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| <span class="mdl-layout-title toc">Table Of Contents</span> |
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| <li class="toctree-l1"><a class="reference internal" href="../../../tutorials/index.html">Python Tutorials</a><ul> |
| <li class="toctree-l2"><a class="reference internal" href="../../../tutorials/getting-started/index.html">Getting Started</a><ul> |
| <li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/index.html">Crash Course</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/0-introduction.html">Introduction</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/1-nparray.html">Step 1: Manipulate data with NP on MXNet</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/2-create-nn.html">Step 2: Create a neural network</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/3-autograd.html">Step 3: Automatic differentiation with autograd</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/4-components.html">Step 4: Necessary components that are not in the network</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html">Step 5: <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html#Using-own-data-with-included-Datasets">Using own data with included <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html#Using-your-own-data-with-custom-Datasets">Using your own data with custom <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html#New-in-MXNet-2.0:-faster-C++-backend-dataloaders">New in MXNet 2.0: faster C++ backend dataloaders</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/6-train-nn.html">Step 6: Train a Neural Network</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/7-use-gpus.html">Step 7: Load and Run a NN using GPU</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/to-mxnet/index.html">Moving to MXNet from Other Frameworks</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/to-mxnet/pytorch.html">PyTorch vs Apache MXNet</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/gluon_from_experiment_to_deployment.html">Gluon: from experiment to deployment</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/gluon_migration_guide.html">Gluon2.0: Migration Guide</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/logistic_regression_explained.html">Logistic regression explained</a></li> |
| <li class="toctree-l3"><a class="reference external" href="https://mxnet.apache.org/api/python/docs/tutorials/packages/gluon/image/mnist.html">MNIST</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l2"><a class="reference internal" href="../../../tutorials/packages/index.html">Packages</a><ul> |
| <li class="toctree-l3"><a class="reference internal" href="../../../tutorials/packages/autograd/index.html">Automatic Differentiation</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../tutorials/packages/gluon/index.html">Gluon</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/packages/gluon/blocks/index.html">Blocks</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/blocks/custom-layer.html">Custom Layers</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/blocks/hybridize.html">Hybridize</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/blocks/init.html">Initialization</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/blocks/naming.html">Parameter and Block Naming</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/blocks/nn.html">Layers and Blocks</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/blocks/parameters.html">Parameter Management</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/blocks/save_load_params.html">Saving and Loading Gluon Models</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/blocks/activations/activations.html">Activation Blocks</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/packages/gluon/data/index.html">Data Tutorials</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/data/data_augmentation.html">Image Augmentation</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/data/datasets.html">Gluon <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/data/datasets.html#Using-own-data-with-included-Datasets">Using own data with included <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/data/datasets.html#Using-own-data-with-custom-Datasets">Using own data with custom <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/data/datasets.html#Appendix:-Upgrading-from-Module-DataIter-to-Gluon-DataLoader">Appendix: Upgrading from Module <code class="docutils literal notranslate"><span class="pre">DataIter</span></code> to Gluon <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li> |
| </ul> |
| </li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/packages/gluon/image/index.html">Image Tutorials</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/image/info_gan.html">Image similarity search with InfoGAN</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/image/mnist.html">Handwritten Digit Recognition</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/packages/gluon/loss/index.html">Losses</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/loss/custom-loss.html">Custom Loss Blocks</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/loss/kl_divergence.html">Kullback-Leibler (KL) Divergence</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/loss/loss.html">Loss functions</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/packages/gluon/text/index.html">Text Tutorials</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/text/gnmt.html">Google Neural Machine Translation</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/text/transformer.html">Machine Translation with Transformer</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/packages/gluon/training/index.html">Training</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/training/fit_api_tutorial.html">MXNet Gluon Fit API</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/training/trainer.html">Trainer</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/training/learning_rates/index.html">Learning Rates</a><ul> |
| <li class="toctree-l6"><a class="reference internal" href="../../../tutorials/packages/gluon/training/learning_rates/learning_rate_finder.html">Learning Rate Finder</a></li> |
| <li class="toctree-l6"><a class="reference internal" href="../../../tutorials/packages/gluon/training/learning_rates/learning_rate_schedules.html">Learning Rate Schedules</a></li> |
| <li class="toctree-l6"><a class="reference internal" href="../../../tutorials/packages/gluon/training/learning_rates/learning_rate_schedules_advanced.html">Advanced Learning Rate Schedules</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/training/normalization/index.html">Normalization Blocks</a></li> |
| </ul> |
| </li> |
| </ul> |
| </li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../tutorials/packages/kvstore/index.html">KVStore</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/packages/kvstore/kvstore.html">Distributed Key-Value Store</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../tutorials/packages/legacy/index.html">Legacy</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/packages/legacy/ndarray/index.html">NDArray</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/legacy/ndarray/01-ndarray-intro.html">An Intro: Manipulate Data the MXNet Way with NDArray</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/legacy/ndarray/02-ndarray-operations.html">NDArray Operations</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/legacy/ndarray/03-ndarray-contexts.html">NDArray Contexts</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/legacy/ndarray/gotchas_numpy_in_mxnet.html">Gotchas using NumPy in Apache MXNet</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/legacy/ndarray/sparse/index.html">Tutorials</a><ul> |
| <li class="toctree-l6"><a class="reference internal" href="../../../tutorials/packages/legacy/ndarray/sparse/csr.html">CSRNDArray - NDArray in Compressed Sparse Row Storage Format</a></li> |
| <li class="toctree-l6"><a class="reference internal" href="../../../tutorials/packages/legacy/ndarray/sparse/row_sparse.html">RowSparseNDArray - NDArray for Sparse Gradient Updates</a></li> |
| </ul> |
| </li> |
| </ul> |
| </li> |
| </ul> |
| </li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../tutorials/packages/np/index.html">What is NP on MXNet</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/packages/np/cheat-sheet.html">The NP on MXNet cheat sheet</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/packages/np/np-vs-numpy.html">Differences between NP on MXNet and NumPy</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../tutorials/packages/onnx/index.html">ONNX</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/packages/onnx/fine_tuning_gluon.html">Fine-tuning an ONNX model</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/packages/onnx/inference_on_onnx_model.html">Running inference on MXNet/Gluon from an ONNX model</a></li> |
| <li class="toctree-l4"><a class="reference external" href="https://mxnet.apache.org/api/python/docs/tutorials/deploy/export/onnx.html">Export ONNX Models</a></li> |
| </ul> |
| </li> |
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| <li class="toctree-l4"><a class="reference internal" href="../../np/routines.linalg.html">Linear algebra (<code class="xref py py-mod docutils literal notranslate"><span class="pre">numpy.linalg</span></code>)</a><ul> |
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| </li> |
| </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> |
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| <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> |
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| <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> |
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| <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> |
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| <span class="mdl-layout-title toc">Table Of Contents</span> |
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| <nav class="mdl-navigation"> |
| <ul class="current"> |
| <li class="toctree-l1"><a class="reference internal" href="../../../tutorials/index.html">Python Tutorials</a><ul> |
| <li class="toctree-l2"><a class="reference internal" href="../../../tutorials/getting-started/index.html">Getting Started</a><ul> |
| <li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/index.html">Crash Course</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/0-introduction.html">Introduction</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/1-nparray.html">Step 1: Manipulate data with NP on MXNet</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/2-create-nn.html">Step 2: Create a neural network</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/3-autograd.html">Step 3: Automatic differentiation with autograd</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/4-components.html">Step 4: Necessary components that are not in the network</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html">Step 5: <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html#Using-own-data-with-included-Datasets">Using own data with included <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html#Using-your-own-data-with-custom-Datasets">Using your own data with custom <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html#New-in-MXNet-2.0:-faster-C++-backend-dataloaders">New in MXNet 2.0: faster C++ backend dataloaders</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/6-train-nn.html">Step 6: Train a Neural Network</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/7-use-gpus.html">Step 7: Load and Run a NN using GPU</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/to-mxnet/index.html">Moving to MXNet from Other Frameworks</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/to-mxnet/pytorch.html">PyTorch vs Apache MXNet</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/gluon_from_experiment_to_deployment.html">Gluon: from experiment to deployment</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/gluon_migration_guide.html">Gluon2.0: Migration Guide</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/logistic_regression_explained.html">Logistic regression explained</a></li> |
| <li class="toctree-l3"><a class="reference external" href="https://mxnet.apache.org/api/python/docs/tutorials/packages/gluon/image/mnist.html">MNIST</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l2"><a class="reference internal" href="../../../tutorials/packages/index.html">Packages</a><ul> |
| <li class="toctree-l3"><a class="reference internal" href="../../../tutorials/packages/autograd/index.html">Automatic Differentiation</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../tutorials/packages/gluon/index.html">Gluon</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/packages/gluon/blocks/index.html">Blocks</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/blocks/custom-layer.html">Custom Layers</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/blocks/hybridize.html">Hybridize</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/blocks/init.html">Initialization</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/blocks/naming.html">Parameter and Block Naming</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/blocks/nn.html">Layers and Blocks</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/blocks/parameters.html">Parameter Management</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/blocks/save_load_params.html">Saving and Loading Gluon Models</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/blocks/activations/activations.html">Activation Blocks</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/packages/gluon/data/index.html">Data Tutorials</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/data/data_augmentation.html">Image Augmentation</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/data/datasets.html">Gluon <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/data/datasets.html#Using-own-data-with-included-Datasets">Using own data with included <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/data/datasets.html#Using-own-data-with-custom-Datasets">Using own data with custom <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/data/datasets.html#Appendix:-Upgrading-from-Module-DataIter-to-Gluon-DataLoader">Appendix: Upgrading from Module <code class="docutils literal notranslate"><span class="pre">DataIter</span></code> to Gluon <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li> |
| </ul> |
| </li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/packages/gluon/image/index.html">Image Tutorials</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/image/info_gan.html">Image similarity search with InfoGAN</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/image/mnist.html">Handwritten Digit Recognition</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/packages/gluon/loss/index.html">Losses</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/loss/custom-loss.html">Custom Loss Blocks</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/loss/kl_divergence.html">Kullback-Leibler (KL) Divergence</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/loss/loss.html">Loss functions</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/packages/gluon/text/index.html">Text Tutorials</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/text/gnmt.html">Google Neural Machine Translation</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/text/transformer.html">Machine Translation with Transformer</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/packages/gluon/training/index.html">Training</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/training/fit_api_tutorial.html">MXNet Gluon Fit API</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/training/trainer.html">Trainer</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/training/learning_rates/index.html">Learning Rates</a><ul> |
| <li class="toctree-l6"><a class="reference internal" href="../../../tutorials/packages/gluon/training/learning_rates/learning_rate_finder.html">Learning Rate Finder</a></li> |
| <li class="toctree-l6"><a class="reference internal" href="../../../tutorials/packages/gluon/training/learning_rates/learning_rate_schedules.html">Learning Rate Schedules</a></li> |
| <li class="toctree-l6"><a class="reference internal" href="../../../tutorials/packages/gluon/training/learning_rates/learning_rate_schedules_advanced.html">Advanced Learning Rate Schedules</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/training/normalization/index.html">Normalization Blocks</a></li> |
| </ul> |
| </li> |
| </ul> |
| </li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../tutorials/packages/kvstore/index.html">KVStore</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/packages/kvstore/kvstore.html">Distributed Key-Value Store</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../tutorials/packages/legacy/index.html">Legacy</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/packages/legacy/ndarray/index.html">NDArray</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/legacy/ndarray/01-ndarray-intro.html">An Intro: Manipulate Data the MXNet Way with NDArray</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/legacy/ndarray/02-ndarray-operations.html">NDArray Operations</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/legacy/ndarray/03-ndarray-contexts.html">NDArray Contexts</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/legacy/ndarray/gotchas_numpy_in_mxnet.html">Gotchas using NumPy in Apache MXNet</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/legacy/ndarray/sparse/index.html">Tutorials</a><ul> |
| <li class="toctree-l6"><a class="reference internal" href="../../../tutorials/packages/legacy/ndarray/sparse/csr.html">CSRNDArray - NDArray in Compressed Sparse Row Storage Format</a></li> |
| <li class="toctree-l6"><a class="reference internal" href="../../../tutorials/packages/legacy/ndarray/sparse/row_sparse.html">RowSparseNDArray - NDArray for Sparse Gradient Updates</a></li> |
| </ul> |
| </li> |
| </ul> |
| </li> |
| </ul> |
| </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> |
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| <li class="toctree-l3"><a class="reference internal" href="../../../tutorials/packages/onnx/index.html">ONNX</a><ul> |
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| <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/visualize_graph">Visualize networks</a></li> |
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| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/performance/compression/int8.html">Deploy with int-8</a></li> |
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| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/performance/backend/dnnl/dnnl_readme.html">Install MXNet with oneDNN</a></li> |
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| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/performance/backend/dnnl/dnnl_quantization_inc.html">Improving accuracy with Intel® Neural Compressor</a></li> |
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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> |
| </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> |
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| <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> |
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| <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> |
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| <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> |
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| <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> |
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| </header> |
| |
| <div class="document"> |
| <div class="page-content" role="main"> |
| |
| <div class="section" id="gluon-contrib"> |
| <h1>gluon.contrib<a class="headerlink" href="#gluon-contrib" title="Permalink to this headline">¶</a></h1> |
| <p>This document lists the contrib APIs in Gluon:</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="#module-mxnet.gluon.contrib" title="mxnet.gluon.contrib"><code class="xref py py-obj docutils literal notranslate"><span class="pre">mxnet.gluon.contrib</span></code></a></p></td> |
| <td><p>Contrib neural network module.</p></td> |
| </tr> |
| </tbody> |
| </table> |
| <p>The <cite>Gluon Contrib</cite> API, defined in the <cite>gluon.contrib</cite> package, provides |
| many useful experimental APIs for new features. |
| This is a place for the community to try out the new features, |
| so that feature contributors can receive feedback.</p> |
| <div class="admonition warning"> |
| <p class="admonition-title">Warning</p> |
| <p>This package contains experimental APIs and may change in the near future.</p> |
| </div> |
| <p>In the rest of this document, we list routines provided by the <cite>gluon.contrib</cite> package.</p> |
| <div class="section" id="vision-data"> |
| <h2>Vision Data<a class="headerlink" href="#vision-data" title="Permalink to this headline">¶</a></h2> |
| <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">data.vision.create_image_augment</span></code></p></td> |
| <td><p>Creates an augmenter block.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">data.vision.ImageDataLoader</span></code></p></td> |
| <td><p>Image data loader with a large number of augmentation choices.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">data.vision.ImageBboxDataLoader</span></code></p></td> |
| <td><p>Image iterator with a large number of augmentation choices for detection.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">data.vision.ImageBboxRandomFlipLeftRight</span></code></p></td> |
| <td><p>Randomly flip the input image and bbox left to right with a probability of p(0.5 by default).</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">data.vision.ImageBboxCrop</span></code></p></td> |
| <td><p>Crops the image <cite>src</cite> and <cite>bbox</cite> to the given <cite>crop</cite>.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">data.vision.ImageBboxRandomCropWithConstraints</span></code></p></td> |
| <td><p>Crop an image randomly with bounding box constraints.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">data.vision.ImageBboxResize</span></code></p></td> |
| <td><p>Apply resize to image and bounding boxes.</p></td> |
| </tr> |
| </tbody> |
| </table> |
| </div> |
| <div class="section" id="estimator"> |
| <h2>Estimator<a class="headerlink" href="#estimator" title="Permalink to this headline">¶</a></h2> |
| <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.contrib.estimator.Estimator" title="mxnet.gluon.contrib.estimator.Estimator"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Estimator</span></code></a></p></td> |
| <td><p>Estimator Class for easy model training</p></td> |
| </tr> |
| </tbody> |
| </table> |
| </div> |
| <div class="section" id="event-handler"> |
| <h2>Event Handler<a class="headerlink" href="#event-handler" title="Permalink to this headline">¶</a></h2> |
| <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.contrib.estimator.StoppingHandler" title="mxnet.gluon.contrib.estimator.StoppingHandler"><code class="xref py py-obj docutils literal notranslate"><span class="pre">StoppingHandler</span></code></a></p></td> |
| <td><p>Stop conditions to stop training Stop training if maximum number of batches or epochs reached.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.gluon.contrib.estimator.MetricHandler" title="mxnet.gluon.contrib.estimator.MetricHandler"><code class="xref py py-obj docutils literal notranslate"><span class="pre">MetricHandler</span></code></a></p></td> |
| <td><p>Metric Handler that update metric values at batch end</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.gluon.contrib.estimator.ValidationHandler" title="mxnet.gluon.contrib.estimator.ValidationHandler"><code class="xref py py-obj docutils literal notranslate"><span class="pre">ValidationHandler</span></code></a></p></td> |
| <td><p>Validation Handler that evaluate model on validation dataset</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.gluon.contrib.estimator.LoggingHandler" title="mxnet.gluon.contrib.estimator.LoggingHandler"><code class="xref py py-obj docutils literal notranslate"><span class="pre">LoggingHandler</span></code></a></p></td> |
| <td><p>Basic Logging Handler that applies to every Gluon estimator by default.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.gluon.contrib.estimator.CheckpointHandler" title="mxnet.gluon.contrib.estimator.CheckpointHandler"><code class="xref py py-obj docutils literal notranslate"><span class="pre">CheckpointHandler</span></code></a></p></td> |
| <td><p>Save the model after user define period</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.gluon.contrib.estimator.EarlyStoppingHandler" title="mxnet.gluon.contrib.estimator.EarlyStoppingHandler"><code class="xref py py-obj docutils literal notranslate"><span class="pre">EarlyStoppingHandler</span></code></a></p></td> |
| <td><p>Early stop training if monitored value is not improving</p></td> |
| </tr> |
| </tbody> |
| </table> |
| </div> |
| <div class="section" id="module-mxnet.gluon.contrib"> |
| <span id="api-reference"></span><h2>API Reference<a class="headerlink" href="#module-mxnet.gluon.contrib" title="Permalink to this headline">¶</a></h2> |
| <p>Contrib neural network module.</p> |
| <span class="target" id="module-mxnet.gluon.contrib.estimator"></span><p>Gluon Estimator Module</p> |
| <dl class="class"> |
| <dt id="mxnet.gluon.contrib.estimator.BatchProcessor"> |
| <em class="property">class </em><code class="sig-name descname">BatchProcessor</code><a class="reference internal" href="../../../_modules/mxnet/gluon/contrib/estimator/batch_processor.html#BatchProcessor"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.gluon.contrib.estimator.BatchProcessor" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">object</span></code></p> |
| <p>BatchProcessor Class for plug and play fit_batch & evaluate_batch</p> |
| <p>During training or validation, data are divided into minibatches for processing. This |
| class aims at providing hooks of training or validating on a minibatch of data. Users |
| may provide customized fit_batch() and evaluate_batch() methods by inheriting from |
| this class and overriding class methods.</p> |
| <p><a class="reference internal" href="#mxnet.gluon.contrib.estimator.BatchProcessor" title="mxnet.gluon.contrib.estimator.BatchProcessor"><code class="xref py py-class docutils literal notranslate"><span class="pre">BatchProcessor</span></code></a> can be used to replace fit_batch() and evaluate_batch() |
| in the base estimator class</p> |
| <dl class="method"> |
| <dt id="mxnet.gluon.contrib.estimator.BatchProcessor.evaluate_batch"> |
| <code class="sig-name descname">evaluate_batch</code><span class="sig-paren">(</span><em class="sig-param">estimator</em>, <em class="sig-param">val_batch</em>, <em class="sig-param">batch_axis=0</em><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/mxnet/gluon/contrib/estimator/batch_processor.html#BatchProcessor.evaluate_batch"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.gluon.contrib.estimator.BatchProcessor.evaluate_batch" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Evaluate the estimator model on a batch of validation data.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>estimator</strong> (<a class="reference internal" href="#mxnet.gluon.contrib.estimator.Estimator" title="mxnet.gluon.contrib.estimator.Estimator"><em>Estimator</em></a>) – Reference to the estimator</p></li> |
| <li><p><strong>val_batch</strong> (<em>tuple</em>) – Data and label of a batch from the validation data loader.</p></li> |
| <li><p><strong>batch_axis</strong> (<em>int</em><em>, </em><em>default 0</em>) – Batch axis to split the validation data into devices.</p></li> |
| </ul> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.contrib.estimator.BatchProcessor.fit_batch"> |
| <code class="sig-name descname">fit_batch</code><span class="sig-paren">(</span><em class="sig-param">estimator</em>, <em class="sig-param">train_batch</em>, <em class="sig-param">batch_axis=0</em><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/mxnet/gluon/contrib/estimator/batch_processor.html#BatchProcessor.fit_batch"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.gluon.contrib.estimator.BatchProcessor.fit_batch" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Trains the estimator model on a batch of training data.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>estimator</strong> (<a class="reference internal" href="#mxnet.gluon.contrib.estimator.Estimator" title="mxnet.gluon.contrib.estimator.Estimator"><em>Estimator</em></a>) – Reference to the estimator</p></li> |
| <li><p><strong>train_batch</strong> (<em>tuple</em>) – Data and label of a batch from the training data loader.</p></li> |
| <li><p><strong>batch_axis</strong> (<em>int</em><em>, </em><em>default 0</em>) – Batch axis to split the training data into devices.</p></li> |
| </ul> |
| </dd> |
| <dt class="field-even">Returns</dt> |
| <dd class="field-even"><p><ul class="simple"> |
| <li><p><strong>data</strong> (<em>List of NDArray</em>) – Sharded data from the batch. Data is sharded with |
| <cite>gluon.split_and_load</cite>.</p></li> |
| <li><p><strong>label</strong> (<em>List of NDArray</em>) – Sharded label from the batch. Labels are sharded with |
| <cite>gluon.split_and_load</cite>.</p></li> |
| <li><p><strong>pred</strong> (<em>List of NDArray</em>) – Prediction on each of the sharded inputs.</p></li> |
| <li><p><strong>loss</strong> (<em>List of NDArray</em>) – Loss on each of the sharded inputs.</p></li> |
| </ul> |
| </p> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| </dd></dl> |
| |
| <dl class="class"> |
| <dt id="mxnet.gluon.contrib.estimator.CheckpointHandler"> |
| <em class="property">class </em><code class="sig-name descname">CheckpointHandler</code><span class="sig-paren">(</span><em class="sig-param">model_dir</em>, <em class="sig-param">model_prefix='model'</em>, <em class="sig-param">monitor=None</em>, <em class="sig-param">verbose=0</em>, <em class="sig-param">save_best=False</em>, <em class="sig-param">mode='auto'</em>, <em class="sig-param">epoch_period=1</em>, <em class="sig-param">batch_period=None</em>, <em class="sig-param">max_checkpoints=5</em>, <em class="sig-param">resume_from_checkpoint=False</em><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/mxnet/gluon/contrib/estimator/event_handler.html#CheckpointHandler"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.gluon.contrib.estimator.CheckpointHandler" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">mxnet.gluon.contrib.estimator.event_handler.TrainBegin</span></code>, <code class="xref py py-class docutils literal notranslate"><span class="pre">mxnet.gluon.contrib.estimator.event_handler.BatchEnd</span></code>, <code class="xref py py-class docutils literal notranslate"><span class="pre">mxnet.gluon.contrib.estimator.event_handler.EpochEnd</span></code></p> |
| <p>Save the model after user define period</p> |
| <p><a class="reference internal" href="#mxnet.gluon.contrib.estimator.CheckpointHandler" title="mxnet.gluon.contrib.estimator.CheckpointHandler"><code class="xref py py-class docutils literal notranslate"><span class="pre">CheckpointHandler</span></code></a> saves the network architecture after first batch if the model |
| can be fully hybridized, saves model parameters and trainer states after user defined period, |
| default saves every epoch.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>model_dir</strong> (<em>str</em>) – File directory to save all the model related files including model architecture, |
| model parameters, and trainer states.</p></li> |
| <li><p><strong>model_prefix</strong> (<em>str default 'model'</em>) – Prefix to add for all checkpoint file names.</p></li> |
| <li><p><strong>monitor</strong> (<a class="reference internal" href="../metric/index.html#mxnet.gluon.metric.EvalMetric" title="mxnet.gluon.metric.EvalMetric"><em>EvalMetric</em></a><em>, </em><em>default None</em>) – The metrics to monitor and determine if model has improved</p></li> |
| <li><p><strong>verbose</strong> (<em>int</em><em>, </em><em>default 0</em>) – Verbosity mode, 1 means inform user every time a checkpoint is saved</p></li> |
| <li><p><strong>save_best</strong> (<em>bool</em><em>, </em><em>default False</em>) – If True, monitor must not be None, <a class="reference internal" href="#mxnet.gluon.contrib.estimator.CheckpointHandler" title="mxnet.gluon.contrib.estimator.CheckpointHandler"><code class="xref py py-class docutils literal notranslate"><span class="pre">CheckpointHandler</span></code></a> will save the |
| model parameters and trainer states with the best monitored value.</p></li> |
| <li><p><strong>mode</strong> (<em>str</em><em>, </em><em>default 'auto'</em>) – One of {auto, min, max}, if <cite>save_best=True</cite>, the comparison to make |
| and determine if the monitored value has improved. if ‘auto’ mode, |
| <a class="reference internal" href="#mxnet.gluon.contrib.estimator.CheckpointHandler" title="mxnet.gluon.contrib.estimator.CheckpointHandler"><code class="xref py py-class docutils literal notranslate"><span class="pre">CheckpointHandler</span></code></a> will try to use min or max based on |
| the monitored metric name.</p></li> |
| <li><p><strong>epoch_period</strong> (<em>int</em><em>, </em><em>default 1</em>) – Epoch intervals between saving the network. By default, checkpoints are |
| saved every epoch.</p></li> |
| <li><p><strong>batch_period</strong> (<em>int</em><em>, </em><em>default None</em>) – Batch intervals between saving the network. |
| By default, checkpoints are not saved based on the number of batches.</p></li> |
| <li><p><strong>max_checkpoints</strong> (<em>int</em><em>, </em><em>default 5</em>) – Maximum number of checkpoint files to keep in the model_dir, older checkpoints |
| will be removed. Best checkpoint file is not counted.</p></li> |
| <li><p><strong>resume_from_checkpoint</strong> (<em>bool</em><em>, </em><em>default False</em>) – Whether to resume training from checkpoint in model_dir. If True and checkpoints |
| found, <a class="reference internal" href="#mxnet.gluon.contrib.estimator.CheckpointHandler" title="mxnet.gluon.contrib.estimator.CheckpointHandler"><code class="xref py py-class docutils literal notranslate"><span class="pre">CheckpointHandler</span></code></a> will load net parameters and trainer states, |
| and train the remaining of epochs and batches.</p></li> |
| </ul> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="class"> |
| <dt id="mxnet.gluon.contrib.estimator.EarlyStoppingHandler"> |
| <em class="property">class </em><code class="sig-name descname">EarlyStoppingHandler</code><span class="sig-paren">(</span><em class="sig-param">monitor</em>, <em class="sig-param">min_delta=0</em>, <em class="sig-param">patience=0</em>, <em class="sig-param">mode='auto'</em>, <em class="sig-param">baseline=None</em><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/mxnet/gluon/contrib/estimator/event_handler.html#EarlyStoppingHandler"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.gluon.contrib.estimator.EarlyStoppingHandler" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">mxnet.gluon.contrib.estimator.event_handler.TrainBegin</span></code>, <code class="xref py py-class docutils literal notranslate"><span class="pre">mxnet.gluon.contrib.estimator.event_handler.EpochEnd</span></code>, <code class="xref py py-class docutils literal notranslate"><span class="pre">mxnet.gluon.contrib.estimator.event_handler.TrainEnd</span></code></p> |
| <p>Early stop training if monitored value is not improving</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>monitor</strong> (<a class="reference internal" href="../metric/index.html#mxnet.gluon.metric.EvalMetric" title="mxnet.gluon.metric.EvalMetric"><em>EvalMetric</em></a>) – The metric to monitor, and stop training if this metric does not improve.</p></li> |
| <li><p><strong>min_delta</strong> (<em>float</em><em>, </em><em>default 0</em>) – Minimal change in monitored value to be considered as an improvement.</p></li> |
| <li><p><strong>patience</strong> (<em>int</em><em>, </em><em>default 0</em>) – Number of epochs to wait for improvement before terminate training.</p></li> |
| <li><p><strong>mode</strong> (<em>str</em><em>, </em><em>default 'auto'</em>) – One of {auto, min, max}, if <cite>save_best_only=True</cite>, the comparison to make |
| and determine if the monitored value has improved. if ‘auto’ mode, checkpoint |
| handler will try to use min or max based on the monitored metric name.</p></li> |
| <li><p><strong>baseline</strong> (<em>float</em>) – Baseline value to compare the monitored value with.</p></li> |
| </ul> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="class"> |
| <dt id="mxnet.gluon.contrib.estimator.Estimator"> |
| <em class="property">class </em><code class="sig-name descname">Estimator</code><span class="sig-paren">(</span><em class="sig-param">net</em>, <em class="sig-param">loss</em>, <em class="sig-param">train_metrics=None</em>, <em class="sig-param">val_metrics=None</em>, <em class="sig-param">initializer=None</em>, <em class="sig-param">trainer=None</em>, <em class="sig-param">device=None</em>, <em class="sig-param">val_net=None</em>, <em class="sig-param">val_loss=None</em>, <em class="sig-param">batch_processor=None</em><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/mxnet/gluon/contrib/estimator/estimator.html#Estimator"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.gluon.contrib.estimator.Estimator" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">object</span></code></p> |
| <p>Estimator Class for easy model training</p> |
| <p><a class="reference internal" href="#mxnet.gluon.contrib.estimator.Estimator" title="mxnet.gluon.contrib.estimator.Estimator"><code class="xref py py-class docutils literal notranslate"><span class="pre">Estimator</span></code></a> can be used to facilitate the training & validation process</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>net</strong> (<a class="reference internal" href="../block.html#mxnet.gluon.Block" title="mxnet.gluon.Block"><em>gluon.Block</em></a>) – The model used for training.</p></li> |
| <li><p><strong>loss</strong> (<a class="reference internal" href="../loss/index.html#mxnet.gluon.loss.Loss" title="mxnet.gluon.loss.Loss"><em>gluon.loss.Loss</em></a>) – Loss (objective) function to calculate during training.</p></li> |
| <li><p><strong>train_metrics</strong> (<a class="reference internal" href="../metric/index.html#mxnet.gluon.metric.EvalMetric" title="mxnet.gluon.metric.EvalMetric"><em>EvalMetric</em></a><em> or </em><em>list of EvalMetric</em>) – Training metrics for evaluating models on training dataset.</p></li> |
| <li><p><strong>val_metrics</strong> (<a class="reference internal" href="../metric/index.html#mxnet.gluon.metric.EvalMetric" title="mxnet.gluon.metric.EvalMetric"><em>EvalMetric</em></a><em> or </em><em>list of EvalMetric</em>) – Validation metrics for evaluating models on validation dataset.</p></li> |
| <li><p><strong>initializer</strong> (<a class="reference internal" href="../../initializer/index.html#mxnet.initializer.Initializer" title="mxnet.initializer.Initializer"><em>Initializer</em></a>) – Initializer to initialize the network.</p></li> |
| <li><p><strong>trainer</strong> (<a class="reference internal" href="../trainer.html#mxnet.gluon.Trainer" title="mxnet.gluon.Trainer"><em>Trainer</em></a>) – Trainer to apply optimizer on network parameters.</p></li> |
| <li><p><strong>device</strong> (<a class="reference internal" href="../../device/index.html#mxnet.device.Device" title="mxnet.device.Device"><em>Device</em></a><em> or </em><em>list of Device</em>) – Device(s) to run the training on.</p></li> |
| <li><p><strong>val_net</strong> (<a class="reference internal" href="../block.html#mxnet.gluon.Block" title="mxnet.gluon.Block"><em>gluon.Block</em></a>) – <p>The model used for validation. The validation model does not necessarily belong to |
| the same model class as the training model. But the two models typically share the |
| same architecture. Therefore the validation model can reuse parameters of the |
| training model.</p> |
| <p>The code example of consruction of val_net sharing the same network parameters as |
| the training net is given below:</p> |
| <div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">net</span> <span class="o">=</span> <span class="n">_get_train_network</span><span class="p">()</span> |
| <span class="gp">>>> </span><span class="n">val_net</span> <span class="o">=</span> <span class="n">_get_test_network</span><span class="p">()</span> |
| <span class="gp">>>> </span><span class="n">val_net</span><span class="o">.</span><span class="n">share_parameters</span><span class="p">(</span><span class="n">net</span><span class="o">.</span><span class="n">collect_params</span><span class="p">())</span> |
| <span class="gp">>>> </span><span class="n">net</span><span class="o">.</span><span class="n">initialize</span><span class="p">(</span><span class="n">device</span><span class="o">=</span><span class="n">device</span><span class="p">)</span> |
| <span class="gp">>>> </span><span class="n">est</span> <span class="o">=</span> <span class="n">Estimator</span><span class="p">(</span><span class="n">net</span><span class="p">,</span> <span class="n">loss</span><span class="p">,</span> <span class="n">val_net</span><span class="o">=</span><span class="n">val_net</span><span class="p">)</span> |
| </pre></div> |
| </div> |
| <p>Proper namespace match is required for weight sharing between two networks. Most networks |
| inheriting <code class="xref py py-class docutils literal notranslate"><span class="pre">Block</span></code> can share their parameters correctly. An exception is |
| Sequential networks that Block scope must be specified for correct weight sharing. For |
| the naming in mxnet Gluon API, please refer to the site |
| (<a class="reference external" href="https://mxnet.apache.org/api/python/docs/tutorials/packages/gluon/blocks/naming.html">https://mxnet.apache.org/api/python/docs/tutorials/packages/gluon/blocks/naming.html</a>) |
| for future information.</p> |
| </p></li> |
| <li><p><strong>val_loss</strong> (<em>gluon.loss.loss</em>) – Loss (objective) function to calculate during validation. If set val_loss |
| None, it will use the same loss function as self.loss</p></li> |
| <li><p><strong>batch_processor</strong> (<a class="reference internal" href="#mxnet.gluon.contrib.estimator.BatchProcessor" title="mxnet.gluon.contrib.estimator.BatchProcessor"><em>BatchProcessor</em></a>) – BatchProcessor provides customized fit_batch() and evaluate_batch() methods</p></li> |
| </ul> |
| </dd> |
| </dl> |
| <dl class="method"> |
| <dt id="mxnet.gluon.contrib.estimator.Estimator.evaluate"> |
| <code class="sig-name descname">evaluate</code><span class="sig-paren">(</span><em class="sig-param">val_data</em>, <em class="sig-param">batch_axis=0</em>, <em class="sig-param">event_handlers=None</em><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/mxnet/gluon/contrib/estimator/estimator.html#Estimator.evaluate"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.gluon.contrib.estimator.Estimator.evaluate" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Evaluate model on validation data.</p> |
| <p>This function calls <code class="xref py py-func docutils literal notranslate"><span class="pre">evaluate_batch()</span></code> on each of the batches from the |
| validation data loader. Thus, for custom use cases, it’s possible to inherit the |
| estimator class and override <code class="xref py py-func docutils literal notranslate"><span class="pre">evaluate_batch()</span></code>.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>val_data</strong> (<a class="reference internal" href="../data/index.html#mxnet.gluon.data.DataLoader" title="mxnet.gluon.data.DataLoader"><em>DataLoader</em></a>) – Validation data loader with data and labels.</p></li> |
| <li><p><strong>batch_axis</strong> (<em>int</em><em>, </em><em>default 0</em>) – Batch axis to split the validation data into devices.</p></li> |
| <li><p><strong>event_handlers</strong> (<em>EventHandler</em><em> or </em><em>list of EventHandler</em>) – List of <code class="xref py py-class docutils literal notranslate"><span class="pre">EventHandlers</span></code> to apply during validation. Besides |
| event handlers specified here, a default MetricHandler and a LoggingHandler |
| will be added if not specified explicitly.</p></li> |
| </ul> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.contrib.estimator.Estimator.fit"> |
| <code class="sig-name descname">fit</code><span class="sig-paren">(</span><em class="sig-param">train_data</em>, <em class="sig-param">val_data=None</em>, <em class="sig-param">epochs=None</em>, <em class="sig-param">event_handlers=None</em>, <em class="sig-param">batches=None</em>, <em class="sig-param">batch_axis=0</em><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/mxnet/gluon/contrib/estimator/estimator.html#Estimator.fit"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.gluon.contrib.estimator.Estimator.fit" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Trains the model with a given <code class="xref py py-class docutils literal notranslate"><span class="pre">DataLoader</span></code> for a specified |
| number of epochs or batches. The batch size is inferred from the |
| data loader’s batch_size.</p> |
| <p>This function calls <code class="xref py py-func docutils literal notranslate"><span class="pre">fit_batch()</span></code> on each of the batches from the |
| training data loader. Thus, for custom use cases, it’s possible to inherit the |
| estimator class and override <code class="xref py py-func docutils literal notranslate"><span class="pre">fit_batch()</span></code>.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>train_data</strong> (<a class="reference internal" href="../data/index.html#mxnet.gluon.data.DataLoader" title="mxnet.gluon.data.DataLoader"><em>DataLoader</em></a>) – Training data loader with data and labels.</p></li> |
| <li><p><strong>val_data</strong> (<a class="reference internal" href="../data/index.html#mxnet.gluon.data.DataLoader" title="mxnet.gluon.data.DataLoader"><em>DataLoader</em></a><em>, </em><em>default None</em>) – Validation data loader with data and labels.</p></li> |
| <li><p><strong>epochs</strong> (<em>int</em><em>, </em><em>default None</em>) – Number of epochs to iterate on the training data. |
| You can only specify one and only one type of iteration(epochs or batches).</p></li> |
| <li><p><strong>event_handlers</strong> (<em>EventHandler</em><em> or </em><em>list of EventHandler</em>) – List of <code class="xref py py-class docutils literal notranslate"><span class="pre">EventHandlers</span></code> to apply during training. Besides |
| the event handlers specified here, a StoppingHandler, |
| LoggingHandler and MetricHandler will be added by default if not |
| yet specified manually. If validation data is provided, a |
| ValidationHandler is also added if not already specified.</p></li> |
| <li><p><strong>batches</strong> (<em>int</em><em>, </em><em>default None</em>) – Number of batches to iterate on the training data. |
| You can only specify one and only one type of iteration(epochs or batches).</p></li> |
| <li><p><strong>batch_axis</strong> (<em>int</em><em>, </em><em>default 0</em>) – Batch axis to split the training data into devices.</p></li> |
| </ul> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="attribute"> |
| <dt id="mxnet.gluon.contrib.estimator.Estimator.logger"> |
| <code class="sig-name descname">logger</code><em class="property"> = None</em><a class="headerlink" href="#mxnet.gluon.contrib.estimator.Estimator.logger" title="Permalink to this definition">¶</a></dt> |
| <dd><p>logging.Logger object associated with the Estimator.</p> |
| <p>The logger is used for all logs generated by this estimator and its |
| handlers. A new logging.Logger is created during Estimator construction and |
| configured to write all logs with level logging.INFO or higher to |
| sys.stdout.</p> |
| <p>You can modify the logging settings using the standard Python methods. For |
| example, to save logs to a file in addition to printing them to stdout |
| output, you can attach a logging.FileHandler to the logger.</p> |
| <div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">est</span> <span class="o">=</span> <span class="n">Estimator</span><span class="p">(</span><span class="n">net</span><span class="p">,</span> <span class="n">loss</span><span class="p">)</span> |
| <span class="gp">>>> </span><span class="kn">import</span> <span class="nn">logging</span> |
| <span class="gp">>>> </span><span class="n">est</span><span class="o">.</span><span class="n">logger</span><span class="o">.</span><span class="n">addHandler</span><span class="p">(</span><span class="n">logging</span><span class="o">.</span><span class="n">FileHandler</span><span class="p">(</span><span class="n">filename</span><span class="p">))</span> |
| </pre></div> |
| </div> |
| </dd></dl> |
| |
| </dd></dl> |
| |
| <dl class="class"> |
| <dt id="mxnet.gluon.contrib.estimator.GradientUpdateHandler"> |
| <em class="property">class </em><code class="sig-name descname">GradientUpdateHandler</code><span class="sig-paren">(</span><em class="sig-param">priority=-2000</em><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/mxnet/gluon/contrib/estimator/event_handler.html#GradientUpdateHandler"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.gluon.contrib.estimator.GradientUpdateHandler" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">mxnet.gluon.contrib.estimator.event_handler.BatchEnd</span></code></p> |
| <p>Gradient Update Handler that apply gradients on network weights</p> |
| <p><a class="reference internal" href="#mxnet.gluon.contrib.estimator.GradientUpdateHandler" title="mxnet.gluon.contrib.estimator.GradientUpdateHandler"><code class="xref py py-class docutils literal notranslate"><span class="pre">GradientUpdateHandler</span></code></a> takes the priority level. It updates weight parameters |
| at the end of each batch</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><p><strong>priority</strong> (<em>scalar</em><em>, </em><em>default -2000</em>) – priority level of the gradient update handler. Priority level is sorted in ascending |
| order. The lower the number is, the higher priority level the handler is.</p> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="class"> |
| <dt id="mxnet.gluon.contrib.estimator.LoggingHandler"> |
| <em class="property">class </em><code class="sig-name descname">LoggingHandler</code><span class="sig-paren">(</span><em class="sig-param">log_interval='epoch'</em>, <em class="sig-param">metrics=None</em>, <em class="sig-param">priority=inf</em><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/mxnet/gluon/contrib/estimator/event_handler.html#LoggingHandler"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.gluon.contrib.estimator.LoggingHandler" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">mxnet.gluon.contrib.estimator.event_handler.TrainBegin</span></code>, <code class="xref py py-class docutils literal notranslate"><span class="pre">mxnet.gluon.contrib.estimator.event_handler.TrainEnd</span></code>, <code class="xref py py-class docutils literal notranslate"><span class="pre">mxnet.gluon.contrib.estimator.event_handler.EpochBegin</span></code>, <code class="xref py py-class docutils literal notranslate"><span class="pre">mxnet.gluon.contrib.estimator.event_handler.EpochEnd</span></code>, <code class="xref py py-class docutils literal notranslate"><span class="pre">mxnet.gluon.contrib.estimator.event_handler.BatchBegin</span></code>, <code class="xref py py-class docutils literal notranslate"><span class="pre">mxnet.gluon.contrib.estimator.event_handler.BatchEnd</span></code></p> |
| <p>Basic Logging Handler that applies to every Gluon estimator by default.</p> |
| <p><a class="reference internal" href="#mxnet.gluon.contrib.estimator.LoggingHandler" title="mxnet.gluon.contrib.estimator.LoggingHandler"><code class="xref py py-class docutils literal notranslate"><span class="pre">LoggingHandler</span></code></a> logs hyper-parameters, training statistics, |
| and other useful information during training</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>log_interval</strong> (<em>int</em><em> or </em><em>str</em><em>, </em><em>default 'epoch'</em>) – Logging interval during training. |
| log_interval=’epoch’: display metrics every epoch |
| log_interval=integer k: display metrics every interval of k batches</p></li> |
| <li><p><strong>metrics</strong> (<em>list of EvalMetrics</em>) – Metrics to be logged, logged at batch end, epoch end, train end.</p></li> |
| <li><p><strong>priority</strong> (<em>scalar</em><em>, </em><em>default np.Inf</em>) – Priority level of the LoggingHandler. Priority level is sorted in |
| ascending order. The lower the number is, the higher priority level the |
| handler is.</p></li> |
| </ul> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="class"> |
| <dt id="mxnet.gluon.contrib.estimator.MetricHandler"> |
| <em class="property">class </em><code class="sig-name descname">MetricHandler</code><span class="sig-paren">(</span><em class="sig-param">metrics</em>, <em class="sig-param">priority=-1000</em><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/mxnet/gluon/contrib/estimator/event_handler.html#MetricHandler"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.gluon.contrib.estimator.MetricHandler" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">mxnet.gluon.contrib.estimator.event_handler.EpochBegin</span></code>, <code class="xref py py-class docutils literal notranslate"><span class="pre">mxnet.gluon.contrib.estimator.event_handler.BatchEnd</span></code></p> |
| <p>Metric Handler that update metric values at batch end</p> |
| <p><a class="reference internal" href="#mxnet.gluon.contrib.estimator.MetricHandler" title="mxnet.gluon.contrib.estimator.MetricHandler"><code class="xref py py-class docutils literal notranslate"><span class="pre">MetricHandler</span></code></a> takes model predictions and true labels |
| and update the metrics, it also update metric wrapper for loss with loss values. |
| Validation loss and metrics will be handled by <a class="reference internal" href="#mxnet.gluon.contrib.estimator.ValidationHandler" title="mxnet.gluon.contrib.estimator.ValidationHandler"><code class="xref py py-class docutils literal notranslate"><span class="pre">ValidationHandler</span></code></a></p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>metrics</strong> (<em>List of EvalMetrics</em>) – Metrics to be updated at batch end.</p></li> |
| <li><p><strong>priority</strong> (<em>scalar</em>) – Priority level of the MetricHandler. Priority level is sorted in ascending |
| order. The lower the number is, the higher priority level the handler is.</p></li> |
| </ul> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="class"> |
| <dt id="mxnet.gluon.contrib.estimator.StoppingHandler"> |
| <em class="property">class </em><code class="sig-name descname">StoppingHandler</code><span class="sig-paren">(</span><em class="sig-param">max_epoch=None</em>, <em class="sig-param">max_batch=None</em><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/mxnet/gluon/contrib/estimator/event_handler.html#StoppingHandler"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.gluon.contrib.estimator.StoppingHandler" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">mxnet.gluon.contrib.estimator.event_handler.TrainBegin</span></code>, <code class="xref py py-class docutils literal notranslate"><span class="pre">mxnet.gluon.contrib.estimator.event_handler.BatchEnd</span></code>, <code class="xref py py-class docutils literal notranslate"><span class="pre">mxnet.gluon.contrib.estimator.event_handler.EpochEnd</span></code></p> |
| <p>Stop conditions to stop training |
| Stop training if maximum number of batches or epochs |
| reached.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>max_epoch</strong> (<em>int</em><em>, </em><em>default None</em>) – Number of maximum epochs to train.</p></li> |
| <li><p><strong>max_batch</strong> (<em>int</em><em>, </em><em>default None</em>) – Number of maximum batches to train.</p></li> |
| </ul> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="class"> |
| <dt id="mxnet.gluon.contrib.estimator.ValidationHandler"> |
| <em class="property">class </em><code class="sig-name descname">ValidationHandler</code><span class="sig-paren">(</span><em class="sig-param">val_data</em>, <em class="sig-param">eval_fn</em>, <em class="sig-param">epoch_period=1</em>, <em class="sig-param">batch_period=None</em>, <em class="sig-param">priority=-1000</em>, <em class="sig-param">event_handlers=None</em><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/mxnet/gluon/contrib/estimator/event_handler.html#ValidationHandler"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.gluon.contrib.estimator.ValidationHandler" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">mxnet.gluon.contrib.estimator.event_handler.TrainBegin</span></code>, <code class="xref py py-class docutils literal notranslate"><span class="pre">mxnet.gluon.contrib.estimator.event_handler.BatchEnd</span></code>, <code class="xref py py-class docutils literal notranslate"><span class="pre">mxnet.gluon.contrib.estimator.event_handler.EpochEnd</span></code></p> |
| <p>Validation Handler that evaluate model on validation dataset</p> |
| <p><a class="reference internal" href="#mxnet.gluon.contrib.estimator.ValidationHandler" title="mxnet.gluon.contrib.estimator.ValidationHandler"><code class="xref py py-class docutils literal notranslate"><span class="pre">ValidationHandler</span></code></a> takes validation dataset, an evaluation function, |
| metrics to be evaluated, and how often to run the validation. You can provide custom |
| evaluation function or use the one provided my <a class="reference internal" href="#mxnet.gluon.contrib.estimator.Estimator" title="mxnet.gluon.contrib.estimator.Estimator"><code class="xref py py-class docutils literal notranslate"><span class="pre">Estimator</span></code></a></p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>val_data</strong> (<a class="reference internal" href="../data/index.html#mxnet.gluon.data.DataLoader" title="mxnet.gluon.data.DataLoader"><em>DataLoader</em></a>) – Validation data set to run evaluation.</p></li> |
| <li><p><strong>eval_fn</strong> (<em>function</em>) – A function defines how to run evaluation and |
| calculate loss and metrics.</p></li> |
| <li><p><strong>epoch_period</strong> (<em>int</em><em>, </em><em>default 1</em>) – How often to run validation at epoch end, by default |
| <a class="reference internal" href="#mxnet.gluon.contrib.estimator.ValidationHandler" title="mxnet.gluon.contrib.estimator.ValidationHandler"><code class="xref py py-class docutils literal notranslate"><span class="pre">ValidationHandler</span></code></a> validate every epoch.</p></li> |
| <li><p><strong>batch_period</strong> (<em>int</em><em>, </em><em>default None</em>) – How often to run validation at batch end, by default |
| <a class="reference internal" href="#mxnet.gluon.contrib.estimator.ValidationHandler" title="mxnet.gluon.contrib.estimator.ValidationHandler"><code class="xref py py-class docutils literal notranslate"><span class="pre">ValidationHandler</span></code></a> does not validate at batch end.</p></li> |
| <li><p><strong>priority</strong> (<em>scalar</em><em>, </em><em>default -1000</em>) – Priority level of the ValidationHandler. Priority level is sorted in |
| ascending order. The lower the number is, the higher priority level the |
| handler is.</p></li> |
| <li><p><strong>event_handlers</strong> (<em>EventHandler</em><em> or </em><em>list of EventHandlers</em>) – List of <code class="xref py py-class docutils literal notranslate"><span class="pre">EventHandler</span></code> to apply during validaiton. This argument |
| is used by self.eval_fn function in order to process customized event |
| handlers.</p></li> |
| </ul> |
| </dd> |
| </dl> |
| </dd></dl> |
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