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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> |
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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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| |
| <div class="document"> |
| <div class="page-content" role="main"> |
| |
| <div class="section" id="gluon-data"> |
| <h1>gluon.data<a class="headerlink" href="#gluon-data" title="Permalink to this headline">¶</a></h1> |
| <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.data" title="mxnet.gluon.data"><code class="xref py py-obj docutils literal notranslate"><span class="pre">mxnet.gluon.data</span></code></a></p></td> |
| <td><p>Dataset utilities.</p></td> |
| </tr> |
| </tbody> |
| </table> |
| <div class="section" id="datasets"> |
| <h2>Datasets<a class="headerlink" href="#datasets" 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.data.Dataset" title="mxnet.gluon.data.Dataset"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Dataset</span></code></a></p></td> |
| <td><p>Abstract dataset class.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.gluon.data.ArrayDataset" title="mxnet.gluon.data.ArrayDataset"><code class="xref py py-obj docutils literal notranslate"><span class="pre">ArrayDataset</span></code></a>(*args)</p></td> |
| <td><p>A dataset that combines multiple dataset-like objects, e.g.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.gluon.data.RecordFileDataset" title="mxnet.gluon.data.RecordFileDataset"><code class="xref py py-obj docutils literal notranslate"><span class="pre">RecordFileDataset</span></code></a>(filename)</p></td> |
| <td><p>A dataset wrapping over a RecordIO (.rec) file.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.gluon.data.SimpleDataset" title="mxnet.gluon.data.SimpleDataset"><code class="xref py py-obj docutils literal notranslate"><span class="pre">SimpleDataset</span></code></a>(data)</p></td> |
| <td><p>Simple Dataset wrapper for lists and arrays.</p></td> |
| </tr> |
| </tbody> |
| </table> |
| </div> |
| <div class="section" id="sampling"> |
| <h2>Sampling<a class="headerlink" href="#sampling" 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.data.Sampler" title="mxnet.gluon.data.Sampler"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Sampler</span></code></a></p></td> |
| <td><p>Base class for samplers.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.gluon.data.SequentialSampler" title="mxnet.gluon.data.SequentialSampler"><code class="xref py py-obj docutils literal notranslate"><span class="pre">SequentialSampler</span></code></a>(length[, start])</p></td> |
| <td><p>Samples elements from [start, start+length) sequentially.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.gluon.data.RandomSampler" title="mxnet.gluon.data.RandomSampler"><code class="xref py py-obj docutils literal notranslate"><span class="pre">RandomSampler</span></code></a>(length)</p></td> |
| <td><p>Samples elements from [0, length) randomly without replacement.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.gluon.data.BatchSampler" title="mxnet.gluon.data.BatchSampler"><code class="xref py py-obj docutils literal notranslate"><span class="pre">BatchSampler</span></code></a>(sampler, batch_size[, last_batch])</p></td> |
| <td><p>Wraps over another <cite>Sampler</cite> and return mini-batches of samples.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.gluon.data.IntervalSampler" title="mxnet.gluon.data.IntervalSampler"><code class="xref py py-obj docutils literal notranslate"><span class="pre">IntervalSampler</span></code></a>(length, interval[, rollover])</p></td> |
| <td><p>Samples elements from [0, length) at fixed intervals.</p></td> |
| </tr> |
| </tbody> |
| </table> |
| </div> |
| <div class="section" id="dataloader"> |
| <h2>DataLoader<a class="headerlink" href="#dataloader" 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.data.DataLoader" title="mxnet.gluon.data.DataLoader"><code class="xref py py-obj docutils literal notranslate"><span class="pre">DataLoader</span></code></a>(dataset[, batch_size, shuffle, …])</p></td> |
| <td><p>Loads data from a dataset and returns mini-batches of data.</p></td> |
| </tr> |
| </tbody> |
| </table> |
| </div> |
| <div class="section" id="module-mxnet.gluon.data"> |
| <span id="api-reference"></span><h2>API Reference<a class="headerlink" href="#module-mxnet.gluon.data" title="Permalink to this headline">¶</a></h2> |
| <p>Dataset utilities.</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.data.ArrayDataset" title="mxnet.gluon.data.ArrayDataset"><code class="xref py py-obj docutils literal notranslate"><span class="pre">ArrayDataset</span></code></a>(*args)</p></td> |
| <td><p>A dataset that combines multiple dataset-like objects, e.g.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.gluon.data.BatchSampler" title="mxnet.gluon.data.BatchSampler"><code class="xref py py-obj docutils literal notranslate"><span class="pre">BatchSampler</span></code></a>(sampler, batch_size[, last_batch])</p></td> |
| <td><p>Wraps over another <cite>Sampler</cite> and return mini-batches of samples.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.gluon.data.DataLoader" title="mxnet.gluon.data.DataLoader"><code class="xref py py-obj docutils literal notranslate"><span class="pre">DataLoader</span></code></a>(dataset[, batch_size, shuffle, …])</p></td> |
| <td><p>Loads data from a dataset and returns mini-batches of data.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.gluon.data.Dataset" title="mxnet.gluon.data.Dataset"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Dataset</span></code></a></p></td> |
| <td><p>Abstract dataset class.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.gluon.data.FilterSampler" title="mxnet.gluon.data.FilterSampler"><code class="xref py py-obj docutils literal notranslate"><span class="pre">FilterSampler</span></code></a>(fn, dataset)</p></td> |
| <td><p>Samples elements from a Dataset for which <cite>fn</cite> returns True.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.gluon.data.IntervalSampler" title="mxnet.gluon.data.IntervalSampler"><code class="xref py py-obj docutils literal notranslate"><span class="pre">IntervalSampler</span></code></a>(length, interval[, rollover])</p></td> |
| <td><p>Samples elements from [0, length) at fixed intervals.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.gluon.data.RandomSampler" title="mxnet.gluon.data.RandomSampler"><code class="xref py py-obj docutils literal notranslate"><span class="pre">RandomSampler</span></code></a>(length)</p></td> |
| <td><p>Samples elements from [0, length) randomly without replacement.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.gluon.data.RecordFileDataset" title="mxnet.gluon.data.RecordFileDataset"><code class="xref py py-obj docutils literal notranslate"><span class="pre">RecordFileDataset</span></code></a>(filename)</p></td> |
| <td><p>A dataset wrapping over a RecordIO (.rec) file.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.gluon.data.Sampler" title="mxnet.gluon.data.Sampler"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Sampler</span></code></a></p></td> |
| <td><p>Base class for samplers.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.gluon.data.SequentialSampler" title="mxnet.gluon.data.SequentialSampler"><code class="xref py py-obj docutils literal notranslate"><span class="pre">SequentialSampler</span></code></a>(length[, start])</p></td> |
| <td><p>Samples elements from [start, start+length) sequentially.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.gluon.data.SimpleDataset" title="mxnet.gluon.data.SimpleDataset"><code class="xref py py-obj docutils literal notranslate"><span class="pre">SimpleDataset</span></code></a>(data)</p></td> |
| <td><p>Simple Dataset wrapper for lists and arrays.</p></td> |
| </tr> |
| </tbody> |
| </table> |
| <dl class="class"> |
| <dt id="mxnet.gluon.data.ArrayDataset"> |
| <em class="property">class </em><code class="sig-name descname">ArrayDataset</code><span class="sig-paren">(</span><em class="sig-param">*args</em><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/mxnet/gluon/data/dataset.html#ArrayDataset"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.gluon.data.ArrayDataset" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">mxnet.gluon.data.dataset.Dataset</span></code></p> |
| <p>A dataset that combines multiple dataset-like objects, e.g. |
| Datasets, lists, arrays, etc.</p> |
| <p>The i-th sample is defined as <cite>(x1[i], x2[i], …)</cite>.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><p><strong>*args</strong> (<em>one</em><em> or </em><em>more dataset-like objects</em>) – The data arrays.</p> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="class"> |
| <dt id="mxnet.gluon.data.BatchSampler"> |
| <em class="property">class </em><code class="sig-name descname">BatchSampler</code><span class="sig-paren">(</span><em class="sig-param">sampler</em>, <em class="sig-param">batch_size</em>, <em class="sig-param">last_batch='keep'</em><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/mxnet/gluon/data/sampler.html#BatchSampler"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.gluon.data.BatchSampler" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">mxnet.gluon.data.sampler.Sampler</span></code></p> |
| <p>Wraps over another <cite>Sampler</cite> and return mini-batches of samples.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>sampler</strong> (<a class="reference internal" href="#mxnet.gluon.data.Sampler" title="mxnet.gluon.data.Sampler"><em>Sampler</em></a>) – The source Sampler.</p></li> |
| <li><p><strong>batch_size</strong> (<em>int</em>) – Size of mini-batch.</p></li> |
| <li><p><strong>last_batch</strong> (<em>{'keep'</em><em>, </em><em>'discard'</em><em>, </em><em>'rollover'}</em>) – <p>Specifies how the last batch is handled if batch_size does not evenly |
| divide sequence length.</p> |
| <p>If ‘keep’, the last batch will be returned directly, but will contain |
| less element than <cite>batch_size</cite> requires.</p> |
| <p>If ‘discard’, the last batch will be discarded.</p> |
| <p>If ‘rollover’, the remaining elements will be rolled over to the next |
| iteration.</p> |
| </p></li> |
| </ul> |
| </dd> |
| </dl> |
| <p class="rubric">Examples</p> |
| <div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">sampler</span> <span class="o">=</span> <span class="n">gluon</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">SequentialSampler</span><span class="p">(</span><span class="mi">10</span><span class="p">)</span> |
| <span class="gp">>>> </span><span class="n">batch_sampler</span> <span class="o">=</span> <span class="n">gluon</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">BatchSampler</span><span class="p">(</span><span class="n">sampler</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="s1">'keep'</span><span class="p">)</span> |
| <span class="gp">>>> </span><span class="nb">list</span><span class="p">(</span><span class="n">batch_sampler</span><span class="p">)</span> |
| <span class="go">[[0, 1, 2], [3, 4, 5], [6, 7, 8], [9]]</span> |
| </pre></div> |
| </div> |
| </dd></dl> |
| |
| <dl class="class"> |
| <dt id="mxnet.gluon.data.DataLoader"> |
| <em class="property">class </em><code class="sig-name descname">DataLoader</code><span class="sig-paren">(</span><em class="sig-param">dataset</em>, <em class="sig-param">batch_size=None</em>, <em class="sig-param">shuffle=False</em>, <em class="sig-param">sampler=None</em>, <em class="sig-param">last_batch=None</em>, <em class="sig-param">batch_sampler=None</em>, <em class="sig-param">batchify_fn=None</em>, <em class="sig-param">num_workers=0</em>, <em class="sig-param">pin_memory=False</em>, <em class="sig-param">pin_device_id=0</em>, <em class="sig-param">prefetch=None</em>, <em class="sig-param">thread_pool=False</em>, <em class="sig-param">timeout=120</em>, <em class="sig-param">try_nopython=None</em><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/mxnet/gluon/data/dataloader.html#DataLoader"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.gluon.data.DataLoader" 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>Loads data from a dataset and returns mini-batches of data.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>dataset</strong> (<a class="reference internal" href="#mxnet.gluon.data.Dataset" title="mxnet.gluon.data.Dataset"><em>Dataset</em></a>) – Source dataset. Note that numpy and mxnet arrays can be directly used |
| as a Dataset.</p></li> |
| <li><p><strong>batch_size</strong> (<em>int</em>) – Size of mini-batch.</p></li> |
| <li><p><strong>shuffle</strong> (<em>bool</em>) – Whether to shuffle the samples.</p></li> |
| <li><p><strong>sampler</strong> (<a class="reference internal" href="#mxnet.gluon.data.Sampler" title="mxnet.gluon.data.Sampler"><em>Sampler</em></a>) – The sampler to use. Either specify sampler or shuffle, not both.</p></li> |
| <li><p><strong>last_batch</strong> (<em>{'keep'</em><em>, </em><em>'discard'</em><em>, </em><em>'rollover'}</em>) – <p>How to handle the last batch if batch_size does not evenly divide |
| <code class="docutils literal notranslate"><span class="pre">len(dataset)</span></code>.</p> |
| <p>keep - A batch with less samples than previous batches is returned. |
| discard - The last batch is discarded if its incomplete. |
| rollover - The remaining samples are rolled over to the next epoch.</p> |
| </p></li> |
| <li><p><strong>batch_sampler</strong> (<a class="reference internal" href="#mxnet.gluon.data.Sampler" title="mxnet.gluon.data.Sampler"><em>Sampler</em></a>) – A sampler that returns mini-batches. Do not specify batch_size, |
| shuffle, sampler, and last_batch if batch_sampler is specified.</p></li> |
| <li><p><strong>batchify_fn</strong> (<em>callable</em>) – <p>Callback function to allow users to specify how to merge samples |
| into a batch. Defaults to <cite>gluon.data.batchify.Stack()</cite>.</p> |
| <div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">default_batchify_fn</span><span class="p">(</span><span class="n">data</span><span class="p">):</span> |
| <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">data</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">nd</span><span class="o">.</span><span class="n">NDArray</span><span class="p">):</span> |
| <span class="k">return</span> <span class="n">nd</span><span class="o">.</span><span class="n">stack</span><span class="p">(</span><span class="o">*</span><span class="n">data</span><span class="p">)</span> |
| <span class="k">elif</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">data</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">):</span> |
| <span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">stack</span><span class="p">(</span><span class="o">*</span><span class="n">data</span><span class="p">)</span> |
| <span class="k">elif</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">data</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="nb">tuple</span><span class="p">):</span> |
| <span class="n">data</span> <span class="o">=</span> <span class="nb">zip</span><span class="p">(</span><span class="o">*</span><span class="n">data</span><span class="p">)</span> |
| <span class="k">return</span> <span class="p">[</span><span class="n">default_batchify_fn</span><span class="p">(</span><span class="n">i</span><span class="p">)</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">data</span><span class="p">]</span> |
| <span class="k">else</span><span class="p">:</span> |
| <span class="n">data</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="n">data</span><span class="p">)</span> |
| <span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">data</span><span class="o">.</span><span class="n">dtype</span><span class="p">)</span> |
| </pre></div> |
| </div> |
| </p></li> |
| <li><p><strong>num_workers</strong> (<em>int</em><em>, </em><em>default 0</em>) – The number of multiprocessing workers to use for data preprocessing.</p></li> |
| <li><p><strong>pin_memory</strong> (<em>boolean</em><em>, </em><em>default False</em>) – If <code class="docutils literal notranslate"><span class="pre">True</span></code>, the dataloader will copy NDArrays into pinned memory |
| before returning them. Copying from CPU pinned memory to GPU is faster |
| than from normal CPU memory.</p></li> |
| <li><p><strong>pin_device_id</strong> (<em>int</em><em>, </em><em>default 0</em>) – The device id to use for allocating pinned memory if pin_memory is <code class="docutils literal notranslate"><span class="pre">True</span></code></p></li> |
| <li><p><strong>prefetch</strong> (int, default is <cite>num_workers * 2</cite>) – The number of prefetching batches only works if <cite>num_workers</cite> > 0. |
| If <cite>prefetch</cite> > 0, it allow worker process to prefetch certain batches before |
| acquiring data from iterators. |
| Note that using large prefetching batch will provide smoother bootstrapping performance, |
| but will consume more shared_memory. Using smaller number may forfeit the purpose of using |
| multiple worker processes, try reduce <cite>num_workers</cite> in this case. |
| By default it defaults to <cite>num_workers * 2</cite>.</p></li> |
| <li><p><strong>thread_pool</strong> (<em>bool</em><em>, </em><em>default False</em>) – If <code class="docutils literal notranslate"><span class="pre">True</span></code>, use threading pool instead of multiprocessing pool. Using threadpool |
| can avoid shared memory usage. If <cite>DataLoader</cite> is more IO bounded or GIL is not a killing |
| problem, threadpool version may achieve better performance than multiprocessing.</p></li> |
| <li><p><strong>timeout</strong> (<em>int</em><em>, </em><em>default is 120</em>) – The timeout in seconds for each worker to fetch a batch data. Only modify this number |
| unless you are experiencing timeout and you know it’s due to slow data loading. |
| Sometimes full <cite>shared_memory</cite> will cause all workers to hang and causes timeout. In these |
| cases please reduce <cite>num_workers</cite> or increase system <cite>shared_memory</cite> size instead.</p></li> |
| <li><p><strong>try_nopython</strong> (<em>bool</em><em> or </em><em>None</em><em>, </em><em>default is None</em>) – Try compile python dataloading pipeline into pure MXNet c++ implementation. The benefit is |
| potentially faster iteration, no <cite>shared_memory</cite> usage, and less processes managed by python. |
| The compilation is not gauranteed to support all use cases, but it will fallback to python in |
| case of failure. You can set <cite>try_nopython</cite> to <cite>False</cite> to disable auto-detection of the |
| compilation feature or leave it to <cite>None</cite> to allow MXNet to determine it automatically. |
| If you request <cite>try_nopython</cite> to <cite>True</cite> and the compilation fails, it will raise a |
| RuntimeError with the failure reason.</p></li> |
| </ul> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="class"> |
| <dt id="mxnet.gluon.data.Dataset"> |
| <em class="property">class </em><code class="sig-name descname">Dataset</code><a class="reference internal" href="../../../_modules/mxnet/gluon/data/dataset.html#Dataset"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.gluon.data.Dataset" 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>Abstract dataset class. All datasets should have this interface.</p> |
| <p>Subclasses need to override <cite>__getitem__</cite>, which returns the i-th |
| element, and <cite>__len__</cite>, which returns the total number elements.</p> |
| <div class="admonition note"> |
| <p class="admonition-title">Note</p> |
| <p>An mxnet or numpy array can be directly used as a dataset.</p> |
| </div> |
| <p><strong>Methods</strong></p> |
| <table class="longtable docutils align-default"> |
| <colgroup> |
| <col style="width: 10%" /> |
| <col style="width: 90%" /> |
| </colgroup> |
| <tbody> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.gluon.data.Dataset.filter" title="mxnet.gluon.data.Dataset.filter"><code class="xref py py-obj docutils literal notranslate"><span class="pre">filter</span></code></a>(fn)</p></td> |
| <td><p>Returns a new dataset with samples filtered by the filter function <cite>fn</cite>.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.gluon.data.Dataset.sample" title="mxnet.gluon.data.Dataset.sample"><code class="xref py py-obj docutils literal notranslate"><span class="pre">sample</span></code></a>(sampler)</p></td> |
| <td><p>Returns a new dataset with elements sampled by the sampler.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.gluon.data.Dataset.shard" title="mxnet.gluon.data.Dataset.shard"><code class="xref py py-obj docutils literal notranslate"><span class="pre">shard</span></code></a>(num_shards, index)</p></td> |
| <td><p>Returns a new dataset includes only 1/num_shards of this dataset.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.gluon.data.Dataset.take" title="mxnet.gluon.data.Dataset.take"><code class="xref py py-obj docutils literal notranslate"><span class="pre">take</span></code></a>(count)</p></td> |
| <td><p>Returns a new dataset with at most <cite>count</cite> number of samples in it.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.gluon.data.Dataset.transform" title="mxnet.gluon.data.Dataset.transform"><code class="xref py py-obj docutils literal notranslate"><span class="pre">transform</span></code></a>(fn[, lazy])</p></td> |
| <td><p>Returns a new dataset with each sample transformed by the transformer function <cite>fn</cite>.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.gluon.data.Dataset.transform_first" title="mxnet.gluon.data.Dataset.transform_first"><code class="xref py py-obj docutils literal notranslate"><span class="pre">transform_first</span></code></a>(fn[, lazy])</p></td> |
| <td><p>Returns a new dataset with the first element of each sample transformed by the transformer function <cite>fn</cite>.</p></td> |
| </tr> |
| </tbody> |
| </table> |
| <dl class="method"> |
| <dt id="mxnet.gluon.data.Dataset.filter"> |
| <code class="sig-name descname">filter</code><span class="sig-paren">(</span><em class="sig-param">fn</em><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/mxnet/gluon/data/dataset.html#Dataset.filter"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.gluon.data.Dataset.filter" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Returns a new dataset with samples filtered by the |
| filter function <cite>fn</cite>.</p> |
| <p>Note that if the Dataset is the result of a lazily transformed one with |
| transform(lazy=False), the filter is eagerly applied to the transformed |
| samples without materializing the transformed result. That is, the |
| transformation will be applied again whenever a sample is retrieved after |
| filter().</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><p><strong>fn</strong> (<em>callable</em>) – A filter function that takes a sample as input and |
| returns a boolean. Samples that return False are discarded.</p> |
| </dd> |
| <dt class="field-even">Returns</dt> |
| <dd class="field-even"><p>The filtered dataset.</p> |
| </dd> |
| <dt class="field-odd">Return type</dt> |
| <dd class="field-odd"><p><a class="reference internal" href="#mxnet.gluon.data.Dataset" title="mxnet.gluon.data.Dataset">Dataset</a></p> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.data.Dataset.sample"> |
| <code class="sig-name descname">sample</code><span class="sig-paren">(</span><em class="sig-param">sampler</em><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/mxnet/gluon/data/dataset.html#Dataset.sample"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.gluon.data.Dataset.sample" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Returns a new dataset with elements sampled by the sampler.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><p><strong>sampler</strong> (<a class="reference internal" href="#mxnet.gluon.data.Sampler" title="mxnet.gluon.data.Sampler"><em>Sampler</em></a>) – A Sampler that returns the indices of sampled elements.</p> |
| </dd> |
| <dt class="field-even">Returns</dt> |
| <dd class="field-even"><p>The result dataset.</p> |
| </dd> |
| <dt class="field-odd">Return type</dt> |
| <dd class="field-odd"><p><a class="reference internal" href="#mxnet.gluon.data.Dataset" title="mxnet.gluon.data.Dataset">Dataset</a></p> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.data.Dataset.shard"> |
| <code class="sig-name descname">shard</code><span class="sig-paren">(</span><em class="sig-param">num_shards</em>, <em class="sig-param">index</em><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/mxnet/gluon/data/dataset.html#Dataset.shard"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.gluon.data.Dataset.shard" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Returns a new dataset includes only 1/num_shards of this dataset.</p> |
| <p>For distributed training, be sure to shard before you randomize the dataset |
| (such as shuffle), if you want each worker to reach a unique subset.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>num_shards</strong> (<em>int</em>) – A integer representing the number of data shards.</p></li> |
| <li><p><strong>index</strong> (<em>int</em>) – A integer representing the index of the current shard.</p></li> |
| </ul> |
| </dd> |
| <dt class="field-even">Returns</dt> |
| <dd class="field-even"><p>The result dataset.</p> |
| </dd> |
| <dt class="field-odd">Return type</dt> |
| <dd class="field-odd"><p><a class="reference internal" href="#mxnet.gluon.data.Dataset" title="mxnet.gluon.data.Dataset">Dataset</a></p> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.data.Dataset.take"> |
| <code class="sig-name descname">take</code><span class="sig-paren">(</span><em class="sig-param">count</em><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/mxnet/gluon/data/dataset.html#Dataset.take"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.gluon.data.Dataset.take" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Returns a new dataset with at most <cite>count</cite> number of samples in it.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><p><strong>count</strong> (<em>int</em><em> or </em><em>None</em>) – A integer representing the number of elements of this dataset that |
| should be taken to form the new dataset. If count is None, or if count |
| is greater than the size of this dataset, the new dataset will contain |
| all elements of this dataset.</p> |
| </dd> |
| <dt class="field-even">Returns</dt> |
| <dd class="field-even"><p>The result dataset.</p> |
| </dd> |
| <dt class="field-odd">Return type</dt> |
| <dd class="field-odd"><p><a class="reference internal" href="#mxnet.gluon.data.Dataset" title="mxnet.gluon.data.Dataset">Dataset</a></p> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.data.Dataset.transform"> |
| <code class="sig-name descname">transform</code><span class="sig-paren">(</span><em class="sig-param">fn</em>, <em class="sig-param">lazy=True</em><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/mxnet/gluon/data/dataset.html#Dataset.transform"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.gluon.data.Dataset.transform" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Returns a new dataset with each sample transformed by the |
| transformer function <cite>fn</cite>.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>fn</strong> (<em>callable</em>) – A transformer function that takes a sample as input and |
| returns the transformed sample.</p></li> |
| <li><p><strong>lazy</strong> (<em>bool</em><em>, </em><em>default True</em>) – If False, transforms all samples at once. Otherwise, |
| transforms each sample on demand. Note that if <cite>fn</cite> |
| is stochastic, you must set lazy to True or you will |
| get the same result on all epochs.</p></li> |
| </ul> |
| </dd> |
| <dt class="field-even">Returns</dt> |
| <dd class="field-even"><p>The transformed dataset.</p> |
| </dd> |
| <dt class="field-odd">Return type</dt> |
| <dd class="field-odd"><p><a class="reference internal" href="#mxnet.gluon.data.Dataset" title="mxnet.gluon.data.Dataset">Dataset</a></p> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.data.Dataset.transform_first"> |
| <code class="sig-name descname">transform_first</code><span class="sig-paren">(</span><em class="sig-param">fn</em>, <em class="sig-param">lazy=True</em><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/mxnet/gluon/data/dataset.html#Dataset.transform_first"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.gluon.data.Dataset.transform_first" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Returns a new dataset with the first element of each sample |
| transformed by the transformer function <cite>fn</cite>.</p> |
| <p>This is mostly applicable when each sample contains two components |
| - features and label, i.e., (X, y), and you only want to transform |
| the first element X (i.e., the features) while keeping the label y |
| unchanged.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>fn</strong> (<em>callable</em>) – A transformer function that takes the first element of a sample |
| as input and returns the transformed element.</p></li> |
| <li><p><strong>lazy</strong> (<em>bool</em><em>, </em><em>default True</em>) – If False, transforms all samples at once. Otherwise, |
| transforms each sample on demand. Note that if <cite>fn</cite> |
| is stochastic, you must set lazy to True or you will |
| get the same result on all epochs.</p></li> |
| </ul> |
| </dd> |
| <dt class="field-even">Returns</dt> |
| <dd class="field-even"><p>The transformed dataset.</p> |
| </dd> |
| <dt class="field-odd">Return type</dt> |
| <dd class="field-odd"><p><a class="reference internal" href="#mxnet.gluon.data.Dataset" title="mxnet.gluon.data.Dataset">Dataset</a></p> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| </dd></dl> |
| |
| <dl class="class"> |
| <dt id="mxnet.gluon.data.FilterSampler"> |
| <em class="property">class </em><code class="sig-name descname">FilterSampler</code><span class="sig-paren">(</span><em class="sig-param">fn</em>, <em class="sig-param">dataset</em><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/mxnet/gluon/data/sampler.html#FilterSampler"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.gluon.data.FilterSampler" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">mxnet.gluon.data.sampler.Sampler</span></code></p> |
| <p>Samples elements from a Dataset for which <cite>fn</cite> returns True.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>fn</strong> (<em>callable</em>) – A callable function that takes a sample and returns a boolean</p></li> |
| <li><p><strong>dataset</strong> (<a class="reference internal" href="#mxnet.gluon.data.Dataset" title="mxnet.gluon.data.Dataset"><em>Dataset</em></a>) – The dataset to filter.</p></li> |
| </ul> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="class"> |
| <dt id="mxnet.gluon.data.IntervalSampler"> |
| <em class="property">class </em><code class="sig-name descname">IntervalSampler</code><span class="sig-paren">(</span><em class="sig-param">length</em>, <em class="sig-param">interval</em>, <em class="sig-param">rollover=True</em><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/mxnet/gluon/data/sampler.html#IntervalSampler"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.gluon.data.IntervalSampler" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">mxnet.gluon.data.sampler.Sampler</span></code></p> |
| <p>Samples elements from [0, length) at fixed intervals.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>length</strong> (<em>int</em>) – Length of the sequence.</p></li> |
| <li><p><strong>interval</strong> (<em>int</em>) – The number of items to skip between two samples.</p></li> |
| <li><p><strong>rollover</strong> (<em>bool</em><em>, </em><em>default True</em>) – Whether to start again from the first skipped item after reaching the end. |
| If true, this sampler would start again from the first skipped item until all items |
| are visited. |
| Otherwise, iteration stops when end is reached and skipped items are ignored.</p></li> |
| </ul> |
| </dd> |
| </dl> |
| <p class="rubric">Examples</p> |
| <div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">sampler</span> <span class="o">=</span> <span class="n">contrib</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">IntervalSampler</span><span class="p">(</span><span class="mi">13</span><span class="p">,</span> <span class="n">interval</span><span class="o">=</span><span class="mi">3</span><span class="p">)</span> |
| <span class="gp">>>> </span><span class="nb">list</span><span class="p">(</span><span class="n">sampler</span><span class="p">)</span> |
| <span class="go">[0, 3, 6, 9, 12, 1, 4, 7, 10, 2, 5, 8, 11]</span> |
| <span class="gp">>>> </span><span class="n">sampler</span> <span class="o">=</span> <span class="n">contrib</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">IntervalSampler</span><span class="p">(</span><span class="mi">13</span><span class="p">,</span> <span class="n">interval</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span> <span class="n">rollover</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span> |
| <span class="gp">>>> </span><span class="nb">list</span><span class="p">(</span><span class="n">sampler</span><span class="p">)</span> |
| <span class="go">[0, 3, 6, 9, 12]</span> |
| </pre></div> |
| </div> |
| </dd></dl> |
| |
| <dl class="class"> |
| <dt id="mxnet.gluon.data.RandomSampler"> |
| <em class="property">class </em><code class="sig-name descname">RandomSampler</code><span class="sig-paren">(</span><em class="sig-param">length</em><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/mxnet/gluon/data/sampler.html#RandomSampler"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.gluon.data.RandomSampler" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">mxnet.gluon.data.sampler.Sampler</span></code></p> |
| <p>Samples elements from [0, length) randomly without replacement.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><p><strong>length</strong> (<em>int</em>) – Length of the sequence.</p> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="class"> |
| <dt id="mxnet.gluon.data.RecordFileDataset"> |
| <em class="property">class </em><code class="sig-name descname">RecordFileDataset</code><span class="sig-paren">(</span><em class="sig-param">filename</em><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/mxnet/gluon/data/dataset.html#RecordFileDataset"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.gluon.data.RecordFileDataset" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">mxnet.gluon.data.dataset.Dataset</span></code></p> |
| <p>A dataset wrapping over a RecordIO (.rec) file.</p> |
| <p>Each sample is a string representing the raw content of an record.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><p><strong>filename</strong> (<em>str</em>) – Path to rec file.</p> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="class"> |
| <dt id="mxnet.gluon.data.Sampler"> |
| <em class="property">class </em><code class="sig-name descname">Sampler</code><a class="reference internal" href="../../../_modules/mxnet/gluon/data/sampler.html#Sampler"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.gluon.data.Sampler" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">object</span></code></p> |
| <p>Base class for samplers.</p> |
| <p>All samplers should subclass <cite>Sampler</cite> and define <cite>__iter__</cite> and <cite>__len__</cite> |
| methods.</p> |
| </dd></dl> |
| |
| <dl class="class"> |
| <dt id="mxnet.gluon.data.SequentialSampler"> |
| <em class="property">class </em><code class="sig-name descname">SequentialSampler</code><span class="sig-paren">(</span><em class="sig-param">length</em>, <em class="sig-param">start=0</em><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/mxnet/gluon/data/sampler.html#SequentialSampler"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.gluon.data.SequentialSampler" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">mxnet.gluon.data.sampler.Sampler</span></code></p> |
| <p>Samples elements from [start, start+length) sequentially.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>length</strong> (<em>int</em>) – Length of the sequence.</p></li> |
| <li><p><strong>start</strong> (<em>int</em><em>, </em><em>default is 0</em>) – The start of the sequence index.</p></li> |
| </ul> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="class"> |
| <dt id="mxnet.gluon.data.SimpleDataset"> |
| <em class="property">class </em><code class="sig-name descname">SimpleDataset</code><span class="sig-paren">(</span><em class="sig-param">data</em><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/mxnet/gluon/data/dataset.html#SimpleDataset"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.gluon.data.SimpleDataset" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">mxnet.gluon.data.dataset.Dataset</span></code></p> |
| <p>Simple Dataset wrapper for lists and arrays.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><p><strong>data</strong> (<em>dataset-like object</em>) – Any object that implements <cite>len()</cite> and <cite>[]</cite>.</p> |
| </dd> |
| </dl> |
| </dd></dl> |
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| <li><a class="reference internal" href="#module-mxnet.gluon.data">API Reference</a></li> |
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