Linear algebra (<code class="xref py py-mod docutils literal notranslate"><span class="pre">numpy.linalg</span></code>)</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../np/generated/mxnet.np.dot.html">mxnet.np.dot</a></li> |
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| <li class="toctree-l4"><a class="reference internal" href="../../np/routines.math.html">Mathematical functions</a><ul> |
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| <li class="toctree-l5"><a class="reference internal" href="../../np/generated/mxnet.np.cos.html">mxnet.np.cos</a></li> |
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| <li class="toctree-l5"><a class="reference internal" href="../../np/generated/mxnet.np.cosh.html">mxnet.np.cosh</a></li> |
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| <li class="toctree-l5"><a class="reference internal" href="../../np/generated/mxnet.np.multiply.html">mxnet.np.multiply</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../np/generated/mxnet.np.true_divide.html">mxnet.np.true_divide</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../np/generated/mxnet.np.remainder.html">mxnet.np.remainder</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../np/generated/mxnet.np.clip.html">mxnet.np.clip</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../np/generated/mxnet.np.sqrt.html">mxnet.np.sqrt</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../np/generated/mxnet.np.cbrt.html">mxnet.np.cbrt</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../np/generated/mxnet.np.square.html">mxnet.np.square</a></li> |
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| <li class="toctree-l5"><a class="reference internal" href="../../np/generated/mxnet.np.sign.html">mxnet.np.sign</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../np/generated/mxnet.np.maximum.html">mxnet.np.maximum</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../np/generated/mxnet.np.minimum.html">mxnet.np.minimum</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l4"><a class="reference internal" href="../../np/random/index.html">np.random</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../np/routines.sort.html">Sorting, searching, and counting</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../np/generated/mxnet.np.argmax.html">mxnet.np.argmax</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../np/generated/mxnet.np.argmin.html">mxnet.np.argmin</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l4"><a class="reference internal" href="../../np/routines.statistics.html">Statistics</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../np/generated/mxnet.np.min.html">mxnet.np.min</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../np/generated/mxnet.np.max.html">mxnet.np.max</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../np/generated/mxnet.np.mean.html">mxnet.np.mean</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../np/generated/mxnet.np.std.html">mxnet.np.std</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../np/generated/mxnet.np.var.html">mxnet.np.var</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../np/generated/mxnet.np.histogram.html">mxnet.np.histogram</a></li> |
| </ul> |
| </li> |
| </ul> |
| </li> |
| </ul> |
| </li> |
| <li class="toctree-l2"><a class="reference internal" href="../../npx/index.html">NPX: NumPy Neural Network Extension</a><ul> |
| <li class="toctree-l3"><a class="reference internal" href="../../npx/generated/mxnet.npx.set_np.html">mxnet.npx.set_np</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../npx/generated/mxnet.npx.reset_np.html">mxnet.npx.reset_np</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../npx/generated/mxnet.npx.cpu.html">mxnet.npx.cpu</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../npx/generated/mxnet.npx.cpu_pinned.html">mxnet.npx.cpu_pinned</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../npx/generated/mxnet.npx.gpu.html">mxnet.npx.gpu</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../npx/generated/mxnet.npx.gpu_memory_info.html">mxnet.npx.gpu_memory_info</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../npx/generated/mxnet.npx.current_context.html">mxnet.npx.current_context</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../npx/generated/mxnet.npx.num_gpus.html">mxnet.npx.num_gpus</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../npx/generated/mxnet.npx.activation.html">mxnet.npx.activation</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../npx/generated/mxnet.npx.batch_norm.html">mxnet.npx.batch_norm</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../npx/generated/mxnet.npx.convolution.html">mxnet.npx.convolution</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../npx/generated/mxnet.npx.dropout.html">mxnet.npx.dropout</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../npx/generated/mxnet.npx.embedding.html">mxnet.npx.embedding</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../npx/generated/mxnet.npx.fully_connected.html">mxnet.npx.fully_connected</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../npx/generated/mxnet.npx.layer_norm.html">mxnet.npx.layer_norm</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../npx/generated/mxnet.npx.pooling.html">mxnet.npx.pooling</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../npx/generated/mxnet.npx.rnn.html">mxnet.npx.rnn</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../npx/generated/mxnet.npx.leaky_relu.html">mxnet.npx.leaky_relu</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../npx/generated/mxnet.npx.multibox_detection.html">mxnet.npx.multibox_detection</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../npx/generated/mxnet.npx.multibox_prior.html">mxnet.npx.multibox_prior</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../npx/generated/mxnet.npx.multibox_target.html">mxnet.npx.multibox_target</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../npx/generated/mxnet.npx.roi_pooling.html">mxnet.npx.roi_pooling</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../npx/generated/mxnet.npx.sigmoid.html">mxnet.npx.sigmoid</a></li> |
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| <div class="document"> |
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| |
| <div class="section" id="gluon-loss"> |
| <h1>gluon.loss<a class="headerlink" href="#gluon-loss" title="Permalink to this headline">¶</a></h1> |
| <p>Gluon provides pre-defined loss functions in the <a class="reference internal" href="#module-mxnet.gluon.loss" title="mxnet.gluon.loss"><code class="xref py py-mod docutils literal notranslate"><span class="pre">mxnet.gluon.loss</span></code></a> |
| module.</p> |
| <span class="target" id="module-mxnet.gluon.loss"></span><p>losses for training neural networks</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.gluon.loss.Loss" title="mxnet.gluon.loss.Loss"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Loss</span></code></a>(weight, batch_axis, **kwargs)</p></td> |
| <td><p>Base class for loss.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.gluon.loss.L2Loss" title="mxnet.gluon.loss.L2Loss"><code class="xref py py-obj docutils literal notranslate"><span class="pre">L2Loss</span></code></a>([weight, batch_axis])</p></td> |
| <td><p>Calculates the mean squared error between <cite>label</cite> and <cite>pred</cite>.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.gluon.loss.L1Loss" title="mxnet.gluon.loss.L1Loss"><code class="xref py py-obj docutils literal notranslate"><span class="pre">L1Loss</span></code></a>([weight, batch_axis])</p></td> |
| <td><p>Calculates the mean absolute error between <cite>label</cite> and <cite>pred</cite>.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.gluon.loss.SigmoidBinaryCrossEntropyLoss" title="mxnet.gluon.loss.SigmoidBinaryCrossEntropyLoss"><code class="xref py py-obj docutils literal notranslate"><span class="pre">SigmoidBinaryCrossEntropyLoss</span></code></a>([…])</p></td> |
| <td><p>The cross-entropy loss for binary classification.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.gluon.loss.SigmoidBCELoss" title="mxnet.gluon.loss.SigmoidBCELoss"><code class="xref py py-obj docutils literal notranslate"><span class="pre">SigmoidBCELoss</span></code></a></p></td> |
| <td><p>The cross-entropy loss for binary classification.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.gluon.loss.SoftmaxCrossEntropyLoss" title="mxnet.gluon.loss.SoftmaxCrossEntropyLoss"><code class="xref py py-obj docutils literal notranslate"><span class="pre">SoftmaxCrossEntropyLoss</span></code></a>([axis, …])</p></td> |
| <td><p>Computes the softmax cross entropy loss.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.gluon.loss.SoftmaxCELoss" title="mxnet.gluon.loss.SoftmaxCELoss"><code class="xref py py-obj docutils literal notranslate"><span class="pre">SoftmaxCELoss</span></code></a></p></td> |
| <td><p>Computes the softmax cross entropy loss.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.gluon.loss.KLDivLoss" title="mxnet.gluon.loss.KLDivLoss"><code class="xref py py-obj docutils literal notranslate"><span class="pre">KLDivLoss</span></code></a>([from_logits, axis, weight, …])</p></td> |
| <td><p>The Kullback-Leibler divergence loss.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.gluon.loss.CTCLoss" title="mxnet.gluon.loss.CTCLoss"><code class="xref py py-obj docutils literal notranslate"><span class="pre">CTCLoss</span></code></a>([layout, label_layout, weight])</p></td> |
| <td><p>Connectionist Temporal Classification Loss.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.gluon.loss.HuberLoss" title="mxnet.gluon.loss.HuberLoss"><code class="xref py py-obj docutils literal notranslate"><span class="pre">HuberLoss</span></code></a>([rho, weight, batch_axis])</p></td> |
| <td><p>Calculates smoothed L1 loss that is equal to L1 loss if absolute error exceeds rho but is equal to L2 loss otherwise.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.gluon.loss.HingeLoss" title="mxnet.gluon.loss.HingeLoss"><code class="xref py py-obj docutils literal notranslate"><span class="pre">HingeLoss</span></code></a>([margin, weight, batch_axis])</p></td> |
| <td><p>Calculates the hinge loss function often used in SVMs:</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.gluon.loss.SquaredHingeLoss" title="mxnet.gluon.loss.SquaredHingeLoss"><code class="xref py py-obj docutils literal notranslate"><span class="pre">SquaredHingeLoss</span></code></a>([margin, weight, batch_axis])</p></td> |
| <td><p>Calculates the soft-margin loss function used in SVMs:</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.gluon.loss.LogisticLoss" title="mxnet.gluon.loss.LogisticLoss"><code class="xref py py-obj docutils literal notranslate"><span class="pre">LogisticLoss</span></code></a>([weight, batch_axis, label_format])</p></td> |
| <td><p>Calculates the logistic loss (for binary losses only):</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.gluon.loss.TripletLoss" title="mxnet.gluon.loss.TripletLoss"><code class="xref py py-obj docutils literal notranslate"><span class="pre">TripletLoss</span></code></a>([margin, weight, batch_axis])</p></td> |
| <td><p>Calculates triplet loss given three input tensors and a positive margin.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.gluon.loss.PoissonNLLLoss" title="mxnet.gluon.loss.PoissonNLLLoss"><code class="xref py py-obj docutils literal notranslate"><span class="pre">PoissonNLLLoss</span></code></a>([weight, from_logits, …])</p></td> |
| <td><p>For a target (Random Variable) in a Poisson distribution, the function calculates the Negative Log likelihood loss.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.gluon.loss.CosineEmbeddingLoss" title="mxnet.gluon.loss.CosineEmbeddingLoss"><code class="xref py py-obj docutils literal notranslate"><span class="pre">CosineEmbeddingLoss</span></code></a>([weight, batch_axis, margin])</p></td> |
| <td><p>For a target label 1 or -1, vectors input1 and input2, the function computes the cosine distance between the vectors.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.gluon.loss.SDMLLoss" title="mxnet.gluon.loss.SDMLLoss"><code class="xref py py-obj docutils literal notranslate"><span class="pre">SDMLLoss</span></code></a>([smoothing_parameter, weight, …])</p></td> |
| <td><p>Calculates Batchwise Smoothed Deep Metric Learning (SDML) Loss given two input tensors and a smoothing weight SDM Loss learns similarity between paired samples by using unpaired samples in the minibatch as potential negative examples.</p></td> |
| </tr> |
| </tbody> |
| </table> |
| <dl class="class"> |
| <dt id="mxnet.gluon.loss.Loss"> |
| <em class="property">class </em><code class="sig-prename descclassname">mxnet.gluon.loss.</code><code class="sig-name descname">Loss</code><span class="sig-paren">(</span><em class="sig-param">weight</em>, <em class="sig-param">batch_axis</em>, <em class="sig-param">**kwargs</em><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/mxnet/gluon/loss.html#Loss"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.gluon.loss.Loss" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">mxnet.gluon.block.HybridBlock</span></code></p> |
| <p>Base class for loss.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>weight</strong> (<em>float</em><em> or </em><em>None</em>) – Global scalar weight for loss.</p></li> |
| <li><p><strong>batch_axis</strong> (<em>int</em><em>, </em><em>default 0</em>) – The axis that represents mini-batch.</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.gluon.loss.Loss.hybrid_forward" title="mxnet.gluon.loss.Loss.hybrid_forward"><code class="xref py py-obj docutils literal notranslate"><span class="pre">hybrid_forward</span></code></a>(F, x, *args, **kwargs)</p></td> |
| <td><p>Overrides to construct symbolic graph for this <cite>Block</cite>.</p></td> |
| </tr> |
| </tbody> |
| </table> |
| <dl class="method"> |
| <dt id="mxnet.gluon.loss.Loss.hybrid_forward"> |
| <code class="sig-name descname">hybrid_forward</code><span class="sig-paren">(</span><em class="sig-param">F</em>, <em class="sig-param">x</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/gluon/loss.html#Loss.hybrid_forward"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.gluon.loss.Loss.hybrid_forward" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Overrides to construct symbolic graph for this <cite>Block</cite>.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>x</strong> (<a class="reference internal" href="../../legacy/symbol/symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a><em> or </em><a class="reference internal" href="../../legacy/ndarray/ndarray.html#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – The first input tensor.</p></li> |
| <li><p><strong>*args</strong> (<em>list of Symbol</em><em> or </em><em>list of NDArray</em>) – Additional input tensors.</p></li> |
| </ul> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| </dd></dl> |
| |
| <dl class="class"> |
| <dt id="mxnet.gluon.loss.L2Loss"> |
| <em class="property">class </em><code class="sig-prename descclassname">mxnet.gluon.loss.</code><code class="sig-name descname">L2Loss</code><span class="sig-paren">(</span><em class="sig-param">weight=1.0</em>, <em class="sig-param">batch_axis=0</em>, <em class="sig-param">**kwargs</em><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/mxnet/gluon/loss.html#L2Loss"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.gluon.loss.L2Loss" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Bases: <a class="reference internal" href="#mxnet.gluon.loss.Loss" title="mxnet.gluon.loss.Loss"><code class="xref py py-class docutils literal notranslate"><span class="pre">mxnet.gluon.loss.Loss</span></code></a></p> |
| <p>Calculates the mean squared error between <cite>label</cite> and <cite>pred</cite>.</p> |
| <div class="math notranslate nohighlight"> |
| \[L = \frac{1}{2} \sum_i \vert {label}_i - {pred}_i \vert^2.\]</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.gluon.loss.L2Loss.hybrid_forward" title="mxnet.gluon.loss.L2Loss.hybrid_forward"><code class="xref py py-obj docutils literal notranslate"><span class="pre">hybrid_forward</span></code></a>(F, pred, label[, sample_weight])</p></td> |
| <td><p>Overrides to construct symbolic graph for this <cite>Block</cite>.</p></td> |
| </tr> |
| </tbody> |
| </table> |
| <p><cite>label</cite> and <cite>pred</cite> can have arbitrary shape as long as they have the same |
| number of elements.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>weight</strong> (<em>float</em><em> or </em><em>None</em>) – Global scalar weight for loss.</p></li> |
| <li><p><strong>batch_axis</strong> (<em>int</em><em>, </em><em>default 0</em>) – The axis that represents mini-batch.</p></li> |
| </ul> |
| </dd> |
| </dl> |
| <dl class="simple"> |
| <dt>Inputs:</dt><dd><ul class="simple"> |
| <li><p><strong>pred</strong>: prediction tensor with arbitrary shape</p></li> |
| <li><p><strong>label</strong>: target tensor with the same size as pred.</p></li> |
| <li><p><strong>sample_weight</strong>: element-wise weighting tensor. Must be broadcastable |
| to the same shape as pred. For example, if pred has shape (64, 10) |
| and you want to weigh each sample in the batch separately, |
| sample_weight should have shape (64, 1).</p></li> |
| </ul> |
| </dd> |
| <dt>Outputs:</dt><dd><ul class="simple"> |
| <li><p><strong>loss</strong>: loss tensor with shape (batch_size,). Dimenions other than |
| batch_axis are averaged out.</p></li> |
| </ul> |
| </dd> |
| </dl> |
| <dl class="method"> |
| <dt id="mxnet.gluon.loss.L2Loss.hybrid_forward"> |
| <code class="sig-name descname">hybrid_forward</code><span class="sig-paren">(</span><em class="sig-param">F</em>, <em class="sig-param">pred</em>, <em class="sig-param">label</em>, <em class="sig-param">sample_weight=None</em><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/mxnet/gluon/loss.html#L2Loss.hybrid_forward"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.gluon.loss.L2Loss.hybrid_forward" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Overrides to construct symbolic graph for this <cite>Block</cite>.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>x</strong> (<a class="reference internal" href="../../legacy/symbol/symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a><em> or </em><a class="reference internal" href="../../legacy/ndarray/ndarray.html#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – The first input tensor.</p></li> |
| <li><p><strong>*args</strong> (<em>list of Symbol</em><em> or </em><em>list of NDArray</em>) – Additional input tensors.</p></li> |
| </ul> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| </dd></dl> |
| |
| <dl class="class"> |
| <dt id="mxnet.gluon.loss.L1Loss"> |
| <em class="property">class </em><code class="sig-prename descclassname">mxnet.gluon.loss.</code><code class="sig-name descname">L1Loss</code><span class="sig-paren">(</span><em class="sig-param">weight=None</em>, <em class="sig-param">batch_axis=0</em>, <em class="sig-param">**kwargs</em><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/mxnet/gluon/loss.html#L1Loss"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.gluon.loss.L1Loss" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Bases: <a class="reference internal" href="#mxnet.gluon.loss.Loss" title="mxnet.gluon.loss.Loss"><code class="xref py py-class docutils literal notranslate"><span class="pre">mxnet.gluon.loss.Loss</span></code></a></p> |
| <p>Calculates the mean absolute error between <cite>label</cite> and <cite>pred</cite>.</p> |
| <div class="math notranslate nohighlight"> |
| \[L = \sum_i \vert {label}_i - {pred}_i \vert.\]</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.gluon.loss.L1Loss.hybrid_forward" title="mxnet.gluon.loss.L1Loss.hybrid_forward"><code class="xref py py-obj docutils literal notranslate"><span class="pre">hybrid_forward</span></code></a>(F, pred, label[, sample_weight])</p></td> |
| <td><p>Overrides to construct symbolic graph for this <cite>Block</cite>.</p></td> |
| </tr> |
| </tbody> |
| </table> |
| <p><cite>label</cite> and <cite>pred</cite> can have arbitrary shape as long as they have the same |
| number of elements.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>weight</strong> (<em>float</em><em> or </em><em>None</em>) – Global scalar weight for loss.</p></li> |
| <li><p><strong>batch_axis</strong> (<em>int</em><em>, </em><em>default 0</em>) – The axis that represents mini-batch.</p></li> |
| </ul> |
| </dd> |
| </dl> |
| <dl class="simple"> |
| <dt>Inputs:</dt><dd><ul class="simple"> |
| <li><p><strong>pred</strong>: prediction tensor with arbitrary shape</p></li> |
| <li><p><strong>label</strong>: target tensor with the same size as pred.</p></li> |
| <li><p><strong>sample_weight</strong>: element-wise weighting tensor. Must be broadcastable |
| to the same shape as pred. For example, if pred has shape (64, 10) |
| and you want to weigh each sample in the batch separately, |
| sample_weight should have shape (64, 1).</p></li> |
| </ul> |
| </dd> |
| <dt>Outputs:</dt><dd><ul class="simple"> |
| <li><p><strong>loss</strong>: loss tensor with shape (batch_size,). Dimenions other than |
| batch_axis are averaged out.</p></li> |
| </ul> |
| </dd> |
| </dl> |
| <dl class="method"> |
| <dt id="mxnet.gluon.loss.L1Loss.hybrid_forward"> |
| <code class="sig-name descname">hybrid_forward</code><span class="sig-paren">(</span><em class="sig-param">F</em>, <em class="sig-param">pred</em>, <em class="sig-param">label</em>, <em class="sig-param">sample_weight=None</em><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/mxnet/gluon/loss.html#L1Loss.hybrid_forward"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.gluon.loss.L1Loss.hybrid_forward" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Overrides to construct symbolic graph for this <cite>Block</cite>.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>x</strong> (<a class="reference internal" href="../../legacy/symbol/symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a><em> or </em><a class="reference internal" href="../../legacy/ndarray/ndarray.html#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – The first input tensor.</p></li> |
| <li><p><strong>*args</strong> (<em>list of Symbol</em><em> or </em><em>list of NDArray</em>) – Additional input tensors.</p></li> |
| </ul> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| </dd></dl> |
| |
| <dl class="class"> |
| <dt id="mxnet.gluon.loss.SigmoidBinaryCrossEntropyLoss"> |
| <em class="property">class </em><code class="sig-prename descclassname">mxnet.gluon.loss.</code><code class="sig-name descname">SigmoidBinaryCrossEntropyLoss</code><span class="sig-paren">(</span><em class="sig-param">from_sigmoid=False</em>, <em class="sig-param">weight=None</em>, <em class="sig-param">batch_axis=0</em>, <em class="sig-param">**kwargs</em><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/mxnet/gluon/loss.html#SigmoidBinaryCrossEntropyLoss"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.gluon.loss.SigmoidBinaryCrossEntropyLoss" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Bases: <a class="reference internal" href="#mxnet.gluon.loss.Loss" title="mxnet.gluon.loss.Loss"><code class="xref py py-class docutils literal notranslate"><span class="pre">mxnet.gluon.loss.Loss</span></code></a></p> |
| <p>The cross-entropy loss for binary classification. (alias: SigmoidBCELoss)</p> |
| <p>BCE loss is useful when training logistic regression. If <cite>from_sigmoid</cite> |
| is False (default), this loss computes:</p> |
| <div class="math notranslate nohighlight"> |
| \[ \begin{align}\begin{aligned}prob = \frac{1}{1 + \exp(-{pred})}\\L = - \sum_i {label}_i * \log({prob}_i) * pos\_weight + |
| (1 - {label}_i) * \log(1 - {prob}_i)\end{aligned}\end{align} \]</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.gluon.loss.SigmoidBinaryCrossEntropyLoss.hybrid_forward" title="mxnet.gluon.loss.SigmoidBinaryCrossEntropyLoss.hybrid_forward"><code class="xref py py-obj docutils literal notranslate"><span class="pre">hybrid_forward</span></code></a>(F, pred, label[, …])</p></td> |
| <td><p>Overrides to construct symbolic graph for this <cite>Block</cite>.</p></td> |
| </tr> |
| </tbody> |
| </table> |
| <p>If <cite>from_sigmoid</cite> is True, this loss computes:</p> |
| <div class="math notranslate nohighlight"> |
| \[L = - \sum_i {label}_i * \log({pred}_i) * pos\_weight + |
| (1 - {label}_i) * \log(1 - {pred}_i)\]</div> |
| <p>A tensor <cite>pos_weight > 1</cite> decreases the false negative count, hence increasing |
| the recall. |
| Conversely setting <cite>pos_weight < 1</cite> decreases the false positive count and |
| increases the precision.</p> |
| <p><cite>pred</cite> and <cite>label</cite> can have arbitrary shape as long as they have the same |
| number of elements.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>from_sigmoid</strong> (bool, default is <cite>False</cite>) – Whether the input is from the output of sigmoid. Set this to false will make |
| the loss calculate sigmoid and BCE together, which is more numerically |
| stable through log-sum-exp trick.</p></li> |
| <li><p><strong>weight</strong> (<em>float</em><em> or </em><em>None</em>) – Global scalar weight for loss.</p></li> |
| <li><p><strong>batch_axis</strong> (<em>int</em><em>, </em><em>default 0</em>) – The axis that represents mini-batch.</p></li> |
| </ul> |
| </dd> |
| </dl> |
| <dl class="simple"> |
| <dt>Inputs:</dt><dd><ul class="simple"> |
| <li><p><strong>pred</strong>: prediction tensor with arbitrary shape</p></li> |
| <li><p><strong>label</strong>: target tensor with values in range <cite>[0, 1]</cite>. Must have the |
| same size as <cite>pred</cite>.</p></li> |
| <li><p><strong>sample_weight</strong>: element-wise weighting tensor. Must be broadcastable |
| to the same shape as pred. For example, if pred has shape (64, 10) |
| and you want to weigh each sample in the batch separately, |
| sample_weight should have shape (64, 1).</p></li> |
| <li><p><strong>pos_weight</strong>: a weighting tensor of positive examples. Must be a vector with length |
| equal to the number of classes.For example, if pred has shape (64, 10), |
| pos_weight should have shape (1, 10).</p></li> |
| </ul> |
| </dd> |
| <dt>Outputs:</dt><dd><ul class="simple"> |
| <li><p><strong>loss</strong>: loss tensor with shape (batch_size,). Dimenions other than |
| batch_axis are averaged out.</p></li> |
| </ul> |
| </dd> |
| </dl> |
| <dl class="method"> |
| <dt id="mxnet.gluon.loss.SigmoidBinaryCrossEntropyLoss.hybrid_forward"> |
| <code class="sig-name descname">hybrid_forward</code><span class="sig-paren">(</span><em class="sig-param">F</em>, <em class="sig-param">pred</em>, <em class="sig-param">label</em>, <em class="sig-param">sample_weight=None</em>, <em class="sig-param">pos_weight=None</em><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/mxnet/gluon/loss.html#SigmoidBinaryCrossEntropyLoss.hybrid_forward"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.gluon.loss.SigmoidBinaryCrossEntropyLoss.hybrid_forward" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Overrides to construct symbolic graph for this <cite>Block</cite>.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>x</strong> (<a class="reference internal" href="../../legacy/symbol/symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a><em> or </em><a class="reference internal" href="../../legacy/ndarray/ndarray.html#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – The first input tensor.</p></li> |
| <li><p><strong>*args</strong> (<em>list of Symbol</em><em> or </em><em>list of NDArray</em>) – Additional input tensors.</p></li> |
| </ul> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| </dd></dl> |
| |
| <dl class="attribute"> |
| <dt id="mxnet.gluon.loss.SigmoidBCELoss"> |
| <code class="sig-prename descclassname">mxnet.gluon.loss.</code><code class="sig-name descname">SigmoidBCELoss</code><a class="headerlink" href="#mxnet.gluon.loss.SigmoidBCELoss" title="Permalink to this definition">¶</a></dt> |
| <table class="longtable docutils align-default"> |
| <colgroup> |
| <col style="width: 10%" /> |
| <col style="width: 90%" /> |
| </colgroup> |
| <tbody> |
| <tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">hybrid_forward</span></code>(F, pred, label[, …])</p></td> |
| <td><p>Overrides to construct symbolic graph for this <cite>Block</cite>.</p></td> |
| </tr> |
| </tbody> |
| </table> |
| <p><strong>Methods</strong></p> |
| <dd><p>alias of <a class="reference internal" href="#mxnet.gluon.loss.SigmoidBinaryCrossEntropyLoss" title="mxnet.gluon.loss.SigmoidBinaryCrossEntropyLoss"><code class="xref py py-class docutils literal notranslate"><span class="pre">mxnet.gluon.loss.SigmoidBinaryCrossEntropyLoss</span></code></a></p> |
| </dd></dl> |
| |
| <dl class="class"> |
| <dt id="mxnet.gluon.loss.SoftmaxCrossEntropyLoss"> |
| <em class="property">class </em><code class="sig-prename descclassname">mxnet.gluon.loss.</code><code class="sig-name descname">SoftmaxCrossEntropyLoss</code><span class="sig-paren">(</span><em class="sig-param">axis=-1</em>, <em class="sig-param">sparse_label=True</em>, <em class="sig-param">from_logits=False</em>, <em class="sig-param">weight=None</em>, <em class="sig-param">batch_axis=0</em>, <em class="sig-param">**kwargs</em><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/mxnet/gluon/loss.html#SoftmaxCrossEntropyLoss"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.gluon.loss.SoftmaxCrossEntropyLoss" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Bases: <a class="reference internal" href="#mxnet.gluon.loss.Loss" title="mxnet.gluon.loss.Loss"><code class="xref py py-class docutils literal notranslate"><span class="pre">mxnet.gluon.loss.Loss</span></code></a></p> |
| <p>Computes the softmax cross entropy loss. (alias: SoftmaxCELoss)</p> |
| <p>If <cite>sparse_label</cite> is <cite>True</cite> (default), label should contain integer |
| category indicators:</p> |
| <div class="math notranslate nohighlight"> |
| \[ \begin{align}\begin{aligned}\DeclareMathOperator{softmax}{softmax}\\p = \softmax({pred})\\L = -\sum_i \log p_{i,{label}_i}\end{aligned}\end{align} \]</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.gluon.loss.SoftmaxCrossEntropyLoss.hybrid_forward" title="mxnet.gluon.loss.SoftmaxCrossEntropyLoss.hybrid_forward"><code class="xref py py-obj docutils literal notranslate"><span class="pre">hybrid_forward</span></code></a>(F, pred, label[, sample_weight])</p></td> |
| <td><p>Overrides to construct symbolic graph for this <cite>Block</cite>.</p></td> |
| </tr> |
| </tbody> |
| </table> |
| <p><cite>label</cite>’s shape should be <cite>pred</cite>’s shape with the <cite>axis</cite> dimension removed. |
| i.e. for <cite>pred</cite> with shape (1,2,3,4) and <cite>axis = 2</cite>, <cite>label</cite>’s shape should |
| be (1,2,4).</p> |
| <p>If <cite>sparse_label</cite> is <cite>False</cite>, <cite>label</cite> should contain probability distribution |
| and <cite>label</cite>’s shape should be the same with <cite>pred</cite>:</p> |
| <div class="math notranslate nohighlight"> |
| \[ \begin{align}\begin{aligned}p = \softmax({pred})\\L = -\sum_i \sum_j {label}_j \log p_{ij}\end{aligned}\end{align} \]</div> |
| <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 to sum over when computing softmax and entropy.</p></li> |
| <li><p><strong>sparse_label</strong> (<em>bool</em><em>, </em><em>default True</em>) – Whether label is an integer array instead of probability distribution.</p></li> |
| <li><p><strong>from_logits</strong> (<em>bool</em><em>, </em><em>default False</em>) – Whether input is a log probability (usually from log_softmax) instead |
| of unnormalized numbers.</p></li> |
| <li><p><strong>weight</strong> (<em>float</em><em> or </em><em>None</em>) – Global scalar weight for loss.</p></li> |
| <li><p><strong>batch_axis</strong> (<em>int</em><em>, </em><em>default 0</em>) – The axis that represents mini-batch.</p></li> |
| </ul> |
| </dd> |
| </dl> |
| <dl class="simple"> |
| <dt>Inputs:</dt><dd><ul class="simple"> |
| <li><p><strong>pred</strong>: the prediction tensor, where the <cite>batch_axis</cite> dimension |
| ranges over batch size and <cite>axis</cite> dimension ranges over the number |
| of classes.</p></li> |
| <li><p><strong>label</strong>: the truth tensor. When <cite>sparse_label</cite> is True, <cite>label</cite>’s |
| shape should be <cite>pred</cite>’s shape with the <cite>axis</cite> dimension removed. |
| i.e. for <cite>pred</cite> with shape (1,2,3,4) and <cite>axis = 2</cite>, <cite>label</cite>’s shape |
| should be (1,2,4) and values should be integers between 0 and 2. If |
| <cite>sparse_label</cite> is False, <cite>label</cite>’s shape must be the same as <cite>pred</cite> |
| and values should be floats in the range <cite>[0, 1]</cite>.</p></li> |
| <li><p><strong>sample_weight</strong>: element-wise weighting tensor. Must be broadcastable |
| to the same shape as pred. For example, if pred has shape (64, 10) |
| and you want to weigh each sample in the batch separately, |
| sample_weight should have shape (64, 1).</p></li> |
| </ul> |
| </dd> |
| <dt>Outputs:</dt><dd><ul class="simple"> |
| <li><p><strong>loss</strong>: loss tensor with shape (batch_size,). Dimenions other than |
| batch_axis are averaged out.</p></li> |
| </ul> |
| </dd> |
| </dl> |
| <dl class="method"> |
| <dt id="mxnet.gluon.loss.SoftmaxCrossEntropyLoss.hybrid_forward"> |
| <code class="sig-name descname">hybrid_forward</code><span class="sig-paren">(</span><em class="sig-param">F</em>, <em class="sig-param">pred</em>, <em class="sig-param">label</em>, <em class="sig-param">sample_weight=None</em><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/mxnet/gluon/loss.html#SoftmaxCrossEntropyLoss.hybrid_forward"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.gluon.loss.SoftmaxCrossEntropyLoss.hybrid_forward" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Overrides to construct symbolic graph for this <cite>Block</cite>.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>x</strong> (<a class="reference internal" href="../../legacy/symbol/symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a><em> or </em><a class="reference internal" href="../../legacy/ndarray/ndarray.html#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – The first input tensor.</p></li> |
| <li><p><strong>*args</strong> (<em>list of Symbol</em><em> or </em><em>list of NDArray</em>) – Additional input tensors.</p></li> |
| </ul> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| </dd></dl> |
| |
| <dl class="attribute"> |
| <dt id="mxnet.gluon.loss.SoftmaxCELoss"> |
| <code class="sig-prename descclassname">mxnet.gluon.loss.</code><code class="sig-name descname">SoftmaxCELoss</code><a class="headerlink" href="#mxnet.gluon.loss.SoftmaxCELoss" title="Permalink to this definition">¶</a></dt> |
| <table class="longtable docutils align-default"> |
| <colgroup> |
| <col style="width: 10%" /> |
| <col style="width: 90%" /> |
| </colgroup> |
| <tbody> |
| <tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">hybrid_forward</span></code>(F, pred, label[, sample_weight])</p></td> |
| <td><p>Overrides to construct symbolic graph for this <cite>Block</cite>.</p></td> |
| </tr> |
| </tbody> |
| </table> |
| <p><strong>Methods</strong></p> |
| <dd><p>alias of <a class="reference internal" href="#mxnet.gluon.loss.SoftmaxCrossEntropyLoss" title="mxnet.gluon.loss.SoftmaxCrossEntropyLoss"><code class="xref py py-class docutils literal notranslate"><span class="pre">mxnet.gluon.loss.SoftmaxCrossEntropyLoss</span></code></a></p> |
| </dd></dl> |
| |
| <dl class="class"> |
| <dt id="mxnet.gluon.loss.KLDivLoss"> |
| <em class="property">class </em><code class="sig-prename descclassname">mxnet.gluon.loss.</code><code class="sig-name descname">KLDivLoss</code><span class="sig-paren">(</span><em class="sig-param">from_logits=True</em>, <em class="sig-param">axis=-1</em>, <em class="sig-param">weight=None</em>, <em class="sig-param">batch_axis=0</em>, <em class="sig-param">**kwargs</em><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/mxnet/gluon/loss.html#KLDivLoss"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.gluon.loss.KLDivLoss" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Bases: <a class="reference internal" href="#mxnet.gluon.loss.Loss" title="mxnet.gluon.loss.Loss"><code class="xref py py-class docutils literal notranslate"><span class="pre">mxnet.gluon.loss.Loss</span></code></a></p> |
| <p>The Kullback-Leibler divergence loss.</p> |
| <p>KL divergence measures the distance between contiguous distributions. It |
| can be used to minimize information loss when approximating a distribution. |
| If <cite>from_logits</cite> is True (default), loss is defined as:</p> |
| <div class="math notranslate nohighlight"> |
| \[L = \sum_i {label}_i * \big[\log({label}_i) - {pred}_i\big]\]</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.gluon.loss.KLDivLoss.hybrid_forward" title="mxnet.gluon.loss.KLDivLoss.hybrid_forward"><code class="xref py py-obj docutils literal notranslate"><span class="pre">hybrid_forward</span></code></a>(F, pred, label[, sample_weight])</p></td> |
| <td><p>Overrides to construct symbolic graph for this <cite>Block</cite>.</p></td> |
| </tr> |
| </tbody> |
| </table> |
| <p>If <cite>from_logits</cite> is False, loss is defined as:</p> |
| <div class="math notranslate nohighlight"> |
| \[ \begin{align}\begin{aligned}\DeclareMathOperator{softmax}{softmax}\\prob = \softmax({pred})\\L = \sum_i {label}_i * \big[\log({label}_i) - \log({prob}_i)\big]\end{aligned}\end{align} \]</div> |
| <p><cite>label</cite> and <cite>pred</cite> can have arbitrary shape as long as they have the same |
| number of elements.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>from_logits</strong> (bool, default is <cite>True</cite>) – Whether the input is log probability (usually from log_softmax) instead |
| of unnormalized numbers.</p></li> |
| <li><p><strong>axis</strong> (<em>int</em><em>, </em><em>default -1</em>) – The dimension along with to compute softmax. Only used when <cite>from_logits</cite> |
| is False.</p></li> |
| <li><p><strong>weight</strong> (<em>float</em><em> or </em><em>None</em>) – Global scalar weight for loss.</p></li> |
| <li><p><strong>batch_axis</strong> (<em>int</em><em>, </em><em>default 0</em>) – The axis that represents mini-batch.</p></li> |
| </ul> |
| </dd> |
| </dl> |
| <dl class="simple"> |
| <dt>Inputs:</dt><dd><ul class="simple"> |
| <li><p><strong>pred</strong>: prediction tensor with arbitrary shape. If <cite>from_logits</cite> is |
| True, <cite>pred</cite> should be log probabilities. Otherwise, it should be |
| unnormalized predictions, i.e. from a dense layer.</p></li> |
| <li><p><strong>label</strong>: truth tensor with values in range <cite>(0, 1)</cite>. Must have |
| the same size as <cite>pred</cite>.</p></li> |
| <li><p><strong>sample_weight</strong>: element-wise weighting tensor. Must be broadcastable |
| to the same shape as pred. For example, if pred has shape (64, 10) |
| and you want to weigh each sample in the batch separately, |
| sample_weight should have shape (64, 1).</p></li> |
| </ul> |
| </dd> |
| <dt>Outputs:</dt><dd><ul class="simple"> |
| <li><p><strong>loss</strong>: loss tensor with shape (batch_size,). Dimenions other than |
| batch_axis are averaged out.</p></li> |
| </ul> |
| </dd> |
| </dl> |
| <p class="rubric">References</p> |
| <p><a class="reference external" href="https://en.wikipedia.org/wiki/Kullback-Leibler_divergence">Kullback-Leibler divergence</a></p> |
| <dl class="method"> |
| <dt id="mxnet.gluon.loss.KLDivLoss.hybrid_forward"> |
| <code class="sig-name descname">hybrid_forward</code><span class="sig-paren">(</span><em class="sig-param">F</em>, <em class="sig-param">pred</em>, <em class="sig-param">label</em>, <em class="sig-param">sample_weight=None</em><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/mxnet/gluon/loss.html#KLDivLoss.hybrid_forward"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.gluon.loss.KLDivLoss.hybrid_forward" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Overrides to construct symbolic graph for this <cite>Block</cite>.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>x</strong> (<a class="reference internal" href="../../legacy/symbol/symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a><em> or </em><a class="reference internal" href="../../legacy/ndarray/ndarray.html#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – The first input tensor.</p></li> |
| <li><p><strong>*args</strong> (<em>list of Symbol</em><em> or </em><em>list of NDArray</em>) – Additional input tensors.</p></li> |
| </ul> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| </dd></dl> |
| |
| <dl class="class"> |
| <dt id="mxnet.gluon.loss.CTCLoss"> |
| <em class="property">class </em><code class="sig-prename descclassname">mxnet.gluon.loss.</code><code class="sig-name descname">CTCLoss</code><span class="sig-paren">(</span><em class="sig-param">layout='NTC'</em>, <em class="sig-param">label_layout='NT'</em>, <em class="sig-param">weight=None</em>, <em class="sig-param">**kwargs</em><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/mxnet/gluon/loss.html#CTCLoss"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.gluon.loss.CTCLoss" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Bases: <a class="reference internal" href="#mxnet.gluon.loss.Loss" title="mxnet.gluon.loss.Loss"><code class="xref py py-class docutils literal notranslate"><span class="pre">mxnet.gluon.loss.Loss</span></code></a></p> |
| <p>Connectionist Temporal Classification Loss.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>layout</strong> (<em>str</em><em>, </em><em>default 'NTC'</em>) – Layout of prediction tensor. ‘N’, ‘T’, ‘C’ stands for batch size, |
| sequence length, and alphabet_size respectively.</p></li> |
| <li><p><strong>label_layout</strong> (<em>str</em><em>, </em><em>default 'NT'</em>) – Layout of the labels. ‘N’, ‘T’ stands for batch size, and sequence |
| length respectively.</p></li> |
| <li><p><strong>weight</strong> (<em>float</em><em> or </em><em>None</em>) – Global scalar weight for loss.</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.gluon.loss.CTCLoss.hybrid_forward" title="mxnet.gluon.loss.CTCLoss.hybrid_forward"><code class="xref py py-obj docutils literal notranslate"><span class="pre">hybrid_forward</span></code></a>(F, pred, label[, …])</p></td> |
| <td><p>Overrides to construct symbolic graph for this <cite>Block</cite>.</p></td> |
| </tr> |
| </tbody> |
| </table> |
| <dl class="simple"> |
| <dt>Inputs:</dt><dd><ul class="simple"> |
| <li><p><strong>pred</strong>: unnormalized prediction tensor (before softmax). |
| Its shape depends on <cite>layout</cite>. If <cite>layout</cite> is ‘TNC’, pred |
| should have shape <cite>(sequence_length, batch_size, alphabet_size)</cite>. |
| Note that in the last dimension, index <cite>alphabet_size-1</cite> is reserved |
| for internal use as blank label. So <cite>alphabet_size</cite> is one plus the |
| actual alphabet size.</p></li> |
| <li><p><strong>label</strong>: zero-based label tensor. Its shape depends on <cite>label_layout</cite>. |
| If <cite>label_layout</cite> is ‘TN’, <cite>label</cite> should have shape |
| <cite>(label_sequence_length, batch_size)</cite>.</p></li> |
| <li><p><strong>pred_lengths</strong>: optional (default None), used for specifying the |
| length of each entry when different <cite>pred</cite> entries in the same batch |
| have different lengths. <cite>pred_lengths</cite> should have shape <cite>(batch_size,)</cite>.</p></li> |
| <li><p><strong>label_lengths</strong>: optional (default None), used for specifying the |
| length of each entry when different <cite>label</cite> entries in the same batch |
| have different lengths. <cite>label_lengths</cite> should have shape <cite>(batch_size,)</cite>.</p></li> |
| </ul> |
| </dd> |
| <dt>Outputs:</dt><dd><ul class="simple"> |
| <li><p><strong>loss</strong>: output loss has shape <cite>(batch_size,)</cite>.</p></li> |
| </ul> |
| </dd> |
| </dl> |
| <p><strong>Example</strong>: suppose the vocabulary is <cite>[a, b, c]</cite>, and in one batch we |
| have three sequences ‘ba’, ‘cbb’, and ‘abac’. We can index the labels as |
| <cite>{‘a’: 0, ‘b’: 1, ‘c’: 2, blank: 3}</cite>. Then <cite>alphabet_size</cite> should be 4, |
| where label 3 is reserved for internal use by <cite>CTCLoss</cite>. We then need to |
| pad each sequence with <cite>-1</cite> to make a rectangular <cite>label</cite> tensor:</p> |
| <div class="highlight-default notranslate"><div class="highlight"><pre><span></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="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">],</span> |
| <span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="o">-</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="mi">0</span><span class="p">,</span> <span class="mi">2</span><span class="p">]]</span> |
| </pre></div> |
| </div> |
| <p class="rubric">References</p> |
| <p><a class="reference external" href="http://www.cs.toronto.edu/~graves/icml_2006.pdf">Connectionist Temporal Classification: Labelling Unsegmented |
| Sequence Data with Recurrent Neural Networks</a></p> |
| <dl class="method"> |
| <dt id="mxnet.gluon.loss.CTCLoss.hybrid_forward"> |
| <code class="sig-name descname">hybrid_forward</code><span class="sig-paren">(</span><em class="sig-param">F</em>, <em class="sig-param">pred</em>, <em class="sig-param">label</em>, <em class="sig-param">pred_lengths=None</em>, <em class="sig-param">label_lengths=None</em>, <em class="sig-param">sample_weight=None</em><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/mxnet/gluon/loss.html#CTCLoss.hybrid_forward"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.gluon.loss.CTCLoss.hybrid_forward" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Overrides to construct symbolic graph for this <cite>Block</cite>.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>x</strong> (<a class="reference internal" href="../../legacy/symbol/symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a><em> or </em><a class="reference internal" href="../../legacy/ndarray/ndarray.html#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – The first input tensor.</p></li> |
| <li><p><strong>*args</strong> (<em>list of Symbol</em><em> or </em><em>list of NDArray</em>) – Additional input tensors.</p></li> |
| </ul> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| </dd></dl> |
| |
| <dl class="class"> |
| <dt id="mxnet.gluon.loss.HuberLoss"> |
| <em class="property">class </em><code class="sig-prename descclassname">mxnet.gluon.loss.</code><code class="sig-name descname">HuberLoss</code><span class="sig-paren">(</span><em class="sig-param">rho=1</em>, <em class="sig-param">weight=None</em>, <em class="sig-param">batch_axis=0</em>, <em class="sig-param">**kwargs</em><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/mxnet/gluon/loss.html#HuberLoss"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.gluon.loss.HuberLoss" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Bases: <a class="reference internal" href="#mxnet.gluon.loss.Loss" title="mxnet.gluon.loss.Loss"><code class="xref py py-class docutils literal notranslate"><span class="pre">mxnet.gluon.loss.Loss</span></code></a></p> |
| <p>Calculates smoothed L1 loss that is equal to L1 loss if absolute error |
| exceeds rho but is equal to L2 loss otherwise. Also called SmoothedL1 loss.</p> |
| <div class="math notranslate nohighlight"> |
| \[\begin{split}L = \sum_i \begin{cases} \frac{1}{2 {rho}} ({label}_i - {pred}_i)^2 & |
| \text{ if } |{label}_i - {pred}_i| < {rho} \\ |
| |{label}_i - {pred}_i| - \frac{{rho}}{2} & |
| \text{ otherwise } |
| \end{cases}\end{split}\]</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.gluon.loss.HuberLoss.hybrid_forward" title="mxnet.gluon.loss.HuberLoss.hybrid_forward"><code class="xref py py-obj docutils literal notranslate"><span class="pre">hybrid_forward</span></code></a>(F, pred, label[, sample_weight])</p></td> |
| <td><p>Overrides to construct symbolic graph for this <cite>Block</cite>.</p></td> |
| </tr> |
| </tbody> |
| </table> |
| <p><cite>label</cite> and <cite>pred</cite> can have arbitrary shape as long as they have the same |
| number of elements.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>rho</strong> (<em>float</em><em>, </em><em>default 1</em>) – Threshold for trimmed mean estimator.</p></li> |
| <li><p><strong>weight</strong> (<em>float</em><em> or </em><em>None</em>) – Global scalar weight for loss.</p></li> |
| <li><p><strong>batch_axis</strong> (<em>int</em><em>, </em><em>default 0</em>) – The axis that represents mini-batch.</p></li> |
| </ul> |
| </dd> |
| </dl> |
| <dl class="simple"> |
| <dt>Inputs:</dt><dd><ul class="simple"> |
| <li><p><strong>pred</strong>: prediction tensor with arbitrary shape</p></li> |
| <li><p><strong>label</strong>: target tensor with the same size as pred.</p></li> |
| <li><p><strong>sample_weight</strong>: element-wise weighting tensor. Must be broadcastable |
| to the same shape as pred. For example, if pred has shape (64, 10) |
| and you want to weigh each sample in the batch separately, |
| sample_weight should have shape (64, 1).</p></li> |
| </ul> |
| </dd> |
| <dt>Outputs:</dt><dd><ul class="simple"> |
| <li><p><strong>loss</strong>: loss tensor with shape (batch_size,). Dimenions other than |
| batch_axis are averaged out.</p></li> |
| </ul> |
| </dd> |
| </dl> |
| <dl class="method"> |
| <dt id="mxnet.gluon.loss.HuberLoss.hybrid_forward"> |
| <code class="sig-name descname">hybrid_forward</code><span class="sig-paren">(</span><em class="sig-param">F</em>, <em class="sig-param">pred</em>, <em class="sig-param">label</em>, <em class="sig-param">sample_weight=None</em><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/mxnet/gluon/loss.html#HuberLoss.hybrid_forward"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.gluon.loss.HuberLoss.hybrid_forward" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Overrides to construct symbolic graph for this <cite>Block</cite>.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>x</strong> (<a class="reference internal" href="../../legacy/symbol/symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a><em> or </em><a class="reference internal" href="../../legacy/ndarray/ndarray.html#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – The first input tensor.</p></li> |
| <li><p><strong>*args</strong> (<em>list of Symbol</em><em> or </em><em>list of NDArray</em>) – Additional input tensors.</p></li> |
| </ul> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| </dd></dl> |
| |
| <dl class="class"> |
| <dt id="mxnet.gluon.loss.HingeLoss"> |
| <em class="property">class </em><code class="sig-prename descclassname">mxnet.gluon.loss.</code><code class="sig-name descname">HingeLoss</code><span class="sig-paren">(</span><em class="sig-param">margin=1</em>, <em class="sig-param">weight=None</em>, <em class="sig-param">batch_axis=0</em>, <em class="sig-param">**kwargs</em><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/mxnet/gluon/loss.html#HingeLoss"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.gluon.loss.HingeLoss" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Bases: <a class="reference internal" href="#mxnet.gluon.loss.Loss" title="mxnet.gluon.loss.Loss"><code class="xref py py-class docutils literal notranslate"><span class="pre">mxnet.gluon.loss.Loss</span></code></a></p> |
| <p>Calculates the hinge loss function often used in SVMs:</p> |
| <div class="math notranslate nohighlight"> |
| \[L = \sum_i max(0, {margin} - {pred}_i \cdot {label}_i)\]</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.gluon.loss.HingeLoss.hybrid_forward" title="mxnet.gluon.loss.HingeLoss.hybrid_forward"><code class="xref py py-obj docutils literal notranslate"><span class="pre">hybrid_forward</span></code></a>(F, pred, label[, sample_weight])</p></td> |
| <td><p>Overrides to construct symbolic graph for this <cite>Block</cite>.</p></td> |
| </tr> |
| </tbody> |
| </table> |
| <p>where <cite>pred</cite> is the classifier prediction and <cite>label</cite> is the target tensor |
| containing values -1 or 1. <cite>label</cite> and <cite>pred</cite> must have the same number of |
| elements.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>margin</strong> (<em>float</em>) – The margin in hinge loss. Defaults to 1.0</p></li> |
| <li><p><strong>weight</strong> (<em>float</em><em> or </em><em>None</em>) – Global scalar weight for loss.</p></li> |
| <li><p><strong>batch_axis</strong> (<em>int</em><em>, </em><em>default 0</em>) – The axis that represents mini-batch.</p></li> |
| </ul> |
| </dd> |
| </dl> |
| <dl class="simple"> |
| <dt>Inputs:</dt><dd><ul class="simple"> |
| <li><p><strong>pred</strong>: prediction tensor with arbitrary shape.</p></li> |
| <li><p><strong>label</strong>: truth tensor with values -1 or 1. Must have the same size |
| as pred.</p></li> |
| <li><p><strong>sample_weight</strong>: element-wise weighting tensor. Must be broadcastable |
| to the same shape as pred. For example, if pred has shape (64, 10) |
| and you want to weigh each sample in the batch separately, |
| sample_weight should have shape (64, 1).</p></li> |
| </ul> |
| </dd> |
| <dt>Outputs:</dt><dd><ul class="simple"> |
| <li><p><strong>loss</strong>: loss tensor with shape (batch_size,). Dimenions other than |
| batch_axis are averaged out.</p></li> |
| </ul> |
| </dd> |
| </dl> |
| <dl class="method"> |
| <dt id="mxnet.gluon.loss.HingeLoss.hybrid_forward"> |
| <code class="sig-name descname">hybrid_forward</code><span class="sig-paren">(</span><em class="sig-param">F</em>, <em class="sig-param">pred</em>, <em class="sig-param">label</em>, <em class="sig-param">sample_weight=None</em><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/mxnet/gluon/loss.html#HingeLoss.hybrid_forward"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.gluon.loss.HingeLoss.hybrid_forward" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Overrides to construct symbolic graph for this <cite>Block</cite>.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>x</strong> (<a class="reference internal" href="../../legacy/symbol/symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a><em> or </em><a class="reference internal" href="../../legacy/ndarray/ndarray.html#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – The first input tensor.</p></li> |
| <li><p><strong>*args</strong> (<em>list of Symbol</em><em> or </em><em>list of NDArray</em>) – Additional input tensors.</p></li> |
| </ul> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| </dd></dl> |
| |
| <dl class="class"> |
| <dt id="mxnet.gluon.loss.SquaredHingeLoss"> |
| <em class="property">class </em><code class="sig-prename descclassname">mxnet.gluon.loss.</code><code class="sig-name descname">SquaredHingeLoss</code><span class="sig-paren">(</span><em class="sig-param">margin=1</em>, <em class="sig-param">weight=None</em>, <em class="sig-param">batch_axis=0</em>, <em class="sig-param">**kwargs</em><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/mxnet/gluon/loss.html#SquaredHingeLoss"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.gluon.loss.SquaredHingeLoss" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Bases: <a class="reference internal" href="#mxnet.gluon.loss.Loss" title="mxnet.gluon.loss.Loss"><code class="xref py py-class docutils literal notranslate"><span class="pre">mxnet.gluon.loss.Loss</span></code></a></p> |
| <p>Calculates the soft-margin loss function used in SVMs:</p> |
| <div class="math notranslate nohighlight"> |
| \[L = \sum_i max(0, {margin} - {pred}_i \cdot {label}_i)^2\]</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.gluon.loss.SquaredHingeLoss.hybrid_forward" title="mxnet.gluon.loss.SquaredHingeLoss.hybrid_forward"><code class="xref py py-obj docutils literal notranslate"><span class="pre">hybrid_forward</span></code></a>(F, pred, label[, sample_weight])</p></td> |
| <td><p>Overrides to construct symbolic graph for this <cite>Block</cite>.</p></td> |
| </tr> |
| </tbody> |
| </table> |
| <p>where <cite>pred</cite> is the classifier prediction and <cite>label</cite> is the target tensor |
| containing values -1 or 1. <cite>label</cite> and <cite>pred</cite> can have arbitrary shape as |
| long as they have the same number of elements.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>margin</strong> (<em>float</em>) – The margin in hinge loss. Defaults to 1.0</p></li> |
| <li><p><strong>weight</strong> (<em>float</em><em> or </em><em>None</em>) – Global scalar weight for loss.</p></li> |
| <li><p><strong>batch_axis</strong> (<em>int</em><em>, </em><em>default 0</em>) – The axis that represents mini-batch.</p></li> |
| </ul> |
| </dd> |
| </dl> |
| <dl class="simple"> |
| <dt>Inputs:</dt><dd><ul class="simple"> |
| <li><p><strong>pred</strong>: prediction tensor with arbitrary shape</p></li> |
| <li><p><strong>label</strong>: truth tensor with values -1 or 1. Must have the same size |
| as pred.</p></li> |
| <li><p><strong>sample_weight</strong>: element-wise weighting tensor. Must be broadcastable |
| to the same shape as pred. For example, if pred has shape (64, 10) |
| and you want to weigh each sample in the batch separately, |
| sample_weight should have shape (64, 1).</p></li> |
| </ul> |
| </dd> |
| <dt>Outputs:</dt><dd><ul class="simple"> |
| <li><p><strong>loss</strong>: loss tensor with shape (batch_size,). Dimenions other than |
| batch_axis are averaged out.</p></li> |
| </ul> |
| </dd> |
| </dl> |
| <dl class="method"> |
| <dt id="mxnet.gluon.loss.SquaredHingeLoss.hybrid_forward"> |
| <code class="sig-name descname">hybrid_forward</code><span class="sig-paren">(</span><em class="sig-param">F</em>, <em class="sig-param">pred</em>, <em class="sig-param">label</em>, <em class="sig-param">sample_weight=None</em><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/mxnet/gluon/loss.html#SquaredHingeLoss.hybrid_forward"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.gluon.loss.SquaredHingeLoss.hybrid_forward" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Overrides to construct symbolic graph for this <cite>Block</cite>.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>x</strong> (<a class="reference internal" href="../../legacy/symbol/symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a><em> or </em><a class="reference internal" href="../../legacy/ndarray/ndarray.html#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – The first input tensor.</p></li> |
| <li><p><strong>*args</strong> (<em>list of Symbol</em><em> or </em><em>list of NDArray</em>) – Additional input tensors.</p></li> |
| </ul> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| </dd></dl> |
| |
| <dl class="class"> |
| <dt id="mxnet.gluon.loss.LogisticLoss"> |
| <em class="property">class </em><code class="sig-prename descclassname">mxnet.gluon.loss.</code><code class="sig-name descname">LogisticLoss</code><span class="sig-paren">(</span><em class="sig-param">weight=None</em>, <em class="sig-param">batch_axis=0</em>, <em class="sig-param">label_format='signed'</em>, <em class="sig-param">**kwargs</em><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/mxnet/gluon/loss.html#LogisticLoss"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.gluon.loss.LogisticLoss" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Bases: <a class="reference internal" href="#mxnet.gluon.loss.Loss" title="mxnet.gluon.loss.Loss"><code class="xref py py-class docutils literal notranslate"><span class="pre">mxnet.gluon.loss.Loss</span></code></a></p> |
| <p>Calculates the logistic loss (for binary losses only):</p> |
| <div class="math notranslate nohighlight"> |
| \[L = \sum_i \log(1 + \exp(- {pred}_i \cdot {label}_i))\]</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.gluon.loss.LogisticLoss.hybrid_forward" title="mxnet.gluon.loss.LogisticLoss.hybrid_forward"><code class="xref py py-obj docutils literal notranslate"><span class="pre">hybrid_forward</span></code></a>(F, pred, label[, sample_weight])</p></td> |
| <td><p>Overrides to construct symbolic graph for this <cite>Block</cite>.</p></td> |
| </tr> |
| </tbody> |
| </table> |
| <p>where <cite>pred</cite> is the classifier prediction and <cite>label</cite> is the target tensor |
| containing values -1 or 1 (0 or 1 if <cite>label_format</cite> is binary). |
| <cite>label</cite> and <cite>pred</cite> can have arbitrary shape as long as they have the same number of elements.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>weight</strong> (<em>float</em><em> or </em><em>None</em>) – Global scalar weight for loss.</p></li> |
| <li><p><strong>batch_axis</strong> (<em>int</em><em>, </em><em>default 0</em>) – The axis that represents mini-batch.</p></li> |
| <li><p><strong>label_format</strong> (<em>str</em><em>, </em><em>default 'signed'</em>) – Can be either ‘signed’ or ‘binary’. If the label_format is ‘signed’, all label values should |
| be either -1 or 1. If the label_format is ‘binary’, all label values should be either |
| 0 or 1.</p></li> |
| <li><p><strong>Inputs</strong> – <ul> |
| <li><p><strong>pred</strong>: prediction tensor with arbitrary shape.</p></li> |
| <li><p><strong>label</strong>: truth tensor with values -1/1 (label_format is ‘signed’) |
| or 0/1 (label_format is ‘binary’). Must have the same size as pred.</p></li> |
| <li><p><strong>sample_weight</strong>: element-wise weighting tensor. Must be broadcastable |
| to the same shape as pred. For example, if pred has shape (64, 10) |
| and you want to weigh each sample in the batch separately, |
| sample_weight should have shape (64, 1).</p></li> |
| </ul> |
| </p></li> |
| <li><p><strong>Outputs</strong> – <ul> |
| <li><p><strong>loss</strong>: loss tensor with shape (batch_size,). Dimenions other than |
| batch_axis are averaged out.</p></li> |
| </ul> |
| </p></li> |
| </ul> |
| </dd> |
| </dl> |
| <dl class="method"> |
| <dt id="mxnet.gluon.loss.LogisticLoss.hybrid_forward"> |
| <code class="sig-name descname">hybrid_forward</code><span class="sig-paren">(</span><em class="sig-param">F</em>, <em class="sig-param">pred</em>, <em class="sig-param">label</em>, <em class="sig-param">sample_weight=None</em><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/mxnet/gluon/loss.html#LogisticLoss.hybrid_forward"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.gluon.loss.LogisticLoss.hybrid_forward" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Overrides to construct symbolic graph for this <cite>Block</cite>.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>x</strong> (<a class="reference internal" href="../../legacy/symbol/symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a><em> or </em><a class="reference internal" href="../../legacy/ndarray/ndarray.html#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – The first input tensor.</p></li> |
| <li><p><strong>*args</strong> (<em>list of Symbol</em><em> or </em><em>list of NDArray</em>) – Additional input tensors.</p></li> |
| </ul> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| </dd></dl> |
| |
| <dl class="class"> |
| <dt id="mxnet.gluon.loss.TripletLoss"> |
| <em class="property">class </em><code class="sig-prename descclassname">mxnet.gluon.loss.</code><code class="sig-name descname">TripletLoss</code><span class="sig-paren">(</span><em class="sig-param">margin=1</em>, <em class="sig-param">weight=None</em>, <em class="sig-param">batch_axis=0</em>, <em class="sig-param">**kwargs</em><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/mxnet/gluon/loss.html#TripletLoss"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.gluon.loss.TripletLoss" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Bases: <a class="reference internal" href="#mxnet.gluon.loss.Loss" title="mxnet.gluon.loss.Loss"><code class="xref py py-class docutils literal notranslate"><span class="pre">mxnet.gluon.loss.Loss</span></code></a></p> |
| <p>Calculates triplet loss given three input tensors and a positive margin. |
| Triplet loss measures the relative similarity between a positive |
| example, a negative example, and prediction:</p> |
| <div class="math notranslate nohighlight"> |
| \[L = \sum_i \max(\Vert {pos_i}_i - {pred} \Vert_2^2 - |
| \Vert {neg_i}_i - {pred} \Vert_2^2 + {margin}, 0)\]</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.gluon.loss.TripletLoss.hybrid_forward" title="mxnet.gluon.loss.TripletLoss.hybrid_forward"><code class="xref py py-obj docutils literal notranslate"><span class="pre">hybrid_forward</span></code></a>(F, pred, positive, negative)</p></td> |
| <td><p>Overrides to construct symbolic graph for this <cite>Block</cite>.</p></td> |
| </tr> |
| </tbody> |
| </table> |
| <p><cite>positive</cite>, <cite>negative</cite>, and ‘pred’ can have arbitrary shape as long as they |
| have the same number of elements.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>margin</strong> (<em>float</em>) – Margin of separation between correct and incorrect pair.</p></li> |
| <li><p><strong>weight</strong> (<em>float</em><em> or </em><em>None</em>) – Global scalar weight for loss.</p></li> |
| <li><p><strong>batch_axis</strong> (<em>int</em><em>, </em><em>default 0</em>) – The axis that represents mini-batch.</p></li> |
| </ul> |
| </dd> |
| </dl> |
| <dl class="simple"> |
| <dt>Inputs:</dt><dd><ul class="simple"> |
| <li><p><strong>pred</strong>: prediction tensor with arbitrary shape</p></li> |
| <li><p><strong>positive</strong>: positive example tensor with arbitrary shape. Must have |
| the same size as pred.</p></li> |
| <li><p><strong>negative</strong>: negative example tensor with arbitrary shape Must have |
| the same size as pred.</p></li> |
| </ul> |
| </dd> |
| <dt>Outputs:</dt><dd><ul class="simple"> |
| <li><p><strong>loss</strong>: loss tensor with shape (batch_size,).</p></li> |
| </ul> |
| </dd> |
| </dl> |
| <dl class="method"> |
| <dt id="mxnet.gluon.loss.TripletLoss.hybrid_forward"> |
| <code class="sig-name descname">hybrid_forward</code><span class="sig-paren">(</span><em class="sig-param">F</em>, <em class="sig-param">pred</em>, <em class="sig-param">positive</em>, <em class="sig-param">negative</em>, <em class="sig-param">sample_weight=None</em><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/mxnet/gluon/loss.html#TripletLoss.hybrid_forward"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.gluon.loss.TripletLoss.hybrid_forward" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Overrides to construct symbolic graph for this <cite>Block</cite>.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>x</strong> (<a class="reference internal" href="../../legacy/symbol/symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a><em> or </em><a class="reference internal" href="../../legacy/ndarray/ndarray.html#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – The first input tensor.</p></li> |
| <li><p><strong>*args</strong> (<em>list of Symbol</em><em> or </em><em>list of NDArray</em>) – Additional input tensors.</p></li> |
| </ul> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| </dd></dl> |
| |
| <dl class="class"> |
| <dt id="mxnet.gluon.loss.PoissonNLLLoss"> |
| <em class="property">class </em><code class="sig-prename descclassname">mxnet.gluon.loss.</code><code class="sig-name descname">PoissonNLLLoss</code><span class="sig-paren">(</span><em class="sig-param">weight=None</em>, <em class="sig-param">from_logits=True</em>, <em class="sig-param">batch_axis=0</em>, <em class="sig-param">compute_full=False</em>, <em class="sig-param">**kwargs</em><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/mxnet/gluon/loss.html#PoissonNLLLoss"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.gluon.loss.PoissonNLLLoss" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Bases: <a class="reference internal" href="#mxnet.gluon.loss.Loss" title="mxnet.gluon.loss.Loss"><code class="xref py py-class docutils literal notranslate"><span class="pre">mxnet.gluon.loss.Loss</span></code></a></p> |
| <p>For a target (Random Variable) in a Poisson distribution, the function calculates the Negative |
| Log likelihood loss. |
| PoissonNLLLoss measures the loss accrued from a poisson regression prediction made by the model.</p> |
| <div class="math notranslate nohighlight"> |
| \[L = \text{pred} - \text{target} * \log(\text{pred}) +\log(\text{target!})\]</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.gluon.loss.PoissonNLLLoss.hybrid_forward" title="mxnet.gluon.loss.PoissonNLLLoss.hybrid_forward"><code class="xref py py-obj docutils literal notranslate"><span class="pre">hybrid_forward</span></code></a>(F, pred, target[, …])</p></td> |
| <td><p>Overrides to construct symbolic graph for this <cite>Block</cite>.</p></td> |
| </tr> |
| </tbody> |
| </table> |
| <p><cite>target</cite>, ‘pred’ can have arbitrary shape as long as they have the same number of elements.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>from_logits</strong> (<em>boolean</em><em>, </em><em>default True</em>) – indicating whether log(predicted) value has already been computed. If True, the loss is computed as |
| <span class="math notranslate nohighlight">\(\exp(\text{pred}) - \text{target} * \text{pred}\)</span>, and if False, then loss is computed as |
| <span class="math notranslate nohighlight">\(\text{pred} - \text{target} * \log(\text{pred}+\text{epsilon})\)</span>.The default value</p></li> |
| <li><p><strong>weight</strong> (<em>float</em><em> or </em><em>None</em>) – Global scalar weight for loss.</p></li> |
| <li><p><strong>batch_axis</strong> (<em>int</em><em>, </em><em>default 0</em>) – The axis that represents mini-batch.</p></li> |
| <li><p><strong>compute_full</strong> (<em>boolean</em><em>, </em><em>default False</em>) – Indicates whether to add an approximation(Stirling factor) for the Factorial term in the formula for the loss. |
| The Stirling factor is: |
| <span class="math notranslate nohighlight">\(\text{target} * \log(\text{target}) - \text{target} + 0.5 * \log(2 * \pi * \text{target})\)</span></p></li> |
| <li><p><strong>epsilon</strong> (<em>float</em><em>, </em><em>default 1e-08</em>) – This is to avoid calculating log(0) which is not defined.</p></li> |
| </ul> |
| </dd> |
| </dl> |
| <dl class="simple"> |
| <dt>Inputs:</dt><dd><ul class="simple"> |
| <li><p><strong>pred</strong>: Predicted value</p></li> |
| <li><p><strong>target</strong>: Random variable(count or number) which belongs to a Poisson distribution.</p></li> |
| <li><p><strong>sample_weight</strong>: element-wise weighting tensor. Must be broadcastable |
| to the same shape as pred. For example, if pred has shape (64, 10) |
| and you want to weigh each sample in the batch separately, |
| sample_weight should have shape (64, 1).</p></li> |
| </ul> |
| </dd> |
| <dt>Outputs:</dt><dd><ul class="simple"> |
| <li><p><strong>loss</strong>: Average loss (shape=(1,1)) of the loss tensor with shape (batch_size,).</p></li> |
| </ul> |
| </dd> |
| </dl> |
| <dl class="method"> |
| <dt id="mxnet.gluon.loss.PoissonNLLLoss.hybrid_forward"> |
| <code class="sig-name descname">hybrid_forward</code><span class="sig-paren">(</span><em class="sig-param">F</em>, <em class="sig-param">pred</em>, <em class="sig-param">target</em>, <em class="sig-param">sample_weight=None</em>, <em class="sig-param">epsilon=1e-08</em><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/mxnet/gluon/loss.html#PoissonNLLLoss.hybrid_forward"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.gluon.loss.PoissonNLLLoss.hybrid_forward" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Overrides to construct symbolic graph for this <cite>Block</cite>.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>x</strong> (<a class="reference internal" href="../../legacy/symbol/symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a><em> or </em><a class="reference internal" href="../../legacy/ndarray/ndarray.html#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – The first input tensor.</p></li> |
| <li><p><strong>*args</strong> (<em>list of Symbol</em><em> or </em><em>list of NDArray</em>) – Additional input tensors.</p></li> |
| </ul> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| </dd></dl> |
| |
| <dl class="class"> |
| <dt id="mxnet.gluon.loss.CosineEmbeddingLoss"> |
| <em class="property">class </em><code class="sig-prename descclassname">mxnet.gluon.loss.</code><code class="sig-name descname">CosineEmbeddingLoss</code><span class="sig-paren">(</span><em class="sig-param">weight=None</em>, <em class="sig-param">batch_axis=0</em>, <em class="sig-param">margin=0</em>, <em class="sig-param">**kwargs</em><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/mxnet/gluon/loss.html#CosineEmbeddingLoss"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.gluon.loss.CosineEmbeddingLoss" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Bases: <a class="reference internal" href="#mxnet.gluon.loss.Loss" title="mxnet.gluon.loss.Loss"><code class="xref py py-class docutils literal notranslate"><span class="pre">mxnet.gluon.loss.Loss</span></code></a></p> |
| <p>For a target label 1 or -1, vectors input1 and input2, the function computes the cosine distance |
| between the vectors. This can be interpreted as how similar/dissimilar two input vectors are.</p> |
| <div class="math notranslate nohighlight"> |
| \[\begin{split}L = \sum_i \begin{cases} 1 - {cos\_sim({input1}_i, {input2}_i)} & \text{ if } {label}_i = 1\\ |
| {cos\_sim({input1}_i, {input2}_i)} & \text{ if } {label}_i = -1 \end{cases}\\ |
| cos\_sim(input1, input2) = \frac{{input1}_i.{input2}_i}{||{input1}_i||.||{input2}_i||}\end{split}\]</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.gluon.loss.CosineEmbeddingLoss.hybrid_forward" title="mxnet.gluon.loss.CosineEmbeddingLoss.hybrid_forward"><code class="xref py py-obj docutils literal notranslate"><span class="pre">hybrid_forward</span></code></a>(F, input1, input2, label[, …])</p></td> |
| <td><p>Overrides to construct symbolic graph for this <cite>Block</cite>.</p></td> |
| </tr> |
| </tbody> |
| </table> |
| <p><cite>input1</cite>, <cite>input2</cite> can have arbitrary shape as long as they have the same number of elements.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>weight</strong> (<em>float</em><em> or </em><em>None</em>) – Global scalar weight for loss.</p></li> |
| <li><p><strong>batch_axis</strong> (<em>int</em><em>, </em><em>default 0</em>) – The axis that represents mini-batch.</p></li> |
| <li><p><strong>margin</strong> (<em>float</em>) – Margin of separation between correct and incorrect pair.</p></li> |
| </ul> |
| </dd> |
| </dl> |
| <dl class="simple"> |
| <dt>Inputs:</dt><dd><ul class="simple"> |
| <li><p><strong>input1</strong>: a tensor with arbitrary shape</p></li> |
| <li><p><strong>input2</strong>: another tensor with same shape as pred to which input1 is |
| compared for similarity and loss calculation</p></li> |
| <li><p><strong>label</strong>: A 1-D tensor indicating for each pair input1 and input2, target label is 1 or -1</p></li> |
| <li><p><strong>sample_weight</strong>: element-wise weighting tensor. Must be broadcastable |
| to the same shape as input1. For example, if input1 has shape (64, 10) |
| and you want to weigh each sample in the batch separately, |
| sample_weight should have shape (64, 1).</p></li> |
| </ul> |
| </dd> |
| <dt>Outputs:</dt><dd><ul class="simple"> |
| <li><p><strong>loss</strong>: The loss tensor with shape (batch_size,).</p></li> |
| </ul> |
| </dd> |
| </dl> |
| <dl class="method"> |
| <dt id="mxnet.gluon.loss.CosineEmbeddingLoss.hybrid_forward"> |
| <code class="sig-name descname">hybrid_forward</code><span class="sig-paren">(</span><em class="sig-param">F</em>, <em class="sig-param">input1</em>, <em class="sig-param">input2</em>, <em class="sig-param">label</em>, <em class="sig-param">sample_weight=None</em><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/mxnet/gluon/loss.html#CosineEmbeddingLoss.hybrid_forward"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.gluon.loss.CosineEmbeddingLoss.hybrid_forward" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Overrides to construct symbolic graph for this <cite>Block</cite>.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>x</strong> (<a class="reference internal" href="../../legacy/symbol/symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a><em> or </em><a class="reference internal" href="../../legacy/ndarray/ndarray.html#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – The first input tensor.</p></li> |
| <li><p><strong>*args</strong> (<em>list of Symbol</em><em> or </em><em>list of NDArray</em>) – Additional input tensors.</p></li> |
| </ul> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| </dd></dl> |
| |
| <dl class="class"> |
| <dt id="mxnet.gluon.loss.SDMLLoss"> |
| <em class="property">class </em><code class="sig-prename descclassname">mxnet.gluon.loss.</code><code class="sig-name descname">SDMLLoss</code><span class="sig-paren">(</span><em class="sig-param">smoothing_parameter=0.3</em>, <em class="sig-param">weight=1.0</em>, <em class="sig-param">batch_axis=0</em>, <em class="sig-param">**kwargs</em><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/mxnet/gluon/loss.html#SDMLLoss"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.gluon.loss.SDMLLoss" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Bases: <a class="reference internal" href="#mxnet.gluon.loss.Loss" title="mxnet.gluon.loss.Loss"><code class="xref py py-class docutils literal notranslate"><span class="pre">mxnet.gluon.loss.Loss</span></code></a></p> |
| <p>Calculates Batchwise Smoothed Deep Metric Learning (SDML) Loss given two input tensors and a smoothing weight |
| SDM Loss learns similarity between paired samples by using unpaired samples in the minibatch |
| as potential negative examples.</p> |
| <p>The loss is described in greater detail in |
| “Large Scale Question Paraphrase Retrieval with Smoothed Deep Metric Learning.” |
| - by Bonadiman, Daniele, Anjishnu Kumar, and Arpit Mittal. arXiv preprint arXiv:1905.12786 (2019). |
| URL: https://arxiv.org/pdf/1905.12786.pdf</p> |
| <p>According to the authors, this loss formulation achieves comparable or higher accuracy to |
| Triplet Loss but converges much faster. |
| The loss assumes that the items in both tensors in each minibatch |
| are aligned such that x1[0] corresponds to x2[0] and all other datapoints in the minibatch are unrelated. |
| <cite>x1</cite> and <cite>x2</cite> are minibatches of vectors.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>smoothing_parameter</strong> (<em>float</em>) – Probability mass to be distributed over the minibatch. Must be < 1.0.</p></li> |
| <li><p><strong>weight</strong> (<em>float</em><em> or </em><em>None</em>) – Global scalar weight for loss.</p></li> |
| <li><p><strong>batch_axis</strong> (<em>int</em><em>, </em><em>default 0</em>) – The axis that represents mini-batch.</p></li> |
| <li><p><strong>Inputs</strong> – <ul> |
| <li><p><strong>x1</strong>: Minibatch of data points with shape (batch_size, vector_dim)</p></li> |
| <li><p><strong>x2</strong>: Minibatch of data points with shape (batch_size, vector_dim) |
| Each item in x2 is a positive sample for the same index in x1. |
| That is, x1[0] and x2[0] form a positive pair, x1[1] and x2[1] form a positive pair - and so on. |
| All data points in different rows should be decorrelated</p></li> |
| </ul> |
| </p></li> |
| <li><p><strong>Outputs</strong> – <ul> |
| <li><p><strong>loss</strong>: loss tensor with shape (batch_size,).</p></li> |
| </ul> |
| </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.gluon.loss.SDMLLoss.hybrid_forward" title="mxnet.gluon.loss.SDMLLoss.hybrid_forward"><code class="xref py py-obj docutils literal notranslate"><span class="pre">hybrid_forward</span></code></a>(F, x1, x2)</p></td> |
| <td><p>the function computes the kl divergence between the negative distances</p></td> |
| </tr> |
| </tbody> |
| </table> |
| <dl class="method"> |
| <dt id="mxnet.gluon.loss.SDMLLoss.hybrid_forward"> |
| <code class="sig-name descname">hybrid_forward</code><span class="sig-paren">(</span><em class="sig-param">F</em>, <em class="sig-param">x1</em>, <em class="sig-param">x2</em><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/mxnet/gluon/loss.html#SDMLLoss.hybrid_forward"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.gluon.loss.SDMLLoss.hybrid_forward" title="Permalink to this definition">¶</a></dt> |
| <dd><p>the function computes the kl divergence between the negative distances |
| (internally it compute a softmax casting into probabilities) and the |
| identity matrix.</p> |
| <p>This assumes that the two batches are aligned therefore the more similar |
| vector should be the one having the same id.</p> |
| <p>Batch1 Batch2</p> |
| <p>President of France French President |
| President of US American President</p> |
| <p>Given the question president of France in batch 1 the model will |
| learn to predict french president comparing it with all the other |
| vectors in batch 2</p> |
| </dd></dl> |
| |
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