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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-rnn"> |
| <h1>gluon.rnn<a class="headerlink" href="#gluon-rnn" title="Permalink to this headline">¶</a></h1> |
| <p>Build-in recurrent neural network layers are provided in the following two modules:</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.rnn" title="mxnet.gluon.rnn"><code class="xref py py-obj docutils literal notranslate"><span class="pre">mxnet.gluon.rnn</span></code></a></p></td> |
| <td><p>Recurrent neural network module.</p></td> |
| </tr> |
| </tbody> |
| </table> |
| <div class="section" id="recurrent-cells"> |
| <h2>Recurrent Cells<a class="headerlink" href="#recurrent-cells" 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.rnn.LSTMCell" title="mxnet.gluon.rnn.LSTMCell"><code class="xref py py-obj docutils literal notranslate"><span class="pre">rnn.LSTMCell</span></code></a></p></td> |
| <td><p>Long-Short Term Memory (LSTM) network cell.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.GRUCell" title="mxnet.gluon.rnn.GRUCell"><code class="xref py py-obj docutils literal notranslate"><span class="pre">rnn.GRUCell</span></code></a></p></td> |
| <td><p>Gated Rectified Unit (GRU) network cell.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.RecurrentCell" title="mxnet.gluon.rnn.RecurrentCell"><code class="xref py py-obj docutils literal notranslate"><span class="pre">rnn.RecurrentCell</span></code></a></p></td> |
| <td><p>Abstract base class for RNN cells</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.LSTMPCell" title="mxnet.gluon.rnn.LSTMPCell"><code class="xref py py-obj docutils literal notranslate"><span class="pre">rnn.LSTMPCell</span></code></a></p></td> |
| <td><p>Long-Short Term Memory Projected (LSTMP) network cell.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.SequentialRNNCell" title="mxnet.gluon.rnn.SequentialRNNCell"><code class="xref py py-obj docutils literal notranslate"><span class="pre">rnn.SequentialRNNCell</span></code></a></p></td> |
| <td><p>Sequentially stacking multiple RNN cells.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.BidirectionalCell" title="mxnet.gluon.rnn.BidirectionalCell"><code class="xref py py-obj docutils literal notranslate"><span class="pre">rnn.BidirectionalCell</span></code></a></p></td> |
| <td><p>Bidirectional RNN cell.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.DropoutCell" title="mxnet.gluon.rnn.DropoutCell"><code class="xref py py-obj docutils literal notranslate"><span class="pre">rnn.DropoutCell</span></code></a></p></td> |
| <td><p>Applies dropout on input.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.VariationalDropoutCell" title="mxnet.gluon.rnn.VariationalDropoutCell"><code class="xref py py-obj docutils literal notranslate"><span class="pre">rnn.VariationalDropoutCell</span></code></a></p></td> |
| <td><p>Applies Variational Dropout on base cell.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.ZoneoutCell" title="mxnet.gluon.rnn.ZoneoutCell"><code class="xref py py-obj docutils literal notranslate"><span class="pre">rnn.ZoneoutCell</span></code></a></p></td> |
| <td><p>Applies Zoneout on base cell.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.ResidualCell" title="mxnet.gluon.rnn.ResidualCell"><code class="xref py py-obj docutils literal notranslate"><span class="pre">rnn.ResidualCell</span></code></a></p></td> |
| <td><p>Adds residual connection as described in Wu et al, 2016 (<a class="reference external" href="https://arxiv.org/abs/1609.08144">https://arxiv.org/abs/1609.08144</a>).</p></td> |
| </tr> |
| </tbody> |
| </table> |
| </div> |
| <div class="section" id="convolutional-recurrent-cells"> |
| <h2>Convolutional Recurrent Cells<a class="headerlink" href="#convolutional-recurrent-cells" 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.rnn.Conv1DLSTMCell" title="mxnet.gluon.rnn.Conv1DLSTMCell"><code class="xref py py-obj docutils literal notranslate"><span class="pre">rnn.Conv1DLSTMCell</span></code></a></p></td> |
| <td><p>1D Convolutional LSTM network cell.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.Conv2DLSTMCell" title="mxnet.gluon.rnn.Conv2DLSTMCell"><code class="xref py py-obj docutils literal notranslate"><span class="pre">rnn.Conv2DLSTMCell</span></code></a></p></td> |
| <td><p>2D Convolutional LSTM network cell.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.Conv3DLSTMCell" title="mxnet.gluon.rnn.Conv3DLSTMCell"><code class="xref py py-obj docutils literal notranslate"><span class="pre">rnn.Conv3DLSTMCell</span></code></a></p></td> |
| <td><p>3D Convolutional LSTM network cell.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.Conv1DGRUCell" title="mxnet.gluon.rnn.Conv1DGRUCell"><code class="xref py py-obj docutils literal notranslate"><span class="pre">rnn.Conv1DGRUCell</span></code></a></p></td> |
| <td><p>1D Convolutional Gated Rectified Unit (GRU) network cell.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.Conv2DGRUCell" title="mxnet.gluon.rnn.Conv2DGRUCell"><code class="xref py py-obj docutils literal notranslate"><span class="pre">rnn.Conv2DGRUCell</span></code></a></p></td> |
| <td><p>2D Convolutional Gated Rectified Unit (GRU) network cell.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.Conv3DGRUCell" title="mxnet.gluon.rnn.Conv3DGRUCell"><code class="xref py py-obj docutils literal notranslate"><span class="pre">rnn.Conv3DGRUCell</span></code></a></p></td> |
| <td><p>3D Convolutional Gated Rectified Unit (GRU) network cell.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.Conv1DRNNCell" title="mxnet.gluon.rnn.Conv1DRNNCell"><code class="xref py py-obj docutils literal notranslate"><span class="pre">rnn.Conv1DRNNCell</span></code></a></p></td> |
| <td><p>1D Convolutional RNN cell.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.Conv2DRNNCell" title="mxnet.gluon.rnn.Conv2DRNNCell"><code class="xref py py-obj docutils literal notranslate"><span class="pre">rnn.Conv2DRNNCell</span></code></a></p></td> |
| <td><p>2D Convolutional RNN cell.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.Conv3DRNNCell" title="mxnet.gluon.rnn.Conv3DRNNCell"><code class="xref py py-obj docutils literal notranslate"><span class="pre">rnn.Conv3DRNNCell</span></code></a></p></td> |
| <td><p>3D Convolutional RNN cells</p></td> |
| </tr> |
| </tbody> |
| </table> |
| </div> |
| <div class="section" id="recurrent-layers"> |
| <h2>Recurrent Layers<a class="headerlink" href="#recurrent-layers" 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.rnn.RNN" title="mxnet.gluon.rnn.RNN"><code class="xref py py-obj docutils literal notranslate"><span class="pre">rnn.RNN</span></code></a></p></td> |
| <td><p>Applies a multi-layer Elman RNN with <cite>tanh</cite> or <cite>ReLU</cite> non-linearity to an input sequence.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.LSTM" title="mxnet.gluon.rnn.LSTM"><code class="xref py py-obj docutils literal notranslate"><span class="pre">rnn.LSTM</span></code></a></p></td> |
| <td><p>Applies a multi-layer long short-term memory (LSTM) RNN to an input sequence.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.GRU" title="mxnet.gluon.rnn.GRU"><code class="xref py py-obj docutils literal notranslate"><span class="pre">rnn.GRU</span></code></a></p></td> |
| <td><p>Applies a multi-layer gated recurrent unit (GRU) RNN to an input sequence.</p></td> |
| </tr> |
| </tbody> |
| </table> |
| </div> |
| <div class="section" id="module-mxnet.gluon.rnn"> |
| <span id="api-reference"></span><h2>API Reference<a class="headerlink" href="#module-mxnet.gluon.rnn" title="Permalink to this headline">¶</a></h2> |
| <p>Recurrent neural network module.</p> |
| <p><strong>Classes</strong></p> |
| <table class="longtable docutils align-default"> |
| <colgroup> |
| <col style="width: 10%" /> |
| <col style="width: 90%" /> |
| </colgroup> |
| <tbody> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.BidirectionalCell" title="mxnet.gluon.rnn.BidirectionalCell"><code class="xref py py-obj docutils literal notranslate"><span class="pre">BidirectionalCell</span></code></a>(l_cell, r_cell)</p></td> |
| <td><p>Bidirectional RNN cell.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.Conv1DGRUCell" title="mxnet.gluon.rnn.Conv1DGRUCell"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Conv1DGRUCell</span></code></a>(input_shape, hidden_channels, …)</p></td> |
| <td><p>1D Convolutional Gated Rectified Unit (GRU) network cell.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.Conv1DLSTMCell" title="mxnet.gluon.rnn.Conv1DLSTMCell"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Conv1DLSTMCell</span></code></a>(input_shape, hidden_channels, …)</p></td> |
| <td><p>1D Convolutional LSTM network cell.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.Conv1DRNNCell" title="mxnet.gluon.rnn.Conv1DRNNCell"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Conv1DRNNCell</span></code></a>(input_shape, hidden_channels, …)</p></td> |
| <td><p>1D Convolutional RNN cell.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.Conv2DGRUCell" title="mxnet.gluon.rnn.Conv2DGRUCell"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Conv2DGRUCell</span></code></a>(input_shape, hidden_channels, …)</p></td> |
| <td><p>2D Convolutional Gated Rectified Unit (GRU) network cell.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.Conv2DLSTMCell" title="mxnet.gluon.rnn.Conv2DLSTMCell"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Conv2DLSTMCell</span></code></a>(input_shape, hidden_channels, …)</p></td> |
| <td><p>2D Convolutional LSTM network cell.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.Conv2DRNNCell" title="mxnet.gluon.rnn.Conv2DRNNCell"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Conv2DRNNCell</span></code></a>(input_shape, hidden_channels, …)</p></td> |
| <td><p>2D Convolutional RNN cell.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.Conv3DGRUCell" title="mxnet.gluon.rnn.Conv3DGRUCell"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Conv3DGRUCell</span></code></a>(input_shape, hidden_channels, …)</p></td> |
| <td><p>3D Convolutional Gated Rectified Unit (GRU) network cell.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.Conv3DLSTMCell" title="mxnet.gluon.rnn.Conv3DLSTMCell"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Conv3DLSTMCell</span></code></a>(input_shape, hidden_channels, …)</p></td> |
| <td><p>3D Convolutional LSTM network cell.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.Conv3DRNNCell" title="mxnet.gluon.rnn.Conv3DRNNCell"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Conv3DRNNCell</span></code></a>(input_shape, hidden_channels, …)</p></td> |
| <td><p>3D Convolutional RNN cells</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.DropoutCell" title="mxnet.gluon.rnn.DropoutCell"><code class="xref py py-obj docutils literal notranslate"><span class="pre">DropoutCell</span></code></a>(rate[, axes])</p></td> |
| <td><p>Applies dropout on input.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.GRU" title="mxnet.gluon.rnn.GRU"><code class="xref py py-obj docutils literal notranslate"><span class="pre">GRU</span></code></a>(hidden_size[, num_layers, layout, …])</p></td> |
| <td><p>Applies a multi-layer gated recurrent unit (GRU) RNN to an input sequence.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.GRUCell" title="mxnet.gluon.rnn.GRUCell"><code class="xref py py-obj docutils literal notranslate"><span class="pre">GRUCell</span></code></a>(hidden_size[, …])</p></td> |
| <td><p>Gated Rectified Unit (GRU) network cell.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.HybridRecurrentCell" title="mxnet.gluon.rnn.HybridRecurrentCell"><code class="xref py py-obj docutils literal notranslate"><span class="pre">HybridRecurrentCell</span></code></a>()</p></td> |
| <td><p>HybridRecurrentCell supports hybridize.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.HybridSequentialRNNCell" title="mxnet.gluon.rnn.HybridSequentialRNNCell"><code class="xref py py-obj docutils literal notranslate"><span class="pre">HybridSequentialRNNCell</span></code></a>()</p></td> |
| <td><p>Sequentially stacking multiple HybridRNN cells.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.LSTM" title="mxnet.gluon.rnn.LSTM"><code class="xref py py-obj docutils literal notranslate"><span class="pre">LSTM</span></code></a>(hidden_size[, num_layers, layout, …])</p></td> |
| <td><p>Applies a multi-layer long short-term memory (LSTM) RNN to an input sequence.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.LSTMCell" title="mxnet.gluon.rnn.LSTMCell"><code class="xref py py-obj docutils literal notranslate"><span class="pre">LSTMCell</span></code></a>(hidden_size[, …])</p></td> |
| <td><p>Long-Short Term Memory (LSTM) network cell.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.LSTMPCell" title="mxnet.gluon.rnn.LSTMPCell"><code class="xref py py-obj docutils literal notranslate"><span class="pre">LSTMPCell</span></code></a>(hidden_size, projection_size[, …])</p></td> |
| <td><p>Long-Short Term Memory Projected (LSTMP) network cell.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.ModifierCell" title="mxnet.gluon.rnn.ModifierCell"><code class="xref py py-obj docutils literal notranslate"><span class="pre">ModifierCell</span></code></a>(base_cell)</p></td> |
| <td><p>Base class for modifier cells.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.RNN" title="mxnet.gluon.rnn.RNN"><code class="xref py py-obj docutils literal notranslate"><span class="pre">RNN</span></code></a>(hidden_size[, num_layers, activation, …])</p></td> |
| <td><p>Applies a multi-layer Elman RNN with <cite>tanh</cite> or <cite>ReLU</cite> non-linearity to an input sequence.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.RNNCell" title="mxnet.gluon.rnn.RNNCell"><code class="xref py py-obj docutils literal notranslate"><span class="pre">RNNCell</span></code></a>(hidden_size[, activation, …])</p></td> |
| <td><p>Elman RNN recurrent neural network cell.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.RecurrentCell" title="mxnet.gluon.rnn.RecurrentCell"><code class="xref py py-obj docutils literal notranslate"><span class="pre">RecurrentCell</span></code></a>()</p></td> |
| <td><p>Abstract base class for RNN cells</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.ResidualCell" title="mxnet.gluon.rnn.ResidualCell"><code class="xref py py-obj docutils literal notranslate"><span class="pre">ResidualCell</span></code></a>(base_cell)</p></td> |
| <td><p>Adds residual connection as described in Wu et al, 2016 (<a class="reference external" href="https://arxiv.org/abs/1609.08144">https://arxiv.org/abs/1609.08144</a>).</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.SequentialRNNCell" title="mxnet.gluon.rnn.SequentialRNNCell"><code class="xref py py-obj docutils literal notranslate"><span class="pre">SequentialRNNCell</span></code></a>()</p></td> |
| <td><p>Sequentially stacking multiple RNN cells.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.VariationalDropoutCell" title="mxnet.gluon.rnn.VariationalDropoutCell"><code class="xref py py-obj docutils literal notranslate"><span class="pre">VariationalDropoutCell</span></code></a>(base_cell[, …])</p></td> |
| <td><p>Applies Variational Dropout on base cell.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.ZoneoutCell" title="mxnet.gluon.rnn.ZoneoutCell"><code class="xref py py-obj docutils literal notranslate"><span class="pre">ZoneoutCell</span></code></a>(base_cell[, zoneout_outputs, …])</p></td> |
| <td><p>Applies Zoneout on base cell.</p></td> |
| </tr> |
| </tbody> |
| </table> |
| <dl class="class"> |
| <dt id="mxnet.gluon.rnn.BidirectionalCell"> |
| <em class="property">class </em><code class="sig-name descname">BidirectionalCell</code><span class="sig-paren">(</span><em class="sig-param">l_cell</em>, <em class="sig-param">r_cell</em><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/mxnet/gluon/rnn/rnn_cell.html#BidirectionalCell"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.gluon.rnn.BidirectionalCell" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">mxnet.gluon.rnn.rnn_cell.HybridRecurrentCell</span></code></p> |
| <p>Bidirectional RNN cell.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>l_cell</strong> (<a class="reference internal" href="#mxnet.gluon.rnn.RecurrentCell" title="mxnet.gluon.rnn.RecurrentCell"><em>RecurrentCell</em></a>) – Cell for forward unrolling</p></li> |
| <li><p><strong>r_cell</strong> (<a class="reference internal" href="#mxnet.gluon.rnn.RecurrentCell" title="mxnet.gluon.rnn.RecurrentCell"><em>RecurrentCell</em></a>) – Cell for backward unrolling</p></li> |
| </ul> |
| </dd> |
| </dl> |
| <p><strong>Methods</strong></p> |
| <table class="longtable docutils align-default"> |
| <colgroup> |
| <col style="width: 10%" /> |
| <col style="width: 90%" /> |
| </colgroup> |
| <tbody> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.BidirectionalCell.apply" title="mxnet.gluon.rnn.BidirectionalCell.apply"><code class="xref py py-obj docutils literal notranslate"><span class="pre">apply</span></code></a>(fn)</p></td> |
| <td><p>Applies <code class="docutils literal notranslate"><span class="pre">fn</span></code> recursively to every child block as well as self.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.BidirectionalCell.begin_state" title="mxnet.gluon.rnn.BidirectionalCell.begin_state"><code class="xref py py-obj docutils literal notranslate"><span class="pre">begin_state</span></code></a>(**kwargs)</p></td> |
| <td><p>Initial state for this cell.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.BidirectionalCell.cast" title="mxnet.gluon.rnn.BidirectionalCell.cast"><code class="xref py py-obj docutils literal notranslate"><span class="pre">cast</span></code></a>(dtype)</p></td> |
| <td><p>Cast this Block to use another data type.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.BidirectionalCell.collect_params" title="mxnet.gluon.rnn.BidirectionalCell.collect_params"><code class="xref py py-obj docutils literal notranslate"><span class="pre">collect_params</span></code></a>([select])</p></td> |
| <td><p>Returns a <code class="xref py py-class docutils literal notranslate"><span class="pre">Dict</span></code> containing this <code class="xref py py-class docutils literal notranslate"><span class="pre">Block</span></code> and all of its children’s Parameters(default), also can returns the select <code class="xref py py-class docutils literal notranslate"><span class="pre">Dict</span></code> which match some given regular expressions.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.BidirectionalCell.export" title="mxnet.gluon.rnn.BidirectionalCell.export"><code class="xref py py-obj docutils literal notranslate"><span class="pre">export</span></code></a>(path[, epoch, remove_amp_cast])</p></td> |
| <td><p>Export HybridBlock to json format that can be loaded by <cite>gluon.SymbolBlock.imports</cite> or the C++ interface.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.BidirectionalCell.hybridize" title="mxnet.gluon.rnn.BidirectionalCell.hybridize"><code class="xref py py-obj docutils literal notranslate"><span class="pre">hybridize</span></code></a>([active])</p></td> |
| <td><p>Please refer description of HybridBlock hybridize().</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.BidirectionalCell.infer_shape" title="mxnet.gluon.rnn.BidirectionalCell.infer_shape"><code class="xref py py-obj docutils literal notranslate"><span class="pre">infer_shape</span></code></a>(i, x, is_bidirect)</p></td> |
| <td><p>Infers shape of Parameters from inputs.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.BidirectionalCell.infer_type" title="mxnet.gluon.rnn.BidirectionalCell.infer_type"><code class="xref py py-obj docutils literal notranslate"><span class="pre">infer_type</span></code></a>(*args)</p></td> |
| <td><p>Infers data type of Parameters from inputs.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.BidirectionalCell.initialize" title="mxnet.gluon.rnn.BidirectionalCell.initialize"><code class="xref py py-obj docutils literal notranslate"><span class="pre">initialize</span></code></a>([init, device, verbose, force_reinit])</p></td> |
| <td><p>Initializes <code class="xref py py-class docutils literal notranslate"><span class="pre">Parameter</span></code> s of this <code class="xref py py-class docutils literal notranslate"><span class="pre">Block</span></code> and its children.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.BidirectionalCell.load" title="mxnet.gluon.rnn.BidirectionalCell.load"><code class="xref py py-obj docutils literal notranslate"><span class="pre">load</span></code></a>(prefix)</p></td> |
| <td><p>Load a model saved using the <cite>save</cite> API</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.BidirectionalCell.load_dict" title="mxnet.gluon.rnn.BidirectionalCell.load_dict"><code class="xref py py-obj docutils literal notranslate"><span class="pre">load_dict</span></code></a>(param_dict[, device, …])</p></td> |
| <td><p>Load parameters from dict</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.BidirectionalCell.load_parameters" title="mxnet.gluon.rnn.BidirectionalCell.load_parameters"><code class="xref py py-obj docutils literal notranslate"><span class="pre">load_parameters</span></code></a>(filename[, device, …])</p></td> |
| <td><p>Load parameters from file previously saved by <cite>save_parameters</cite>.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.BidirectionalCell.optimize_for" title="mxnet.gluon.rnn.BidirectionalCell.optimize_for"><code class="xref py py-obj docutils literal notranslate"><span class="pre">optimize_for</span></code></a>(x, *args[, backend, clear, …])</p></td> |
| <td><p>Partitions the current HybridBlock and optimizes it for a given backend without executing a forward pass.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.BidirectionalCell.register_child" title="mxnet.gluon.rnn.BidirectionalCell.register_child"><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_child</span></code></a>(block[, name])</p></td> |
| <td><p>Registers block as a child of self.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.BidirectionalCell.register_forward_hook" title="mxnet.gluon.rnn.BidirectionalCell.register_forward_hook"><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_forward_hook</span></code></a>(hook)</p></td> |
| <td><p>Registers a forward hook on the block.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.BidirectionalCell.register_forward_pre_hook" title="mxnet.gluon.rnn.BidirectionalCell.register_forward_pre_hook"><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_forward_pre_hook</span></code></a>(hook)</p></td> |
| <td><p>Registers a forward pre-hook on the block.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.BidirectionalCell.register_op_hook" title="mxnet.gluon.rnn.BidirectionalCell.register_op_hook"><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_op_hook</span></code></a>(callback[, monitor_all])</p></td> |
| <td><p>Install callback monitor.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.BidirectionalCell.reset" title="mxnet.gluon.rnn.BidirectionalCell.reset"><code class="xref py py-obj docutils literal notranslate"><span class="pre">reset</span></code></a>()</p></td> |
| <td><p>Reset before re-using the cell for another graph.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.BidirectionalCell.reset_ctx" title="mxnet.gluon.rnn.BidirectionalCell.reset_ctx"><code class="xref py py-obj docutils literal notranslate"><span class="pre">reset_ctx</span></code></a>(ctx)</p></td> |
| <td><p>This function has been deprecated.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.BidirectionalCell.reset_device" title="mxnet.gluon.rnn.BidirectionalCell.reset_device"><code class="xref py py-obj docutils literal notranslate"><span class="pre">reset_device</span></code></a>(device)</p></td> |
| <td><p>Re-assign all Parameters to other devices.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.BidirectionalCell.save" title="mxnet.gluon.rnn.BidirectionalCell.save"><code class="xref py py-obj docutils literal notranslate"><span class="pre">save</span></code></a>(prefix)</p></td> |
| <td><p>Save the model architecture and parameters to load again later</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.BidirectionalCell.save_parameters" title="mxnet.gluon.rnn.BidirectionalCell.save_parameters"><code class="xref py py-obj docutils literal notranslate"><span class="pre">save_parameters</span></code></a>(filename[, deduplicate])</p></td> |
| <td><p>Save parameters to file.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.BidirectionalCell.setattr" title="mxnet.gluon.rnn.BidirectionalCell.setattr"><code class="xref py py-obj docutils literal notranslate"><span class="pre">setattr</span></code></a>(name, value)</p></td> |
| <td><p>Set an attribute to a new value for all Parameters.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.BidirectionalCell.share_parameters" title="mxnet.gluon.rnn.BidirectionalCell.share_parameters"><code class="xref py py-obj docutils literal notranslate"><span class="pre">share_parameters</span></code></a>(shared)</p></td> |
| <td><p>Share parameters recursively inside the model.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.BidirectionalCell.state_info" title="mxnet.gluon.rnn.BidirectionalCell.state_info"><code class="xref py py-obj docutils literal notranslate"><span class="pre">state_info</span></code></a>([batch_size])</p></td> |
| <td><p>shape and layout information of states</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.BidirectionalCell.summary" title="mxnet.gluon.rnn.BidirectionalCell.summary"><code class="xref py py-obj docutils literal notranslate"><span class="pre">summary</span></code></a>(*inputs)</p></td> |
| <td><p>Print the summary of the model’s output and parameters.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.BidirectionalCell.unroll" title="mxnet.gluon.rnn.BidirectionalCell.unroll"><code class="xref py py-obj docutils literal notranslate"><span class="pre">unroll</span></code></a>(length, inputs[, begin_state, …])</p></td> |
| <td><p>Unrolls an RNN cell across time steps.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.BidirectionalCell.zero_grad" title="mxnet.gluon.rnn.BidirectionalCell.zero_grad"><code class="xref py py-obj docutils literal notranslate"><span class="pre">zero_grad</span></code></a>()</p></td> |
| <td><p>Sets all Parameters’ gradient buffer to 0.</p></td> |
| </tr> |
| </tbody> |
| </table> |
| <p><strong>Attributes</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.rnn.BidirectionalCell.params" title="mxnet.gluon.rnn.BidirectionalCell.params"><code class="xref py py-obj docutils literal notranslate"><span class="pre">params</span></code></a></p></td> |
| <td><p>Returns this <code class="xref py py-class docutils literal notranslate"><span class="pre">Block</span></code>’s parameter dictionary (does not include its children’s parameters).</p></td> |
| </tr> |
| </tbody> |
| </table> |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.BidirectionalCell.apply"> |
| <code class="sig-name descname">apply</code><span class="sig-paren">(</span><em class="sig-param">fn</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.rnn.BidirectionalCell.apply" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Applies <code class="docutils literal notranslate"><span class="pre">fn</span></code> recursively to every child block as well as self.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><p><strong>fn</strong> (<em>callable</em>) – Function to be applied to each submodule, of form <cite>fn(block)</cite>.</p> |
| </dd> |
| <dt class="field-even">Returns</dt> |
| <dd class="field-even"><p></p> |
| </dd> |
| <dt class="field-odd">Return type</dt> |
| <dd class="field-odd"><p>this block</p> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.BidirectionalCell.begin_state"> |
| <code class="sig-name descname">begin_state</code><span class="sig-paren">(</span><em class="sig-param">**kwargs</em><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/mxnet/gluon/rnn/rnn_cell.html#BidirectionalCell.begin_state"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.gluon.rnn.BidirectionalCell.begin_state" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Initial state for this cell.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>func</strong> (<em>callable</em><em>, </em><em>default symbol.zeros</em>) – <p>Function for creating initial state.</p> |
| <p>For Symbol API, func can be <cite>symbol.zeros</cite>, <cite>symbol.uniform</cite>, |
| <cite>symbol.var etc</cite>. Use <cite>symbol.var</cite> if you want to directly |
| feed input as states.</p> |
| <p>For NDArray API, func can be <cite>ndarray.zeros</cite>, <cite>ndarray.ones</cite>, etc.</p> |
| </p></li> |
| <li><p><strong>batch_size</strong> (<em>int</em><em>, </em><em>default 0</em>) – Only required for NDArray API. Size of the batch (‘N’ in layout) |
| dimension of input.</p></li> |
| <li><p><strong>**kwargs</strong> – Additional keyword arguments passed to func. For example |
| <cite>mean</cite>, <cite>std</cite>, <cite>dtype</cite>, etc.</p></li> |
| </ul> |
| </dd> |
| <dt class="field-even">Returns</dt> |
| <dd class="field-even"><p><strong>states</strong> – Starting states for the first RNN step.</p> |
| </dd> |
| <dt class="field-odd">Return type</dt> |
| <dd class="field-odd"><p>nested list of Symbol</p> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.BidirectionalCell.cast"> |
| <code class="sig-name descname">cast</code><span class="sig-paren">(</span><em class="sig-param">dtype</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.rnn.BidirectionalCell.cast" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Cast this Block to use another data type.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><p><strong>dtype</strong> (<em>str</em><em> or </em><em>numpy.dtype</em>) – The new data type.</p> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.BidirectionalCell.collect_params"> |
| <code class="sig-name descname">collect_params</code><span class="sig-paren">(</span><em class="sig-param">select=None</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.rnn.BidirectionalCell.collect_params" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Returns a <code class="xref py py-class docutils literal notranslate"><span class="pre">Dict</span></code> containing this <code class="xref py py-class docutils literal notranslate"><span class="pre">Block</span></code> and all of its |
| children’s Parameters(default), also can returns the select <code class="xref py py-class docutils literal notranslate"><span class="pre">Dict</span></code> |
| which match some given regular expressions.</p> |
| <p>For example, collect the specified parameters in [‘conv1.weight’, ‘conv1.bias’, ‘fc.weight’, |
| ‘fc.bias’]:</p> |
| <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">model</span><span class="o">.</span><span class="n">collect_params</span><span class="p">(</span><span class="s1">'conv1.weight|conv1.bias|fc.weight|fc.bias'</span><span class="p">)</span> |
| </pre></div> |
| </div> |
| <p>or collect all parameters whose names end with ‘weight’ or ‘bias’, this can be done |
| using regular expressions:</p> |
| <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">model</span><span class="o">.</span><span class="n">collect_params</span><span class="p">(</span><span class="s1">'.*weight|.*bias'</span><span class="p">)</span> |
| </pre></div> |
| </div> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><p><strong>select</strong> (<em>str</em>) – regular expressions</p> |
| </dd> |
| <dt class="field-even">Returns</dt> |
| <dd class="field-even"><p></p> |
| </dd> |
| <dt class="field-odd">Return type</dt> |
| <dd class="field-odd"><p>The selected <code class="xref py py-class docutils literal notranslate"><span class="pre">Dict</span></code></p> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.BidirectionalCell.export"> |
| <code class="sig-name descname">export</code><span class="sig-paren">(</span><em class="sig-param">path</em>, <em class="sig-param">epoch=0</em>, <em class="sig-param">remove_amp_cast=True</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.rnn.BidirectionalCell.export" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Export HybridBlock to json format that can be loaded by |
| <cite>gluon.SymbolBlock.imports</cite> or the C++ interface.</p> |
| <div class="admonition note"> |
| <p class="admonition-title">Note</p> |
| <p>When there are only one input, it will have name <cite>data</cite>. When there |
| Are more than one inputs, they will be named as <cite>data0</cite>, <cite>data1</cite>, etc.</p> |
| </div> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>path</strong> (<em>str</em><em> or </em><em>None</em>) – Path to save model. Two files <cite>path-symbol.json</cite> and <cite>path-xxxx.params</cite> |
| will be created, where xxxx is the 4 digits epoch number. |
| If None, do not export to file but return Python Symbol object and |
| corresponding dictionary of parameters.</p></li> |
| <li><p><strong>epoch</strong> (<em>int</em>) – Epoch number of saved model.</p></li> |
| <li><p><strong>remove_amp_cast</strong> (<em>bool</em><em>, </em><em>optional</em>) – Whether to remove the amp_cast and amp_multicast operators, before saving the model.</p></li> |
| </ul> |
| </dd> |
| <dt class="field-even">Returns</dt> |
| <dd class="field-even"><p><ul class="simple"> |
| <li><p><strong>symbol_filename</strong> (<em>str</em>) – Filename to which model symbols were saved, including <cite>path</cite> prefix.</p></li> |
| <li><p><strong>params_filename</strong> (<em>str</em>) – Filename to which model parameters were saved, including <cite>path</cite> prefix.</p></li> |
| </ul> |
| </p> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.BidirectionalCell.hybridize"> |
| <code class="sig-name descname">hybridize</code><span class="sig-paren">(</span><em class="sig-param">active=True</em>, <em class="sig-param">**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.rnn.BidirectionalCell.hybridize" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Please refer description of HybridBlock hybridize().</p> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.BidirectionalCell.infer_shape"> |
| <code class="sig-name descname">infer_shape</code><span class="sig-paren">(</span><em class="sig-param">i</em>, <em class="sig-param">x</em>, <em class="sig-param">is_bidirect</em><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/mxnet/gluon/rnn/rnn_cell.html#BidirectionalCell.infer_shape"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.gluon.rnn.BidirectionalCell.infer_shape" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Infers shape of Parameters from inputs.</p> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.BidirectionalCell.infer_type"> |
| <code class="sig-name descname">infer_type</code><span class="sig-paren">(</span><em class="sig-param">*args</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.rnn.BidirectionalCell.infer_type" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Infers data type of Parameters from inputs.</p> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.BidirectionalCell.initialize"> |
| <code class="sig-name descname">initialize</code><span class="sig-paren">(</span><em class="sig-param">init=<mxnet.initializer.Uniform object></em>, <em class="sig-param">device=None</em>, <em class="sig-param">verbose=False</em>, <em class="sig-param">force_reinit=False</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.rnn.BidirectionalCell.initialize" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Initializes <code class="xref py py-class docutils literal notranslate"><span class="pre">Parameter</span></code> s of this <code class="xref py py-class docutils literal notranslate"><span class="pre">Block</span></code> and its children.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>init</strong> (<a class="reference internal" href="../../initializer/index.html#mxnet.initializer.Initializer" title="mxnet.initializer.Initializer"><em>Initializer</em></a>) – Global default Initializer to be used when <code class="xref py py-meth docutils literal notranslate"><span class="pre">Parameter.init()</span></code> is <code class="docutils literal notranslate"><span class="pre">None</span></code>. |
| Otherwise, <code class="xref py py-meth docutils literal notranslate"><span class="pre">Parameter.init()</span></code> takes precedence.</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>) – Keeps a copy of Parameters on one or many device(s).</p></li> |
| <li><p><strong>verbose</strong> (<em>bool</em><em>, </em><em>default False</em>) – Whether to verbosely print out details on initialization.</p></li> |
| <li><p><strong>force_reinit</strong> (<em>bool</em><em>, </em><em>default False</em>) – Whether to force re-initialization if parameter is already initialized.</p></li> |
| </ul> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.BidirectionalCell.load"> |
| <code class="sig-name descname">load</code><span class="sig-paren">(</span><em class="sig-param">prefix</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.rnn.BidirectionalCell.load" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Load a model saved using the <cite>save</cite> API</p> |
| <p>Reconfigures a model using the saved configuration. This function |
| does not regenerate the model architecture. It resets each Block’s |
| parameter UUIDs as they were when saved in order to match the names of the |
| saved parameters.</p> |
| <p>This function assumes the Blocks in the model were created in the same |
| order they were when the model was saved. This is because each Block is |
| uniquely identified by Block class name and a unique ID in order (since |
| its an OrderedDict) and uses the unique ID to denote that specific Block.</p> |
| <p>Assumes that the model is created in an identical order every time. |
| If the model is not able to be recreated deterministically do not |
| use this set of APIs to save/load your model.</p> |
| <p>For HybridBlocks, the cached_graph (Symbol & inputs) and settings are |
| restored if it had been hybridized before saving.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><p><strong>prefix</strong> (<em>str</em>) – The prefix to use in filenames for loading this model: |
| <prefix>-model.json and <prefix>-model.params</p> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.BidirectionalCell.load_dict"> |
| <code class="sig-name descname">load_dict</code><span class="sig-paren">(</span><em class="sig-param">param_dict</em>, <em class="sig-param">device=None</em>, <em class="sig-param">allow_missing=False</em>, <em class="sig-param">ignore_extra=False</em>, <em class="sig-param">cast_dtype=False</em>, <em class="sig-param">dtype_source='current'</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.rnn.BidirectionalCell.load_dict" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Load parameters from dict</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>param_dict</strong> (<em>dict</em>) – Dictionary containing model 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>, </em><em>optional</em>) – Device context on which the memory is allocated. Default is |
| <cite>mxnet.device.current_device()</cite>.</p></li> |
| <li><p><strong>allow_missing</strong> (<em>bool</em><em>, </em><em>default False</em>) – Whether to silently skip loading parameters not represented in the file.</p></li> |
| <li><p><strong>ignore_extra</strong> (<em>bool</em><em>, </em><em>default False</em>) – Whether to silently ignore parameters from the file that are not |
| present in this dict.</p></li> |
| <li><p><strong>cast_dtype</strong> (<em>bool</em><em>, </em><em>default False</em>) – Cast the data type of the NDArray loaded from the checkpoint to the dtype |
| provided by the Parameter if any</p></li> |
| <li><p><strong>dtype_source</strong> (<em>str</em><em>, </em><em>default 'current'</em>) – must be in {‘current’, ‘saved’} |
| Only valid if cast_dtype=True, specify the source of the dtype for casting |
| the parameters</p></li> |
| </ul> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.BidirectionalCell.load_parameters"> |
| <code class="sig-name descname">load_parameters</code><span class="sig-paren">(</span><em class="sig-param">filename</em>, <em class="sig-param">device=None</em>, <em class="sig-param">allow_missing=False</em>, <em class="sig-param">ignore_extra=False</em>, <em class="sig-param">cast_dtype=False</em>, <em class="sig-param">dtype_source='current'</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.rnn.BidirectionalCell.load_parameters" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Load parameters from file previously saved by <cite>save_parameters</cite>.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>filename</strong> (<em>str</em>) – Path to parameter file.</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><em>, </em><em>default cpu</em><em>(</em><em>)</em>) – Device(s) to initialize loaded parameters on.</p></li> |
| <li><p><strong>allow_missing</strong> (<em>bool</em><em>, </em><em>default False</em>) – Whether to silently skip loading parameters not represents in the file.</p></li> |
| <li><p><strong>ignore_extra</strong> (<em>bool</em><em>, </em><em>default False</em>) – Whether to silently ignore parameters from the file that are not |
| present in this Block.</p></li> |
| <li><p><strong>cast_dtype</strong> (<em>bool</em><em>, </em><em>default False</em>) – Cast the data type of the NDArray loaded from the checkpoint to the dtype |
| provided by the Parameter if any.</p></li> |
| <li><p><strong>dtype_source</strong> (<em>str</em><em>, </em><em>default 'current'</em>) – must be in {‘current’, ‘saved’} |
| Only valid if cast_dtype=True, specify the source of the dtype for casting |
| the parameters</p></li> |
| </ul> |
| </dd> |
| </dl> |
| <p class="rubric">References</p> |
| <p><a class="reference external" href="https://mxnet.apache.org/api/python/docs/tutorials/packages/gluon/blocks/save_load_params.html">Saving and Loading Gluon Models</a></p> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.BidirectionalCell.optimize_for"> |
| <code class="sig-name descname">optimize_for</code><span class="sig-paren">(</span><em class="sig-param">x</em>, <em class="sig-param">*args</em>, <em class="sig-param">backend=None</em>, <em class="sig-param">clear=False</em>, <em class="sig-param">partition_if_dynamic=True</em>, <em class="sig-param">static_alloc=False</em>, <em class="sig-param">static_shape=False</em>, <em class="sig-param">inline_limit=2</em>, <em class="sig-param">forward_bulk_size=None</em>, <em class="sig-param">backward_bulk_size=None</em>, <em class="sig-param">**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.rnn.BidirectionalCell.optimize_for" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Partitions the current HybridBlock and optimizes it for a given backend |
| without executing a forward pass. Modifies the HybridBlock in-place.</p> |
| <p>Immediately partitions a HybridBlock using the specified backend. Combines |
| the work done in the hybridize API with part of the work done in the forward |
| pass without calling the CachedOp. Can be used in place of hybridize, |
| afterwards <cite>export</cite> can be called or inference can be run. See README.md in |
| example/extensions/lib_subgraph/README.md for more details.</p> |
| <p class="rubric">Examples</p> |
| <p># partition and then export to file |
| block.optimize_for(x, backend=’myPart’) |
| block.export(‘partitioned’)</p> |
| <p># partition and then run inference |
| block.optimize_for(x, backend=’myPart’) |
| block(x)</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>x</strong> (<a class="reference internal" href="../../legacy/ndarray/ndarray.html#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – first input to model</p></li> |
| <li><p><strong>*args</strong> (<a class="reference internal" href="../../legacy/ndarray/ndarray.html#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – other inputs to model</p></li> |
| <li><p><strong>backend</strong> (<em>str</em>) – The name of backend, as registered in <cite>SubgraphBackendRegistry</cite>, default None</p></li> |
| <li><p><strong>backend_opts</strong> (<em>dict of user-specified options to pass to the backend for partitioning</em><em>, </em><em>optional</em>) – Passed on to <cite>PrePartition</cite> and <cite>PostPartition</cite> functions of <cite>SubgraphProperty</cite></p></li> |
| <li><p><strong>clear</strong> (<em>bool</em><em>, </em><em>default False</em>) – clears any previous optimizations</p></li> |
| <li><p><strong>partition_if_dynamic</strong> (<em>bool</em><em>, </em><em>default False</em>) – whether to partition the graph when dynamic shape op exists</p></li> |
| <li><p><strong>static_alloc</strong> (<em>bool</em><em>, </em><em>default False</em>) – Statically allocate memory to improve speed. Memory usage may increase.</p></li> |
| <li><p><strong>static_shape</strong> (<em>bool</em><em>, </em><em>default False</em>) – Optimize for invariant input shapes between iterations. Must also |
| set static_alloc to True. Change of input shapes is still allowed |
| but slower.</p></li> |
| <li><p><strong>inline_limit</strong> (<em>optional int</em><em>, </em><em>default 2</em>) – Maximum number of operators that can be inlined.</p></li> |
| <li><p><strong>forward_bulk_size</strong> (<em>optional int</em><em>, </em><em>default None</em>) – Segment size of bulk execution during forward pass.</p></li> |
| <li><p><strong>backward_bulk_size</strong> (<em>optional int</em><em>, </em><em>default None</em>) – Segment size of bulk execution during backward pass.</p></li> |
| <li><p><strong>**kwargs</strong> (<em>The backend options</em><em>, </em><em>optional</em>) – Passed on to <cite>PrePartition</cite> and <cite>PostPartition</cite> functions of <cite>SubgraphProperty</cite></p></li> |
| </ul> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.BidirectionalCell.params"> |
| <em class="property">property </em><code class="sig-name descname">params</code><a class="headerlink" href="#mxnet.gluon.rnn.BidirectionalCell.params" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Returns this <code class="xref py py-class docutils literal notranslate"><span class="pre">Block</span></code>’s parameter dictionary (does not include its |
| children’s parameters).</p> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.BidirectionalCell.register_child"> |
| <code class="sig-name descname">register_child</code><span class="sig-paren">(</span><em class="sig-param">block</em>, <em class="sig-param">name=None</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.rnn.BidirectionalCell.register_child" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Registers block as a child of self. <code class="xref py py-class docutils literal notranslate"><span class="pre">Block</span></code> s assigned to self as |
| attributes will be registered automatically.</p> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.BidirectionalCell.register_forward_hook"> |
| <code class="sig-name descname">register_forward_hook</code><span class="sig-paren">(</span><em class="sig-param">hook</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.rnn.BidirectionalCell.register_forward_hook" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Registers a forward hook on the block.</p> |
| <p>The hook function is called immediately after <code class="xref py py-func docutils literal notranslate"><span class="pre">forward()</span></code>. |
| It should not modify the input or output.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><p><strong>hook</strong> (<em>callable</em>) – The forward hook function of form <cite>hook(block, input, output) -> None</cite>.</p> |
| </dd> |
| <dt class="field-even">Returns</dt> |
| <dd class="field-even"><p></p> |
| </dd> |
| <dt class="field-odd">Return type</dt> |
| <dd class="field-odd"><p><code class="xref py py-class docutils literal notranslate"><span class="pre">mxnet.gluon.utils.HookHandle</span></code></p> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.BidirectionalCell.register_forward_pre_hook"> |
| <code class="sig-name descname">register_forward_pre_hook</code><span class="sig-paren">(</span><em class="sig-param">hook</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.rnn.BidirectionalCell.register_forward_pre_hook" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Registers a forward pre-hook on the block.</p> |
| <p>The hook function is called immediately before <code class="xref py py-func docutils literal notranslate"><span class="pre">forward()</span></code>. |
| It should not modify the input or output.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><p><strong>hook</strong> (<em>callable</em>) – The forward hook function of form <cite>hook(block, input) -> None</cite>.</p> |
| </dd> |
| <dt class="field-even">Returns</dt> |
| <dd class="field-even"><p></p> |
| </dd> |
| <dt class="field-odd">Return type</dt> |
| <dd class="field-odd"><p><code class="xref py py-class docutils literal notranslate"><span class="pre">mxnet.gluon.utils.HookHandle</span></code></p> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.BidirectionalCell.register_op_hook"> |
| <code class="sig-name descname">register_op_hook</code><span class="sig-paren">(</span><em class="sig-param">callback</em>, <em class="sig-param">monitor_all=False</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.rnn.BidirectionalCell.register_op_hook" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Install callback monitor.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>callback</strong> (<em>function</em>) – Function called to inspect the values of the intermediate outputs |
| of blocks after hybridization. It takes 3 parameters: |
| name of the tensor being inspected (str) |
| name of the operator producing or consuming that tensor (str) |
| tensor being inspected (NDArray).</p></li> |
| <li><p><strong>monitor_all</strong> (<em>bool</em><em>, </em><em>default False</em>) – If True, monitor both input and output, otherwise monitor output only.</p></li> |
| </ul> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.BidirectionalCell.reset"> |
| <code class="sig-name descname">reset</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.rnn.BidirectionalCell.reset" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Reset before re-using the cell for another graph.</p> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.BidirectionalCell.reset_ctx"> |
| <code class="sig-name descname">reset_ctx</code><span class="sig-paren">(</span><em class="sig-param">ctx</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.rnn.BidirectionalCell.reset_ctx" title="Permalink to this definition">¶</a></dt> |
| <dd><p>This function has been deprecated. Please refer to <code class="docutils literal notranslate"><span class="pre">Block.reset_device</span></code>.</p> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.BidirectionalCell.reset_device"> |
| <code class="sig-name descname">reset_device</code><span class="sig-paren">(</span><em class="sig-param">device</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.rnn.BidirectionalCell.reset_device" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Re-assign all Parameters to other devices.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><p><strong>device</strong> (Device or list of Device, default <code class="xref py py-meth docutils literal notranslate"><span class="pre">device.current_device()</span></code>.) – Assign Parameter to given device. If device is a list of Device, a |
| copy will be made for each device.</p> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.BidirectionalCell.save"> |
| <code class="sig-name descname">save</code><span class="sig-paren">(</span><em class="sig-param">prefix</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.rnn.BidirectionalCell.save" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Save the model architecture and parameters to load again later</p> |
| <p>Saves the model architecture as a nested dictionary where each Block |
| in the model is a dictionary and its children are sub-dictionaries.</p> |
| <p>Each Block is uniquely identified by Block class name and a unique ID. |
| We save each Block’s parameter UUID to restore later in order to match |
| the saved parameters.</p> |
| <p>Recursively traverses a Block’s children in order (since its an |
| OrderedDict) and uses the unique ID to denote that specific Block.</p> |
| <p>Assumes that the model is created in an identical order every time. |
| If the model is not able to be recreated deterministically do not |
| use this set of APIs to save/load your model.</p> |
| <p>For HybridBlocks, the cached_graph is saved (Symbol & inputs) if |
| it has already been hybridized.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><p><strong>prefix</strong> (<em>str</em>) – The prefix to use in filenames for saving this model: |
| <prefix>-model.json and <prefix>-model.params</p> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.BidirectionalCell.save_parameters"> |
| <code class="sig-name descname">save_parameters</code><span class="sig-paren">(</span><em class="sig-param">filename</em>, <em class="sig-param">deduplicate=False</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.rnn.BidirectionalCell.save_parameters" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Save parameters to file.</p> |
| <p>Saved parameters can only be loaded with <cite>load_parameters</cite>. Note that this |
| method only saves parameters, not model structure. If you want to save |
| model structures, please use <code class="xref py py-meth docutils literal notranslate"><span class="pre">HybridBlock.export()</span></code>.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>filename</strong> (<em>str</em>) – Path to file.</p></li> |
| <li><p><strong>deduplicate</strong> (<em>bool</em><em>, </em><em>default False</em>) – If True, save shared parameters only once. Otherwise, if a Block |
| contains multiple sub-blocks that share parameters, each of the |
| shared parameters will be separately saved for every sub-block.</p></li> |
| </ul> |
| </dd> |
| </dl> |
| <p class="rubric">References</p> |
| <p><a class="reference external" href="https://mxnet.apache.org/api/python/docs/tutorials/packages/gluon/blocks/save_load_params.html">Saving and Loading Gluon Models</a></p> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.BidirectionalCell.setattr"> |
| <code class="sig-name descname">setattr</code><span class="sig-paren">(</span><em class="sig-param">name</em>, <em class="sig-param">value</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.rnn.BidirectionalCell.setattr" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Set an attribute to a new value for all Parameters.</p> |
| <p>For example, set grad_req to null if you don’t need gradient w.r.t a |
| model’s Parameters:</p> |
| <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">model</span><span class="o">.</span><span class="n">setattr</span><span class="p">(</span><span class="s1">'grad_req'</span><span class="p">,</span> <span class="s1">'null'</span><span class="p">)</span> |
| </pre></div> |
| </div> |
| <p>or change the learning rate multiplier:</p> |
| <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">model</span><span class="o">.</span><span class="n">setattr</span><span class="p">(</span><span class="s1">'lr_mult'</span><span class="p">,</span> <span class="mf">0.5</span><span class="p">)</span> |
| </pre></div> |
| </div> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>name</strong> (<em>str</em>) – Name of the attribute.</p></li> |
| <li><p><strong>value</strong> (<em>valid type for attribute name</em>) – The new value for the attribute.</p></li> |
| </ul> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.BidirectionalCell.share_parameters"> |
| <code class="sig-name descname">share_parameters</code><span class="sig-paren">(</span><em class="sig-param">shared</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.rnn.BidirectionalCell.share_parameters" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Share parameters recursively inside the model.</p> |
| <p>For example, if you want <code class="docutils literal notranslate"><span class="pre">dense1</span></code> to share <code class="docutils literal notranslate"><span class="pre">dense0</span></code>’s weights, you can do:</p> |
| <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">dense0</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Dense</span><span class="p">(</span><span class="mi">20</span><span class="p">)</span> |
| <span class="n">dense1</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Dense</span><span class="p">(</span><span class="mi">20</span><span class="p">)</span> |
| <span class="n">dense1</span><span class="o">.</span><span class="n">share_parameters</span><span class="p">(</span><span class="n">dense0</span><span class="o">.</span><span class="n">collect_params</span><span class="p">())</span> |
| </pre></div> |
| </div> |
| <dl class="simple"> |
| <dt>which equals to</dt><dd><p>dense1.weight = dense0.weight |
| dense1.bias = dense0.bias</p> |
| </dd> |
| </dl> |
| <p>Note that unlike the <cite>load_parameters</cite> or <cite>load_dict</cite> functions, |
| <cite>share_parameters</cite> results in the <cite>Parameter</cite> object being shared (or |
| tied) between the models, whereas <cite>load_parameters</cite> or <cite>load_dict</cite> only |
| set the value of the data dictionary of a model. If you call |
| <cite>load_parameters</cite> or <cite>load_dict</cite> after <cite>share_parameters</cite>, the loaded |
| value will be reflected in all networks that use the shared (or tied) |
| <cite>Parameter</cite> object.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><p><strong>shared</strong> (<em>Dict</em>) – Dict of the shared parameters.</p> |
| </dd> |
| <dt class="field-even">Returns</dt> |
| <dd class="field-even"><p></p> |
| </dd> |
| <dt class="field-odd">Return type</dt> |
| <dd class="field-odd"><p>this block</p> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.BidirectionalCell.state_info"> |
| <code class="sig-name descname">state_info</code><span class="sig-paren">(</span><em class="sig-param">batch_size=0</em><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/mxnet/gluon/rnn/rnn_cell.html#BidirectionalCell.state_info"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.gluon.rnn.BidirectionalCell.state_info" title="Permalink to this definition">¶</a></dt> |
| <dd><p>shape and layout information of states</p> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.BidirectionalCell.summary"> |
| <code class="sig-name descname">summary</code><span class="sig-paren">(</span><em class="sig-param">*inputs</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.rnn.BidirectionalCell.summary" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Print the summary of the model’s output and parameters.</p> |
| <p>The network must have been initialized, and must not have been hybridized.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><p><strong>inputs</strong> (<em>object</em>) – Any input that the model supports. For any tensor in the input, only |
| <a class="reference internal" href="../../legacy/ndarray/ndarray.html#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><code class="xref py py-class docutils literal notranslate"><span class="pre">mxnet.ndarray.NDArray</span></code></a> is supported.</p> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.BidirectionalCell.unroll"> |
| <code class="sig-name descname">unroll</code><span class="sig-paren">(</span><em class="sig-param">length</em>, <em class="sig-param">inputs</em>, <em class="sig-param">begin_state=None</em>, <em class="sig-param">layout='NTC'</em>, <em class="sig-param">merge_outputs=None</em>, <em class="sig-param">valid_length=None</em><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/mxnet/gluon/rnn/rnn_cell.html#BidirectionalCell.unroll"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.gluon.rnn.BidirectionalCell.unroll" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Unrolls an RNN cell across time steps.</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>) – Number of steps to unroll.</p></li> |
| <li><p><strong>inputs</strong> (<a class="reference internal" href="../../legacy/symbol/symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a><em>, </em><em>list of Symbol</em><em>, or </em><em>None</em>) – <p>If <cite>inputs</cite> is a single Symbol (usually the output |
| of Embedding symbol), it should have shape |
| (batch_size, length, …) if <cite>layout</cite> is ‘NTC’, |
| or (length, batch_size, …) if <cite>layout</cite> is ‘TNC’.</p> |
| <p>If <cite>inputs</cite> is a list of symbols (usually output of |
| previous unroll), they should all have shape |
| (batch_size, …).</p> |
| </p></li> |
| <li><p><strong>begin_state</strong> (<em>nested list of Symbol</em><em>, </em><em>optional</em>) – Input states created by <cite>begin_state()</cite> |
| or output state of another cell. |
| Created from <cite>begin_state()</cite> if <cite>None</cite>.</p></li> |
| <li><p><strong>layout</strong> (<em>str</em><em>, </em><em>optional</em>) – <cite>layout</cite> of input symbol. Only used if inputs |
| is a single Symbol.</p></li> |
| <li><p><strong>merge_outputs</strong> (<em>bool</em><em>, </em><em>optional</em>) – If <cite>False</cite>, returns outputs as a list of Symbols. |
| If <cite>True</cite>, concatenates output across time steps |
| and returns a single symbol with shape |
| (batch_size, length, …) if layout is ‘NTC’, |
| or (length, batch_size, …) if layout is ‘TNC’. |
| If <cite>None</cite>, output whatever is faster.</p></li> |
| <li><p><strong>valid_length</strong> (<a class="reference internal" href="../../legacy/symbol/symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a><em>, </em><a class="reference internal" href="../../legacy/ndarray/ndarray.html#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a><em> or </em><em>None</em>) – <cite>valid_length</cite> specifies the length of the sequences in the batch without padding. |
| This option is especially useful for building sequence-to-sequence models where |
| the input and output sequences would potentially be padded. |
| If <cite>valid_length</cite> is None, all sequences are assumed to have the same length. |
| If <cite>valid_length</cite> is a Symbol or NDArray, it should have shape (batch_size,). |
| The ith element will be the length of the ith sequence in the batch. |
| The last valid state will be return and the padded outputs will be masked with 0. |
| Note that <cite>valid_length</cite> must be smaller or equal to <cite>length</cite>.</p></li> |
| </ul> |
| </dd> |
| <dt class="field-even">Returns</dt> |
| <dd class="field-even"><p><ul class="simple"> |
| <li><p><strong>outputs</strong> (<em>list of Symbol or Symbol</em>) – Symbol (if <cite>merge_outputs</cite> is True) or list of Symbols |
| (if <cite>merge_outputs</cite> is False) corresponding to the output from |
| the RNN from this unrolling.</p></li> |
| <li><p><strong>states</strong> (<em>list of Symbol</em>) – The new state of this RNN after this unrolling. |
| The type of this symbol is same as the output of <cite>begin_state()</cite>.</p></li> |
| </ul> |
| </p> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.BidirectionalCell.zero_grad"> |
| <code class="sig-name descname">zero_grad</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.rnn.BidirectionalCell.zero_grad" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Sets all Parameters’ gradient buffer to 0.</p> |
| </dd></dl> |
| |
| </dd></dl> |
| |
| <dl class="class"> |
| <dt id="mxnet.gluon.rnn.Conv1DGRUCell"> |
| <em class="property">class </em><code class="sig-name descname">Conv1DGRUCell</code><span class="sig-paren">(</span><em class="sig-param">input_shape</em>, <em class="sig-param">hidden_channels</em>, <em class="sig-param">i2h_kernel</em>, <em class="sig-param">h2h_kernel</em>, <em class="sig-param">i2h_pad=(0</em>, <em class="sig-param">)</em>, <em class="sig-param">i2h_dilate=(1</em>, <em class="sig-param">)</em>, <em class="sig-param">h2h_dilate=(1</em>, <em class="sig-param">)</em>, <em class="sig-param">i2h_weight_initializer=None</em>, <em class="sig-param">h2h_weight_initializer=None</em>, <em class="sig-param">i2h_bias_initializer='zeros'</em>, <em class="sig-param">h2h_bias_initializer='zeros'</em>, <em class="sig-param">conv_layout='NCW'</em>, <em class="sig-param">activation='tanh'</em><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/mxnet/gluon/rnn/conv_rnn_cell.html#Conv1DGRUCell"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.gluon.rnn.Conv1DGRUCell" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">mxnet.gluon.rnn.conv_rnn_cell._ConvGRUCell</span></code></p> |
| <p>1D Convolutional Gated Rectified Unit (GRU) network cell.</p> |
| <div class="math notranslate nohighlight"> |
| \[\begin{split}\begin{array}{ll} |
| r_t = \sigma(W_r \ast x_t + R_r \ast h_{t-1} + b_r) \\ |
| z_t = \sigma(W_z \ast x_t + R_z \ast h_{t-1} + b_z) \\ |
| n_t = tanh(W_i \ast x_t + b_i + r_t \circ (R_n \ast h_{t-1} + b_n)) \\ |
| h^\prime_t = (1 - z_t) \circ n_t + z_t \circ h \\ |
| \end{array}\end{split}\]</div> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>input_shape</strong> (<em>tuple of int</em>) – Input tensor shape at each time step for each sample, excluding dimension of the batch size |
| and sequence length. Must be consistent with <cite>conv_layout</cite>. |
| For example, for layout ‘NCW’ the shape should be (C, W).</p></li> |
| <li><p><strong>hidden_channels</strong> (<em>int</em>) – Number of output channels.</p></li> |
| <li><p><strong>i2h_kernel</strong> (<em>int</em><em> or </em><em>tuple of int</em>) – Input convolution kernel sizes.</p></li> |
| <li><p><strong>h2h_kernel</strong> (<em>int</em><em> or </em><em>tuple of int</em>) – Recurrent convolution kernel sizes. Only odd-numbered sizes are supported.</p></li> |
| <li><p><strong>i2h_pad</strong> (<em>int</em><em> or </em><em>tuple of int</em><em>, </em><em>default</em><em> (</em><em>0</em><em>,</em><em>)</em>) – Pad for input convolution.</p></li> |
| <li><p><strong>i2h_dilate</strong> (<em>int</em><em> or </em><em>tuple of int</em><em>, </em><em>default</em><em> (</em><em>1</em><em>,</em><em>)</em>) – Input convolution dilate.</p></li> |
| <li><p><strong>h2h_dilate</strong> (<em>int</em><em> or </em><em>tuple of int</em><em>, </em><em>default</em><em> (</em><em>1</em><em>,</em><em>)</em>) – Recurrent convolution dilate.</p></li> |
| <li><p><strong>i2h_weight_initializer</strong> (<em>str</em><em> or </em><a class="reference internal" href="../../initializer/index.html#mxnet.initializer.Initializer" title="mxnet.initializer.Initializer"><em>Initializer</em></a>) – Initializer for the input weights matrix, used for the input convolutions.</p></li> |
| <li><p><strong>h2h_weight_initializer</strong> (<em>str</em><em> or </em><a class="reference internal" href="../../initializer/index.html#mxnet.initializer.Initializer" title="mxnet.initializer.Initializer"><em>Initializer</em></a>) – Initializer for the recurrent weights matrix, used for the input convolutions.</p></li> |
| <li><p><strong>i2h_bias_initializer</strong> (<em>str</em><em> or </em><a class="reference internal" href="../../initializer/index.html#mxnet.initializer.Initializer" title="mxnet.initializer.Initializer"><em>Initializer</em></a><em>, </em><em>default zeros</em>) – Initializer for the input convolution bias vectors.</p></li> |
| <li><p><strong>h2h_bias_initializer</strong> (<em>str</em><em> or </em><a class="reference internal" href="../../initializer/index.html#mxnet.initializer.Initializer" title="mxnet.initializer.Initializer"><em>Initializer</em></a><em>, </em><em>default zeros</em>) – Initializer for the recurrent convolution bias vectors.</p></li> |
| <li><p><strong>conv_layout</strong> (<em>str</em><em>, </em><em>default 'NCW'</em>) – Layout for all convolution inputs, outputs and weights. Options are ‘NCW’ and ‘NWC’.</p></li> |
| <li><p><strong>activation</strong> (<em>str</em><em> or </em><a class="reference internal" href="../block.html#mxnet.gluon.Block" title="mxnet.gluon.Block"><em>gluon.Block</em></a><em>, </em><em>default 'tanh'</em>) – Type of activation function used in n_t. |
| If argument type is string, it’s equivalent to nn.Activation(act_type=str). See |
| <a class="reference internal" href="../../legacy/ndarray/ndarray.html#mxnet.ndarray.Activation" title="mxnet.ndarray.Activation"><code class="xref py py-func docutils literal notranslate"><span class="pre">Activation()</span></code></a> for available choices. |
| Alternatively, other activation blocks such as nn.LeakyReLU can be used.</p></li> |
| </ul> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="class"> |
| <dt id="mxnet.gluon.rnn.Conv1DLSTMCell"> |
| <em class="property">class </em><code class="sig-name descname">Conv1DLSTMCell</code><span class="sig-paren">(</span><em class="sig-param">input_shape</em>, <em class="sig-param">hidden_channels</em>, <em class="sig-param">i2h_kernel</em>, <em class="sig-param">h2h_kernel</em>, <em class="sig-param">i2h_pad=(0</em>, <em class="sig-param">)</em>, <em class="sig-param">i2h_dilate=(1</em>, <em class="sig-param">)</em>, <em class="sig-param">h2h_dilate=(1</em>, <em class="sig-param">)</em>, <em class="sig-param">i2h_weight_initializer=None</em>, <em class="sig-param">h2h_weight_initializer=None</em>, <em class="sig-param">i2h_bias_initializer='zeros'</em>, <em class="sig-param">h2h_bias_initializer='zeros'</em>, <em class="sig-param">conv_layout='NCW'</em>, <em class="sig-param">activation='tanh'</em><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/mxnet/gluon/rnn/conv_rnn_cell.html#Conv1DLSTMCell"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.gluon.rnn.Conv1DLSTMCell" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">mxnet.gluon.rnn.conv_rnn_cell._ConvLSTMCell</span></code></p> |
| <p>1D Convolutional LSTM network cell.</p> |
| <p><a class="reference external" href="https://arxiv.org/abs/1506.04214">“Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting”</a> paper. Xingjian et al. NIPS2015</p> |
| <div class="math notranslate nohighlight"> |
| \[\begin{split}\begin{array}{ll} |
| i_t = \sigma(W_i \ast x_t + R_i \ast h_{t-1} + b_i) \\ |
| f_t = \sigma(W_f \ast x_t + R_f \ast h_{t-1} + b_f) \\ |
| o_t = \sigma(W_o \ast x_t + R_o \ast h_{t-1} + b_o) \\ |
| c^\prime_t = tanh(W_c \ast x_t + R_c \ast h_{t-1} + b_c) \\ |
| c_t = f_t \circ c_{t-1} + i_t \circ c^\prime_t \\ |
| h_t = o_t \circ tanh(c_t) \\ |
| \end{array}\end{split}\]</div> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>input_shape</strong> (<em>tuple of int</em>) – Input tensor shape at each time step for each sample, excluding dimension of the batch size |
| and sequence length. Must be consistent with <cite>conv_layout</cite>. |
| For example, for layout ‘NCW’ the shape should be (C, W).</p></li> |
| <li><p><strong>hidden_channels</strong> (<em>int</em>) – Number of output channels.</p></li> |
| <li><p><strong>i2h_kernel</strong> (<em>int</em><em> or </em><em>tuple of int</em>) – Input convolution kernel sizes.</p></li> |
| <li><p><strong>h2h_kernel</strong> (<em>int</em><em> or </em><em>tuple of int</em>) – Recurrent convolution kernel sizes. Only odd-numbered sizes are supported.</p></li> |
| <li><p><strong>i2h_pad</strong> (<em>int</em><em> or </em><em>tuple of int</em><em>, </em><em>default</em><em> (</em><em>0</em><em>,</em><em>)</em>) – Pad for input convolution.</p></li> |
| <li><p><strong>i2h_dilate</strong> (<em>int</em><em> or </em><em>tuple of int</em><em>, </em><em>default</em><em> (</em><em>1</em><em>,</em><em>)</em>) – Input convolution dilate.</p></li> |
| <li><p><strong>h2h_dilate</strong> (<em>int</em><em> or </em><em>tuple of int</em><em>, </em><em>default</em><em> (</em><em>1</em><em>,</em><em>)</em>) – Recurrent convolution dilate.</p></li> |
| <li><p><strong>i2h_weight_initializer</strong> (<em>str</em><em> or </em><a class="reference internal" href="../../initializer/index.html#mxnet.initializer.Initializer" title="mxnet.initializer.Initializer"><em>Initializer</em></a>) – Initializer for the input weights matrix, used for the input convolutions.</p></li> |
| <li><p><strong>h2h_weight_initializer</strong> (<em>str</em><em> or </em><a class="reference internal" href="../../initializer/index.html#mxnet.initializer.Initializer" title="mxnet.initializer.Initializer"><em>Initializer</em></a>) – Initializer for the recurrent weights matrix, used for the input convolutions.</p></li> |
| <li><p><strong>i2h_bias_initializer</strong> (<em>str</em><em> or </em><a class="reference internal" href="../../initializer/index.html#mxnet.initializer.Initializer" title="mxnet.initializer.Initializer"><em>Initializer</em></a><em>, </em><em>default zeros</em>) – Initializer for the input convolution bias vectors.</p></li> |
| <li><p><strong>h2h_bias_initializer</strong> (<em>str</em><em> or </em><a class="reference internal" href="../../initializer/index.html#mxnet.initializer.Initializer" title="mxnet.initializer.Initializer"><em>Initializer</em></a><em>, </em><em>default zeros</em>) – Initializer for the recurrent convolution bias vectors.</p></li> |
| <li><p><strong>conv_layout</strong> (<em>str</em><em>, </em><em>default 'NCW'</em>) – Layout for all convolution inputs, outputs and weights. Options are ‘NCW’ and ‘NWC’.</p></li> |
| <li><p><strong>activation</strong> (<em>str</em><em> or </em><a class="reference internal" href="../block.html#mxnet.gluon.Block" title="mxnet.gluon.Block"><em>gluon.Block</em></a><em>, </em><em>default 'tanh'</em>) – Type of activation function used in c^prime_t. |
| If argument type is string, it’s equivalent to nn.Activation(act_type=str). See |
| <a class="reference internal" href="../../legacy/ndarray/ndarray.html#mxnet.ndarray.Activation" title="mxnet.ndarray.Activation"><code class="xref py py-func docutils literal notranslate"><span class="pre">Activation()</span></code></a> for available choices. |
| Alternatively, other activation blocks such as nn.LeakyReLU can be used.</p></li> |
| </ul> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="class"> |
| <dt id="mxnet.gluon.rnn.Conv1DRNNCell"> |
| <em class="property">class </em><code class="sig-name descname">Conv1DRNNCell</code><span class="sig-paren">(</span><em class="sig-param">input_shape</em>, <em class="sig-param">hidden_channels</em>, <em class="sig-param">i2h_kernel</em>, <em class="sig-param">h2h_kernel</em>, <em class="sig-param">i2h_pad=(0</em>, <em class="sig-param">)</em>, <em class="sig-param">i2h_dilate=(1</em>, <em class="sig-param">)</em>, <em class="sig-param">h2h_dilate=(1</em>, <em class="sig-param">)</em>, <em class="sig-param">i2h_weight_initializer=None</em>, <em class="sig-param">h2h_weight_initializer=None</em>, <em class="sig-param">i2h_bias_initializer='zeros'</em>, <em class="sig-param">h2h_bias_initializer='zeros'</em>, <em class="sig-param">conv_layout='NCW'</em>, <em class="sig-param">activation='tanh'</em><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/mxnet/gluon/rnn/conv_rnn_cell.html#Conv1DRNNCell"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.gluon.rnn.Conv1DRNNCell" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">mxnet.gluon.rnn.conv_rnn_cell._ConvRNNCell</span></code></p> |
| <p>1D Convolutional RNN cell.</p> |
| <div class="math notranslate nohighlight"> |
| \[h_t = tanh(W_i \ast x_t + R_i \ast h_{t-1} + b_i)\]</div> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>input_shape</strong> (<em>tuple of int</em>) – Input tensor shape at each time step for each sample, excluding dimension of the batch size |
| and sequence length. Must be consistent with <cite>conv_layout</cite>. |
| For example, for layout ‘NCW’ the shape should be (C, W).</p></li> |
| <li><p><strong>hidden_channels</strong> (<em>int</em>) – Number of output channels.</p></li> |
| <li><p><strong>i2h_kernel</strong> (<em>int</em><em> or </em><em>tuple of int</em>) – Input convolution kernel sizes.</p></li> |
| <li><p><strong>h2h_kernel</strong> (<em>int</em><em> or </em><em>tuple of int</em>) – Recurrent convolution kernel sizes. Only odd-numbered sizes are supported.</p></li> |
| <li><p><strong>i2h_pad</strong> (<em>int</em><em> or </em><em>tuple of int</em><em>, </em><em>default</em><em> (</em><em>0</em><em>,</em><em>)</em>) – Pad for input convolution.</p></li> |
| <li><p><strong>i2h_dilate</strong> (<em>int</em><em> or </em><em>tuple of int</em><em>, </em><em>default</em><em> (</em><em>1</em><em>,</em><em>)</em>) – Input convolution dilate.</p></li> |
| <li><p><strong>h2h_dilate</strong> (<em>int</em><em> or </em><em>tuple of int</em><em>, </em><em>default</em><em> (</em><em>1</em><em>,</em><em>)</em>) – Recurrent convolution dilate.</p></li> |
| <li><p><strong>i2h_weight_initializer</strong> (<em>str</em><em> or </em><a class="reference internal" href="../../initializer/index.html#mxnet.initializer.Initializer" title="mxnet.initializer.Initializer"><em>Initializer</em></a>) – Initializer for the input weights matrix, used for the input convolutions.</p></li> |
| <li><p><strong>h2h_weight_initializer</strong> (<em>str</em><em> or </em><a class="reference internal" href="../../initializer/index.html#mxnet.initializer.Initializer" title="mxnet.initializer.Initializer"><em>Initializer</em></a>) – Initializer for the recurrent weights matrix, used for the input convolutions.</p></li> |
| <li><p><strong>i2h_bias_initializer</strong> (<em>str</em><em> or </em><a class="reference internal" href="../../initializer/index.html#mxnet.initializer.Initializer" title="mxnet.initializer.Initializer"><em>Initializer</em></a><em>, </em><em>default zeros</em>) – Initializer for the input convolution bias vectors.</p></li> |
| <li><p><strong>h2h_bias_initializer</strong> (<em>str</em><em> or </em><a class="reference internal" href="../../initializer/index.html#mxnet.initializer.Initializer" title="mxnet.initializer.Initializer"><em>Initializer</em></a><em>, </em><em>default zeros</em>) – Initializer for the recurrent convolution bias vectors.</p></li> |
| <li><p><strong>conv_layout</strong> (<em>str</em><em>, </em><em>default 'NCW'</em>) – Layout for all convolution inputs, outputs and weights. Options are ‘NCW’ and ‘NWC’.</p></li> |
| <li><p><strong>activation</strong> (<em>str</em><em> or </em><a class="reference internal" href="../block.html#mxnet.gluon.Block" title="mxnet.gluon.Block"><em>gluon.Block</em></a><em>, </em><em>default 'tanh'</em>) – Type of activation function. |
| If argument type is string, it’s equivalent to nn.Activation(act_type=str). See |
| <a class="reference internal" href="../../legacy/ndarray/ndarray.html#mxnet.ndarray.Activation" title="mxnet.ndarray.Activation"><code class="xref py py-func docutils literal notranslate"><span class="pre">Activation()</span></code></a> for available choices. |
| Alternatively, other activation blocks such as nn.LeakyReLU can be used.</p></li> |
| </ul> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="class"> |
| <dt id="mxnet.gluon.rnn.Conv2DGRUCell"> |
| <em class="property">class </em><code class="sig-name descname">Conv2DGRUCell</code><span class="sig-paren">(</span><em class="sig-param">input_shape</em>, <em class="sig-param">hidden_channels</em>, <em class="sig-param">i2h_kernel</em>, <em class="sig-param">h2h_kernel</em>, <em class="sig-param">i2h_pad=(0</em>, <em class="sig-param">0)</em>, <em class="sig-param">i2h_dilate=(1</em>, <em class="sig-param">1)</em>, <em class="sig-param">h2h_dilate=(1</em>, <em class="sig-param">1)</em>, <em class="sig-param">i2h_weight_initializer=None</em>, <em class="sig-param">h2h_weight_initializer=None</em>, <em class="sig-param">i2h_bias_initializer='zeros'</em>, <em class="sig-param">h2h_bias_initializer='zeros'</em>, <em class="sig-param">conv_layout='NCHW'</em>, <em class="sig-param">activation='tanh'</em><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/mxnet/gluon/rnn/conv_rnn_cell.html#Conv2DGRUCell"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.gluon.rnn.Conv2DGRUCell" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">mxnet.gluon.rnn.conv_rnn_cell._ConvGRUCell</span></code></p> |
| <p>2D Convolutional Gated Rectified Unit (GRU) network cell.</p> |
| <div class="math notranslate nohighlight"> |
| \[\begin{split}\begin{array}{ll} |
| r_t = \sigma(W_r \ast x_t + R_r \ast h_{t-1} + b_r) \\ |
| z_t = \sigma(W_z \ast x_t + R_z \ast h_{t-1} + b_z) \\ |
| n_t = tanh(W_i \ast x_t + b_i + r_t \circ (R_n \ast h_{t-1} + b_n)) \\ |
| h^\prime_t = (1 - z_t) \circ n_t + z_t \circ h \\ |
| \end{array}\end{split}\]</div> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>input_shape</strong> (<em>tuple of int</em>) – Input tensor shape at each time step for each sample, excluding dimension of the batch size |
| and sequence length. Must be consistent with <cite>conv_layout</cite>. |
| For example, for layout ‘NCHW’ the shape should be (C, H, W).</p></li> |
| <li><p><strong>hidden_channels</strong> (<em>int</em>) – Number of output channels.</p></li> |
| <li><p><strong>i2h_kernel</strong> (<em>int</em><em> or </em><em>tuple of int</em>) – Input convolution kernel sizes.</p></li> |
| <li><p><strong>h2h_kernel</strong> (<em>int</em><em> or </em><em>tuple of int</em>) – Recurrent convolution kernel sizes. Only odd-numbered sizes are supported.</p></li> |
| <li><p><strong>i2h_pad</strong> (<em>int</em><em> or </em><em>tuple of int</em><em>, </em><em>default</em><em> (</em><em>0</em><em>, </em><em>0</em><em>)</em>) – Pad for input convolution.</p></li> |
| <li><p><strong>i2h_dilate</strong> (<em>int</em><em> or </em><em>tuple of int</em><em>, </em><em>default</em><em> (</em><em>1</em><em>, </em><em>1</em><em>)</em>) – Input convolution dilate.</p></li> |
| <li><p><strong>h2h_dilate</strong> (<em>int</em><em> or </em><em>tuple of int</em><em>, </em><em>default</em><em> (</em><em>1</em><em>, </em><em>1</em><em>)</em>) – Recurrent convolution dilate.</p></li> |
| <li><p><strong>i2h_weight_initializer</strong> (<em>str</em><em> or </em><a class="reference internal" href="../../initializer/index.html#mxnet.initializer.Initializer" title="mxnet.initializer.Initializer"><em>Initializer</em></a>) – Initializer for the input weights matrix, used for the input convolutions.</p></li> |
| <li><p><strong>h2h_weight_initializer</strong> (<em>str</em><em> or </em><a class="reference internal" href="../../initializer/index.html#mxnet.initializer.Initializer" title="mxnet.initializer.Initializer"><em>Initializer</em></a>) – Initializer for the recurrent weights matrix, used for the input convolutions.</p></li> |
| <li><p><strong>i2h_bias_initializer</strong> (<em>str</em><em> or </em><a class="reference internal" href="../../initializer/index.html#mxnet.initializer.Initializer" title="mxnet.initializer.Initializer"><em>Initializer</em></a><em>, </em><em>default zeros</em>) – Initializer for the input convolution bias vectors.</p></li> |
| <li><p><strong>h2h_bias_initializer</strong> (<em>str</em><em> or </em><a class="reference internal" href="../../initializer/index.html#mxnet.initializer.Initializer" title="mxnet.initializer.Initializer"><em>Initializer</em></a><em>, </em><em>default zeros</em>) – Initializer for the recurrent convolution bias vectors.</p></li> |
| <li><p><strong>conv_layout</strong> (<em>str</em><em>, </em><em>default 'NCHW'</em>) – Layout for all convolution inputs, outputs and weights. Options are ‘NCHW’ and ‘NHWC’.</p></li> |
| <li><p><strong>activation</strong> (<em>str</em><em> or </em><a class="reference internal" href="../block.html#mxnet.gluon.Block" title="mxnet.gluon.Block"><em>gluon.Block</em></a><em>, </em><em>default 'tanh'</em>) – Type of activation function used in n_t. |
| If argument type is string, it’s equivalent to nn.Activation(act_type=str). See |
| <a class="reference internal" href="../../legacy/ndarray/ndarray.html#mxnet.ndarray.Activation" title="mxnet.ndarray.Activation"><code class="xref py py-func docutils literal notranslate"><span class="pre">Activation()</span></code></a> for available choices. |
| Alternatively, other activation blocks such as nn.LeakyReLU can be used.</p></li> |
| </ul> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="class"> |
| <dt id="mxnet.gluon.rnn.Conv2DLSTMCell"> |
| <em class="property">class </em><code class="sig-name descname">Conv2DLSTMCell</code><span class="sig-paren">(</span><em class="sig-param">input_shape</em>, <em class="sig-param">hidden_channels</em>, <em class="sig-param">i2h_kernel</em>, <em class="sig-param">h2h_kernel</em>, <em class="sig-param">i2h_pad=(0</em>, <em class="sig-param">0)</em>, <em class="sig-param">i2h_dilate=(1</em>, <em class="sig-param">1)</em>, <em class="sig-param">h2h_dilate=(1</em>, <em class="sig-param">1)</em>, <em class="sig-param">i2h_weight_initializer=None</em>, <em class="sig-param">h2h_weight_initializer=None</em>, <em class="sig-param">i2h_bias_initializer='zeros'</em>, <em class="sig-param">h2h_bias_initializer='zeros'</em>, <em class="sig-param">conv_layout='NCHW'</em>, <em class="sig-param">activation='tanh'</em><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/mxnet/gluon/rnn/conv_rnn_cell.html#Conv2DLSTMCell"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.gluon.rnn.Conv2DLSTMCell" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">mxnet.gluon.rnn.conv_rnn_cell._ConvLSTMCell</span></code></p> |
| <p>2D Convolutional LSTM network cell.</p> |
| <p><a class="reference external" href="https://arxiv.org/abs/1506.04214">“Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting”</a> paper. Xingjian et al. NIPS2015</p> |
| <div class="math notranslate nohighlight"> |
| \[\begin{split}\begin{array}{ll} |
| i_t = \sigma(W_i \ast x_t + R_i \ast h_{t-1} + b_i) \\ |
| f_t = \sigma(W_f \ast x_t + R_f \ast h_{t-1} + b_f) \\ |
| o_t = \sigma(W_o \ast x_t + R_o \ast h_{t-1} + b_o) \\ |
| c^\prime_t = tanh(W_c \ast x_t + R_c \ast h_{t-1} + b_c) \\ |
| c_t = f_t \circ c_{t-1} + i_t \circ c^\prime_t \\ |
| h_t = o_t \circ tanh(c_t) \\ |
| \end{array}\end{split}\]</div> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>input_shape</strong> (<em>tuple of int</em>) – Input tensor shape at each time step for each sample, excluding dimension of the batch size |
| and sequence length. Must be consistent with <cite>conv_layout</cite>. |
| For example, for layout ‘NCHW’ the shape should be (C, H, W).</p></li> |
| <li><p><strong>hidden_channels</strong> (<em>int</em>) – Number of output channels.</p></li> |
| <li><p><strong>i2h_kernel</strong> (<em>int</em><em> or </em><em>tuple of int</em>) – Input convolution kernel sizes.</p></li> |
| <li><p><strong>h2h_kernel</strong> (<em>int</em><em> or </em><em>tuple of int</em>) – Recurrent convolution kernel sizes. Only odd-numbered sizes are supported.</p></li> |
| <li><p><strong>i2h_pad</strong> (<em>int</em><em> or </em><em>tuple of int</em><em>, </em><em>default</em><em> (</em><em>0</em><em>, </em><em>0</em><em>)</em>) – Pad for input convolution.</p></li> |
| <li><p><strong>i2h_dilate</strong> (<em>int</em><em> or </em><em>tuple of int</em><em>, </em><em>default</em><em> (</em><em>1</em><em>, </em><em>1</em><em>)</em>) – Input convolution dilate.</p></li> |
| <li><p><strong>h2h_dilate</strong> (<em>int</em><em> or </em><em>tuple of int</em><em>, </em><em>default</em><em> (</em><em>1</em><em>, </em><em>1</em><em>)</em>) – Recurrent convolution dilate.</p></li> |
| <li><p><strong>i2h_weight_initializer</strong> (<em>str</em><em> or </em><a class="reference internal" href="../../initializer/index.html#mxnet.initializer.Initializer" title="mxnet.initializer.Initializer"><em>Initializer</em></a>) – Initializer for the input weights matrix, used for the input convolutions.</p></li> |
| <li><p><strong>h2h_weight_initializer</strong> (<em>str</em><em> or </em><a class="reference internal" href="../../initializer/index.html#mxnet.initializer.Initializer" title="mxnet.initializer.Initializer"><em>Initializer</em></a>) – Initializer for the recurrent weights matrix, used for the input convolutions.</p></li> |
| <li><p><strong>i2h_bias_initializer</strong> (<em>str</em><em> or </em><a class="reference internal" href="../../initializer/index.html#mxnet.initializer.Initializer" title="mxnet.initializer.Initializer"><em>Initializer</em></a><em>, </em><em>default zeros</em>) – Initializer for the input convolution bias vectors.</p></li> |
| <li><p><strong>h2h_bias_initializer</strong> (<em>str</em><em> or </em><a class="reference internal" href="../../initializer/index.html#mxnet.initializer.Initializer" title="mxnet.initializer.Initializer"><em>Initializer</em></a><em>, </em><em>default zeros</em>) – Initializer for the recurrent convolution bias vectors.</p></li> |
| <li><p><strong>conv_layout</strong> (<em>str</em><em>, </em><em>default 'NCHW'</em>) – Layout for all convolution inputs, outputs and weights. Options are ‘NCHW’ and ‘NHWC’.</p></li> |
| <li><p><strong>activation</strong> (<em>str</em><em> or </em><a class="reference internal" href="../block.html#mxnet.gluon.Block" title="mxnet.gluon.Block"><em>gluon.Block</em></a><em>, </em><em>default 'tanh'</em>) – Type of activation function used in c^prime_t. |
| If argument type is string, it’s equivalent to nn.Activation(act_type=str). See |
| <a class="reference internal" href="../../legacy/ndarray/ndarray.html#mxnet.ndarray.Activation" title="mxnet.ndarray.Activation"><code class="xref py py-func docutils literal notranslate"><span class="pre">Activation()</span></code></a> for available choices. |
| Alternatively, other activation blocks such as nn.LeakyReLU can be used.</p></li> |
| </ul> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="class"> |
| <dt id="mxnet.gluon.rnn.Conv2DRNNCell"> |
| <em class="property">class </em><code class="sig-name descname">Conv2DRNNCell</code><span class="sig-paren">(</span><em class="sig-param">input_shape</em>, <em class="sig-param">hidden_channels</em>, <em class="sig-param">i2h_kernel</em>, <em class="sig-param">h2h_kernel</em>, <em class="sig-param">i2h_pad=(0</em>, <em class="sig-param">0)</em>, <em class="sig-param">i2h_dilate=(1</em>, <em class="sig-param">1)</em>, <em class="sig-param">h2h_dilate=(1</em>, <em class="sig-param">1)</em>, <em class="sig-param">i2h_weight_initializer=None</em>, <em class="sig-param">h2h_weight_initializer=None</em>, <em class="sig-param">i2h_bias_initializer='zeros'</em>, <em class="sig-param">h2h_bias_initializer='zeros'</em>, <em class="sig-param">conv_layout='NCHW'</em>, <em class="sig-param">activation='tanh'</em><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/mxnet/gluon/rnn/conv_rnn_cell.html#Conv2DRNNCell"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.gluon.rnn.Conv2DRNNCell" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">mxnet.gluon.rnn.conv_rnn_cell._ConvRNNCell</span></code></p> |
| <p>2D Convolutional RNN cell.</p> |
| <div class="math notranslate nohighlight"> |
| \[h_t = tanh(W_i \ast x_t + R_i \ast h_{t-1} + b_i)\]</div> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>input_shape</strong> (<em>tuple of int</em>) – Input tensor shape at each time step for each sample, excluding dimension of the batch size |
| and sequence length. Must be consistent with <cite>conv_layout</cite>. |
| For example, for layout ‘NCHW’ the shape should be (C, H, W).</p></li> |
| <li><p><strong>hidden_channels</strong> (<em>int</em>) – Number of output channels.</p></li> |
| <li><p><strong>i2h_kernel</strong> (<em>int</em><em> or </em><em>tuple of int</em>) – Input convolution kernel sizes.</p></li> |
| <li><p><strong>h2h_kernel</strong> (<em>int</em><em> or </em><em>tuple of int</em>) – Recurrent convolution kernel sizes. Only odd-numbered sizes are supported.</p></li> |
| <li><p><strong>i2h_pad</strong> (<em>int</em><em> or </em><em>tuple of int</em><em>, </em><em>default</em><em> (</em><em>0</em><em>, </em><em>0</em><em>)</em>) – Pad for input convolution.</p></li> |
| <li><p><strong>i2h_dilate</strong> (<em>int</em><em> or </em><em>tuple of int</em><em>, </em><em>default</em><em> (</em><em>1</em><em>, </em><em>1</em><em>)</em>) – Input convolution dilate.</p></li> |
| <li><p><strong>h2h_dilate</strong> (<em>int</em><em> or </em><em>tuple of int</em><em>, </em><em>default</em><em> (</em><em>1</em><em>, </em><em>1</em><em>)</em>) – Recurrent convolution dilate.</p></li> |
| <li><p><strong>i2h_weight_initializer</strong> (<em>str</em><em> or </em><a class="reference internal" href="../../initializer/index.html#mxnet.initializer.Initializer" title="mxnet.initializer.Initializer"><em>Initializer</em></a>) – Initializer for the input weights matrix, used for the input convolutions.</p></li> |
| <li><p><strong>h2h_weight_initializer</strong> (<em>str</em><em> or </em><a class="reference internal" href="../../initializer/index.html#mxnet.initializer.Initializer" title="mxnet.initializer.Initializer"><em>Initializer</em></a>) – Initializer for the recurrent weights matrix, used for the input convolutions.</p></li> |
| <li><p><strong>i2h_bias_initializer</strong> (<em>str</em><em> or </em><a class="reference internal" href="../../initializer/index.html#mxnet.initializer.Initializer" title="mxnet.initializer.Initializer"><em>Initializer</em></a><em>, </em><em>default zeros</em>) – Initializer for the input convolution bias vectors.</p></li> |
| <li><p><strong>h2h_bias_initializer</strong> (<em>str</em><em> or </em><a class="reference internal" href="../../initializer/index.html#mxnet.initializer.Initializer" title="mxnet.initializer.Initializer"><em>Initializer</em></a><em>, </em><em>default zeros</em>) – Initializer for the recurrent convolution bias vectors.</p></li> |
| <li><p><strong>conv_layout</strong> (<em>str</em><em>, </em><em>default 'NCHW'</em>) – Layout for all convolution inputs, outputs and weights. Options are ‘NCHW’ and ‘NHWC’.</p></li> |
| <li><p><strong>activation</strong> (<em>str</em><em> or </em><a class="reference internal" href="../block.html#mxnet.gluon.Block" title="mxnet.gluon.Block"><em>gluon.Block</em></a><em>, </em><em>default 'tanh'</em>) – Type of activation function. |
| If argument type is string, it’s equivalent to nn.Activation(act_type=str). See |
| <a class="reference internal" href="../../legacy/ndarray/ndarray.html#mxnet.ndarray.Activation" title="mxnet.ndarray.Activation"><code class="xref py py-func docutils literal notranslate"><span class="pre">Activation()</span></code></a> for available choices. |
| Alternatively, other activation blocks such as nn.LeakyReLU can be used.</p></li> |
| </ul> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="class"> |
| <dt id="mxnet.gluon.rnn.Conv3DGRUCell"> |
| <em class="property">class </em><code class="sig-name descname">Conv3DGRUCell</code><span class="sig-paren">(</span><em class="sig-param">input_shape</em>, <em class="sig-param">hidden_channels</em>, <em class="sig-param">i2h_kernel</em>, <em class="sig-param">h2h_kernel</em>, <em class="sig-param">i2h_pad=(0</em>, <em class="sig-param">0</em>, <em class="sig-param">0)</em>, <em class="sig-param">i2h_dilate=(1</em>, <em class="sig-param">1</em>, <em class="sig-param">1)</em>, <em class="sig-param">h2h_dilate=(1</em>, <em class="sig-param">1</em>, <em class="sig-param">1)</em>, <em class="sig-param">i2h_weight_initializer=None</em>, <em class="sig-param">h2h_weight_initializer=None</em>, <em class="sig-param">i2h_bias_initializer='zeros'</em>, <em class="sig-param">h2h_bias_initializer='zeros'</em>, <em class="sig-param">conv_layout='NCDHW'</em>, <em class="sig-param">activation='tanh'</em><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/mxnet/gluon/rnn/conv_rnn_cell.html#Conv3DGRUCell"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.gluon.rnn.Conv3DGRUCell" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">mxnet.gluon.rnn.conv_rnn_cell._ConvGRUCell</span></code></p> |
| <p>3D Convolutional Gated Rectified Unit (GRU) network cell.</p> |
| <div class="math notranslate nohighlight"> |
| \[\begin{split}\begin{array}{ll} |
| r_t = \sigma(W_r \ast x_t + R_r \ast h_{t-1} + b_r) \\ |
| z_t = \sigma(W_z \ast x_t + R_z \ast h_{t-1} + b_z) \\ |
| n_t = tanh(W_i \ast x_t + b_i + r_t \circ (R_n \ast h_{t-1} + b_n)) \\ |
| h^\prime_t = (1 - z_t) \circ n_t + z_t \circ h \\ |
| \end{array}\end{split}\]</div> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>input_shape</strong> (<em>tuple of int</em>) – Input tensor shape at each time step for each sample, excluding dimension of the batch size |
| and sequence length. Must be consistent with <cite>conv_layout</cite>. |
| For example, for layout ‘NCDHW’ the shape should be (C, D, H, W).</p></li> |
| <li><p><strong>hidden_channels</strong> (<em>int</em>) – Number of output channels.</p></li> |
| <li><p><strong>i2h_kernel</strong> (<em>int</em><em> or </em><em>tuple of int</em>) – Input convolution kernel sizes.</p></li> |
| <li><p><strong>h2h_kernel</strong> (<em>int</em><em> or </em><em>tuple of int</em>) – Recurrent convolution kernel sizes. Only odd-numbered sizes are supported.</p></li> |
| <li><p><strong>i2h_pad</strong> (<em>int</em><em> or </em><em>tuple of int</em><em>, </em><em>default</em><em> (</em><em>0</em><em>, </em><em>0</em><em>, </em><em>0</em><em>)</em>) – Pad for input convolution.</p></li> |
| <li><p><strong>i2h_dilate</strong> (<em>int</em><em> or </em><em>tuple of int</em><em>, </em><em>default</em><em> (</em><em>1</em><em>, </em><em>1</em><em>, </em><em>1</em><em>)</em>) – Input convolution dilate.</p></li> |
| <li><p><strong>h2h_dilate</strong> (<em>int</em><em> or </em><em>tuple of int</em><em>, </em><em>default</em><em> (</em><em>1</em><em>, </em><em>1</em><em>, </em><em>1</em><em>)</em>) – Recurrent convolution dilate.</p></li> |
| <li><p><strong>i2h_weight_initializer</strong> (<em>str</em><em> or </em><a class="reference internal" href="../../initializer/index.html#mxnet.initializer.Initializer" title="mxnet.initializer.Initializer"><em>Initializer</em></a>) – Initializer for the input weights matrix, used for the input convolutions.</p></li> |
| <li><p><strong>h2h_weight_initializer</strong> (<em>str</em><em> or </em><a class="reference internal" href="../../initializer/index.html#mxnet.initializer.Initializer" title="mxnet.initializer.Initializer"><em>Initializer</em></a>) – Initializer for the recurrent weights matrix, used for the input convolutions.</p></li> |
| <li><p><strong>i2h_bias_initializer</strong> (<em>str</em><em> or </em><a class="reference internal" href="../../initializer/index.html#mxnet.initializer.Initializer" title="mxnet.initializer.Initializer"><em>Initializer</em></a><em>, </em><em>default zeros</em>) – Initializer for the input convolution bias vectors.</p></li> |
| <li><p><strong>h2h_bias_initializer</strong> (<em>str</em><em> or </em><a class="reference internal" href="../../initializer/index.html#mxnet.initializer.Initializer" title="mxnet.initializer.Initializer"><em>Initializer</em></a><em>, </em><em>default zeros</em>) – Initializer for the recurrent convolution bias vectors.</p></li> |
| <li><p><strong>conv_layout</strong> (<em>str</em><em>, </em><em>default 'NCDHW'</em>) – Layout for all convolution inputs, outputs and weights. Options are ‘NCDHW’ and ‘NDHWC’.</p></li> |
| <li><p><strong>activation</strong> (<em>str</em><em> or </em><a class="reference internal" href="../block.html#mxnet.gluon.Block" title="mxnet.gluon.Block"><em>gluon.Block</em></a><em>, </em><em>default 'tanh'</em>) – Type of activation function used in n_t. |
| If argument type is string, it’s equivalent to nn.Activation(act_type=str). See |
| <a class="reference internal" href="../../legacy/ndarray/ndarray.html#mxnet.ndarray.Activation" title="mxnet.ndarray.Activation"><code class="xref py py-func docutils literal notranslate"><span class="pre">Activation()</span></code></a> for available choices. |
| Alternatively, other activation blocks such as nn.LeakyReLU can be used.</p></li> |
| </ul> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="class"> |
| <dt id="mxnet.gluon.rnn.Conv3DLSTMCell"> |
| <em class="property">class </em><code class="sig-name descname">Conv3DLSTMCell</code><span class="sig-paren">(</span><em class="sig-param">input_shape</em>, <em class="sig-param">hidden_channels</em>, <em class="sig-param">i2h_kernel</em>, <em class="sig-param">h2h_kernel</em>, <em class="sig-param">i2h_pad=(0</em>, <em class="sig-param">0</em>, <em class="sig-param">0)</em>, <em class="sig-param">i2h_dilate=(1</em>, <em class="sig-param">1</em>, <em class="sig-param">1)</em>, <em class="sig-param">h2h_dilate=(1</em>, <em class="sig-param">1</em>, <em class="sig-param">1)</em>, <em class="sig-param">i2h_weight_initializer=None</em>, <em class="sig-param">h2h_weight_initializer=None</em>, <em class="sig-param">i2h_bias_initializer='zeros'</em>, <em class="sig-param">h2h_bias_initializer='zeros'</em>, <em class="sig-param">conv_layout='NCDHW'</em>, <em class="sig-param">activation='tanh'</em><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/mxnet/gluon/rnn/conv_rnn_cell.html#Conv3DLSTMCell"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.gluon.rnn.Conv3DLSTMCell" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">mxnet.gluon.rnn.conv_rnn_cell._ConvLSTMCell</span></code></p> |
| <p>3D Convolutional LSTM network cell.</p> |
| <p><a class="reference external" href="https://arxiv.org/abs/1506.04214">“Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting”</a> paper. Xingjian et al. NIPS2015</p> |
| <div class="math notranslate nohighlight"> |
| \[\begin{split}\begin{array}{ll} |
| i_t = \sigma(W_i \ast x_t + R_i \ast h_{t-1} + b_i) \\ |
| f_t = \sigma(W_f \ast x_t + R_f \ast h_{t-1} + b_f) \\ |
| o_t = \sigma(W_o \ast x_t + R_o \ast h_{t-1} + b_o) \\ |
| c^\prime_t = tanh(W_c \ast x_t + R_c \ast h_{t-1} + b_c) \\ |
| c_t = f_t \circ c_{t-1} + i_t \circ c^\prime_t \\ |
| h_t = o_t \circ tanh(c_t) \\ |
| \end{array}\end{split}\]</div> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>input_shape</strong> (<em>tuple of int</em>) – Input tensor shape at each time step for each sample, excluding dimension of the batch size |
| and sequence length. Must be consistent with <cite>conv_layout</cite>. |
| For example, for layout ‘NCDHW’ the shape should be (C, D, H, W).</p></li> |
| <li><p><strong>hidden_channels</strong> (<em>int</em>) – Number of output channels.</p></li> |
| <li><p><strong>i2h_kernel</strong> (<em>int</em><em> or </em><em>tuple of int</em>) – Input convolution kernel sizes.</p></li> |
| <li><p><strong>h2h_kernel</strong> (<em>int</em><em> or </em><em>tuple of int</em>) – Recurrent convolution kernel sizes. Only odd-numbered sizes are supported.</p></li> |
| <li><p><strong>i2h_pad</strong> (<em>int</em><em> or </em><em>tuple of int</em><em>, </em><em>default</em><em> (</em><em>0</em><em>, </em><em>0</em><em>, </em><em>0</em><em>)</em>) – Pad for input convolution.</p></li> |
| <li><p><strong>i2h_dilate</strong> (<em>int</em><em> or </em><em>tuple of int</em><em>, </em><em>default</em><em> (</em><em>1</em><em>, </em><em>1</em><em>, </em><em>1</em><em>)</em>) – Input convolution dilate.</p></li> |
| <li><p><strong>h2h_dilate</strong> (<em>int</em><em> or </em><em>tuple of int</em><em>, </em><em>default</em><em> (</em><em>1</em><em>, </em><em>1</em><em>, </em><em>1</em><em>)</em>) – Recurrent convolution dilate.</p></li> |
| <li><p><strong>i2h_weight_initializer</strong> (<em>str</em><em> or </em><a class="reference internal" href="../../initializer/index.html#mxnet.initializer.Initializer" title="mxnet.initializer.Initializer"><em>Initializer</em></a>) – Initializer for the input weights matrix, used for the input convolutions.</p></li> |
| <li><p><strong>h2h_weight_initializer</strong> (<em>str</em><em> or </em><a class="reference internal" href="../../initializer/index.html#mxnet.initializer.Initializer" title="mxnet.initializer.Initializer"><em>Initializer</em></a>) – Initializer for the recurrent weights matrix, used for the input convolutions.</p></li> |
| <li><p><strong>i2h_bias_initializer</strong> (<em>str</em><em> or </em><a class="reference internal" href="../../initializer/index.html#mxnet.initializer.Initializer" title="mxnet.initializer.Initializer"><em>Initializer</em></a><em>, </em><em>default zeros</em>) – Initializer for the input convolution bias vectors.</p></li> |
| <li><p><strong>h2h_bias_initializer</strong> (<em>str</em><em> or </em><a class="reference internal" href="../../initializer/index.html#mxnet.initializer.Initializer" title="mxnet.initializer.Initializer"><em>Initializer</em></a><em>, </em><em>default zeros</em>) – Initializer for the recurrent convolution bias vectors.</p></li> |
| <li><p><strong>conv_layout</strong> (<em>str</em><em>, </em><em>default 'NCDHW'</em>) – Layout for all convolution inputs, outputs and weights. Options are ‘NCDHW’ and ‘NDHWC’.</p></li> |
| <li><p><strong>activation</strong> (<em>str</em><em> or </em><a class="reference internal" href="../block.html#mxnet.gluon.Block" title="mxnet.gluon.Block"><em>gluon.Block</em></a><em>, </em><em>default 'tanh'</em>) – Type of activation function used in c^prime_t. |
| If argument type is string, it’s equivalent to nn.Activation(act_type=str). See |
| <a class="reference internal" href="../../legacy/ndarray/ndarray.html#mxnet.ndarray.Activation" title="mxnet.ndarray.Activation"><code class="xref py py-func docutils literal notranslate"><span class="pre">Activation()</span></code></a> for available choices. |
| Alternatively, other activation blocks such as nn.LeakyReLU can be used.</p></li> |
| </ul> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="class"> |
| <dt id="mxnet.gluon.rnn.Conv3DRNNCell"> |
| <em class="property">class </em><code class="sig-name descname">Conv3DRNNCell</code><span class="sig-paren">(</span><em class="sig-param">input_shape</em>, <em class="sig-param">hidden_channels</em>, <em class="sig-param">i2h_kernel</em>, <em class="sig-param">h2h_kernel</em>, <em class="sig-param">i2h_pad=(0</em>, <em class="sig-param">0</em>, <em class="sig-param">0)</em>, <em class="sig-param">i2h_dilate=(1</em>, <em class="sig-param">1</em>, <em class="sig-param">1)</em>, <em class="sig-param">h2h_dilate=(1</em>, <em class="sig-param">1</em>, <em class="sig-param">1)</em>, <em class="sig-param">i2h_weight_initializer=None</em>, <em class="sig-param">h2h_weight_initializer=None</em>, <em class="sig-param">i2h_bias_initializer='zeros'</em>, <em class="sig-param">h2h_bias_initializer='zeros'</em>, <em class="sig-param">conv_layout='NCDHW'</em>, <em class="sig-param">activation='tanh'</em><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/mxnet/gluon/rnn/conv_rnn_cell.html#Conv3DRNNCell"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.gluon.rnn.Conv3DRNNCell" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">mxnet.gluon.rnn.conv_rnn_cell._ConvRNNCell</span></code></p> |
| <p>3D Convolutional RNN cells</p> |
| <div class="math notranslate nohighlight"> |
| \[h_t = tanh(W_i \ast x_t + R_i \ast h_{t-1} + b_i)\]</div> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>input_shape</strong> (<em>tuple of int</em>) – Input tensor shape at each time step for each sample, excluding dimension of the batch size |
| and sequence length. Must be consistent with <cite>conv_layout</cite>. |
| For example, for layout ‘NCDHW’ the shape should be (C, D, H, W).</p></li> |
| <li><p><strong>hidden_channels</strong> (<em>int</em>) – Number of output channels.</p></li> |
| <li><p><strong>i2h_kernel</strong> (<em>int</em><em> or </em><em>tuple of int</em>) – Input convolution kernel sizes.</p></li> |
| <li><p><strong>h2h_kernel</strong> (<em>int</em><em> or </em><em>tuple of int</em>) – Recurrent convolution kernel sizes. Only odd-numbered sizes are supported.</p></li> |
| <li><p><strong>i2h_pad</strong> (<em>int</em><em> or </em><em>tuple of int</em><em>, </em><em>default</em><em> (</em><em>0</em><em>, </em><em>0</em><em>, </em><em>0</em><em>)</em>) – Pad for input convolution.</p></li> |
| <li><p><strong>i2h_dilate</strong> (<em>int</em><em> or </em><em>tuple of int</em><em>, </em><em>default</em><em> (</em><em>1</em><em>, </em><em>1</em><em>, </em><em>1</em><em>)</em>) – Input convolution dilate.</p></li> |
| <li><p><strong>h2h_dilate</strong> (<em>int</em><em> or </em><em>tuple of int</em><em>, </em><em>default</em><em> (</em><em>1</em><em>, </em><em>1</em><em>, </em><em>1</em><em>)</em>) – Recurrent convolution dilate.</p></li> |
| <li><p><strong>i2h_weight_initializer</strong> (<em>str</em><em> or </em><a class="reference internal" href="../../initializer/index.html#mxnet.initializer.Initializer" title="mxnet.initializer.Initializer"><em>Initializer</em></a>) – Initializer for the input weights matrix, used for the input convolutions.</p></li> |
| <li><p><strong>h2h_weight_initializer</strong> (<em>str</em><em> or </em><a class="reference internal" href="../../initializer/index.html#mxnet.initializer.Initializer" title="mxnet.initializer.Initializer"><em>Initializer</em></a>) – Initializer for the recurrent weights matrix, used for the input convolutions.</p></li> |
| <li><p><strong>i2h_bias_initializer</strong> (<em>str</em><em> or </em><a class="reference internal" href="../../initializer/index.html#mxnet.initializer.Initializer" title="mxnet.initializer.Initializer"><em>Initializer</em></a><em>, </em><em>default zeros</em>) – Initializer for the input convolution bias vectors.</p></li> |
| <li><p><strong>h2h_bias_initializer</strong> (<em>str</em><em> or </em><a class="reference internal" href="../../initializer/index.html#mxnet.initializer.Initializer" title="mxnet.initializer.Initializer"><em>Initializer</em></a><em>, </em><em>default zeros</em>) – Initializer for the recurrent convolution bias vectors.</p></li> |
| <li><p><strong>conv_layout</strong> (<em>str</em><em>, </em><em>default 'NCDHW'</em>) – Layout for all convolution inputs, outputs and weights. Options are ‘NCDHW’ and ‘NDHWC’.</p></li> |
| <li><p><strong>activation</strong> (<em>str</em><em> or </em><a class="reference internal" href="../block.html#mxnet.gluon.Block" title="mxnet.gluon.Block"><em>gluon.Block</em></a><em>, </em><em>default 'tanh'</em>) – Type of activation function. |
| If argument type is string, it’s equivalent to nn.Activation(act_type=str). See |
| <a class="reference internal" href="../../legacy/ndarray/ndarray.html#mxnet.ndarray.Activation" title="mxnet.ndarray.Activation"><code class="xref py py-func docutils literal notranslate"><span class="pre">Activation()</span></code></a> for available choices. |
| Alternatively, other activation blocks such as nn.LeakyReLU can be used.</p></li> |
| </ul> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="class"> |
| <dt id="mxnet.gluon.rnn.DropoutCell"> |
| <em class="property">class </em><code class="sig-name descname">DropoutCell</code><span class="sig-paren">(</span><em class="sig-param">rate</em>, <em class="sig-param">axes=()</em><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/mxnet/gluon/rnn/rnn_cell.html#DropoutCell"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.gluon.rnn.DropoutCell" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">mxnet.gluon.rnn.rnn_cell.HybridRecurrentCell</span></code></p> |
| <p>Applies dropout on input.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>rate</strong> (<em>float</em>) – Percentage of elements to drop out, which |
| is 1 - percentage to retain.</p></li> |
| <li><p><strong>axes</strong> (<em>tuple of int</em><em>, </em><em>default</em><em> (</em><em>)</em>) – The axes on which dropout mask is shared. If empty, regular dropout is applied.</p></li> |
| </ul> |
| </dd> |
| </dl> |
| <p><strong>Methods</strong></p> |
| <table class="longtable docutils align-default"> |
| <colgroup> |
| <col style="width: 10%" /> |
| <col style="width: 90%" /> |
| </colgroup> |
| <tbody> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.DropoutCell.apply" title="mxnet.gluon.rnn.DropoutCell.apply"><code class="xref py py-obj docutils literal notranslate"><span class="pre">apply</span></code></a>(fn)</p></td> |
| <td><p>Applies <code class="docutils literal notranslate"><span class="pre">fn</span></code> recursively to every child block as well as self.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.DropoutCell.begin_state" title="mxnet.gluon.rnn.DropoutCell.begin_state"><code class="xref py py-obj docutils literal notranslate"><span class="pre">begin_state</span></code></a>([batch_size, func])</p></td> |
| <td><p>Initial state for this cell.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.DropoutCell.cast" title="mxnet.gluon.rnn.DropoutCell.cast"><code class="xref py py-obj docutils literal notranslate"><span class="pre">cast</span></code></a>(dtype)</p></td> |
| <td><p>Cast this Block to use another data type.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.DropoutCell.collect_params" title="mxnet.gluon.rnn.DropoutCell.collect_params"><code class="xref py py-obj docutils literal notranslate"><span class="pre">collect_params</span></code></a>([select])</p></td> |
| <td><p>Returns a <code class="xref py py-class docutils literal notranslate"><span class="pre">Dict</span></code> containing this <code class="xref py py-class docutils literal notranslate"><span class="pre">Block</span></code> and all of its children’s Parameters(default), also can returns the select <code class="xref py py-class docutils literal notranslate"><span class="pre">Dict</span></code> which match some given regular expressions.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.DropoutCell.export" title="mxnet.gluon.rnn.DropoutCell.export"><code class="xref py py-obj docutils literal notranslate"><span class="pre">export</span></code></a>(path[, epoch, remove_amp_cast])</p></td> |
| <td><p>Export HybridBlock to json format that can be loaded by <cite>gluon.SymbolBlock.imports</cite> or the C++ interface.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.DropoutCell.forward" title="mxnet.gluon.rnn.DropoutCell.forward"><code class="xref py py-obj docutils literal notranslate"><span class="pre">forward</span></code></a>(inputs, states)</p></td> |
| <td><p>Unrolls the recurrent cell for one time step.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.DropoutCell.hybridize" title="mxnet.gluon.rnn.DropoutCell.hybridize"><code class="xref py py-obj docutils literal notranslate"><span class="pre">hybridize</span></code></a>([active])</p></td> |
| <td><p>Please refer description of HybridBlock hybridize().</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.DropoutCell.infer_shape" title="mxnet.gluon.rnn.DropoutCell.infer_shape"><code class="xref py py-obj docutils literal notranslate"><span class="pre">infer_shape</span></code></a>(*args)</p></td> |
| <td><p>Infers shape of Parameters from inputs.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.DropoutCell.infer_type" title="mxnet.gluon.rnn.DropoutCell.infer_type"><code class="xref py py-obj docutils literal notranslate"><span class="pre">infer_type</span></code></a>(*args)</p></td> |
| <td><p>Infers data type of Parameters from inputs.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.DropoutCell.initialize" title="mxnet.gluon.rnn.DropoutCell.initialize"><code class="xref py py-obj docutils literal notranslate"><span class="pre">initialize</span></code></a>([init, device, verbose, force_reinit])</p></td> |
| <td><p>Initializes <code class="xref py py-class docutils literal notranslate"><span class="pre">Parameter</span></code> s of this <code class="xref py py-class docutils literal notranslate"><span class="pre">Block</span></code> and its children.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.DropoutCell.load" title="mxnet.gluon.rnn.DropoutCell.load"><code class="xref py py-obj docutils literal notranslate"><span class="pre">load</span></code></a>(prefix)</p></td> |
| <td><p>Load a model saved using the <cite>save</cite> API</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.DropoutCell.load_dict" title="mxnet.gluon.rnn.DropoutCell.load_dict"><code class="xref py py-obj docutils literal notranslate"><span class="pre">load_dict</span></code></a>(param_dict[, device, …])</p></td> |
| <td><p>Load parameters from dict</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.DropoutCell.load_parameters" title="mxnet.gluon.rnn.DropoutCell.load_parameters"><code class="xref py py-obj docutils literal notranslate"><span class="pre">load_parameters</span></code></a>(filename[, device, …])</p></td> |
| <td><p>Load parameters from file previously saved by <cite>save_parameters</cite>.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.DropoutCell.optimize_for" title="mxnet.gluon.rnn.DropoutCell.optimize_for"><code class="xref py py-obj docutils literal notranslate"><span class="pre">optimize_for</span></code></a>(x, *args[, backend, clear, …])</p></td> |
| <td><p>Partitions the current HybridBlock and optimizes it for a given backend without executing a forward pass.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.DropoutCell.register_child" title="mxnet.gluon.rnn.DropoutCell.register_child"><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_child</span></code></a>(block[, name])</p></td> |
| <td><p>Registers block as a child of self.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.DropoutCell.register_forward_hook" title="mxnet.gluon.rnn.DropoutCell.register_forward_hook"><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_forward_hook</span></code></a>(hook)</p></td> |
| <td><p>Registers a forward hook on the block.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.DropoutCell.register_forward_pre_hook" title="mxnet.gluon.rnn.DropoutCell.register_forward_pre_hook"><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_forward_pre_hook</span></code></a>(hook)</p></td> |
| <td><p>Registers a forward pre-hook on the block.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.DropoutCell.register_op_hook" title="mxnet.gluon.rnn.DropoutCell.register_op_hook"><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_op_hook</span></code></a>(callback[, monitor_all])</p></td> |
| <td><p>Install callback monitor.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.DropoutCell.reset" title="mxnet.gluon.rnn.DropoutCell.reset"><code class="xref py py-obj docutils literal notranslate"><span class="pre">reset</span></code></a>()</p></td> |
| <td><p>Reset before re-using the cell for another graph.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.DropoutCell.reset_ctx" title="mxnet.gluon.rnn.DropoutCell.reset_ctx"><code class="xref py py-obj docutils literal notranslate"><span class="pre">reset_ctx</span></code></a>(ctx)</p></td> |
| <td><p>This function has been deprecated.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.DropoutCell.reset_device" title="mxnet.gluon.rnn.DropoutCell.reset_device"><code class="xref py py-obj docutils literal notranslate"><span class="pre">reset_device</span></code></a>(device)</p></td> |
| <td><p>Re-assign all Parameters to other devices.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.DropoutCell.save" title="mxnet.gluon.rnn.DropoutCell.save"><code class="xref py py-obj docutils literal notranslate"><span class="pre">save</span></code></a>(prefix)</p></td> |
| <td><p>Save the model architecture and parameters to load again later</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.DropoutCell.save_parameters" title="mxnet.gluon.rnn.DropoutCell.save_parameters"><code class="xref py py-obj docutils literal notranslate"><span class="pre">save_parameters</span></code></a>(filename[, deduplicate])</p></td> |
| <td><p>Save parameters to file.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.DropoutCell.setattr" title="mxnet.gluon.rnn.DropoutCell.setattr"><code class="xref py py-obj docutils literal notranslate"><span class="pre">setattr</span></code></a>(name, value)</p></td> |
| <td><p>Set an attribute to a new value for all Parameters.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.DropoutCell.share_parameters" title="mxnet.gluon.rnn.DropoutCell.share_parameters"><code class="xref py py-obj docutils literal notranslate"><span class="pre">share_parameters</span></code></a>(shared)</p></td> |
| <td><p>Share parameters recursively inside the model.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.DropoutCell.state_info" title="mxnet.gluon.rnn.DropoutCell.state_info"><code class="xref py py-obj docutils literal notranslate"><span class="pre">state_info</span></code></a>([batch_size])</p></td> |
| <td><p>shape and layout information of states</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.DropoutCell.summary" title="mxnet.gluon.rnn.DropoutCell.summary"><code class="xref py py-obj docutils literal notranslate"><span class="pre">summary</span></code></a>(*inputs)</p></td> |
| <td><p>Print the summary of the model’s output and parameters.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.DropoutCell.unroll" title="mxnet.gluon.rnn.DropoutCell.unroll"><code class="xref py py-obj docutils literal notranslate"><span class="pre">unroll</span></code></a>(length, inputs[, begin_state, …])</p></td> |
| <td><p>Unrolls an RNN cell across time steps.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.DropoutCell.zero_grad" title="mxnet.gluon.rnn.DropoutCell.zero_grad"><code class="xref py py-obj docutils literal notranslate"><span class="pre">zero_grad</span></code></a>()</p></td> |
| <td><p>Sets all Parameters’ gradient buffer to 0.</p></td> |
| </tr> |
| </tbody> |
| </table> |
| <p><strong>Attributes</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.rnn.DropoutCell.params" title="mxnet.gluon.rnn.DropoutCell.params"><code class="xref py py-obj docutils literal notranslate"><span class="pre">params</span></code></a></p></td> |
| <td><p>Returns this <code class="xref py py-class docutils literal notranslate"><span class="pre">Block</span></code>’s parameter dictionary (does not include its children’s parameters).</p></td> |
| </tr> |
| </tbody> |
| </table> |
| <dl class="simple"> |
| <dt>Inputs:</dt><dd><ul class="simple"> |
| <li><p><strong>data</strong>: input tensor with shape <cite>(batch_size, size)</cite>.</p></li> |
| <li><p><strong>states</strong>: a list of recurrent state tensors.</p></li> |
| </ul> |
| </dd> |
| <dt>Outputs:</dt><dd><ul class="simple"> |
| <li><p><strong>out</strong>: output tensor with shape <cite>(batch_size, size)</cite>.</p></li> |
| <li><p><strong>next_states</strong>: returns input <cite>states</cite> directly.</p></li> |
| </ul> |
| </dd> |
| </dl> |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.DropoutCell.apply"> |
| <code class="sig-name descname">apply</code><span class="sig-paren">(</span><em class="sig-param">fn</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.rnn.DropoutCell.apply" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Applies <code class="docutils literal notranslate"><span class="pre">fn</span></code> recursively to every child block as well as self.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><p><strong>fn</strong> (<em>callable</em>) – Function to be applied to each submodule, of form <cite>fn(block)</cite>.</p> |
| </dd> |
| <dt class="field-even">Returns</dt> |
| <dd class="field-even"><p></p> |
| </dd> |
| <dt class="field-odd">Return type</dt> |
| <dd class="field-odd"><p>this block</p> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.DropoutCell.begin_state"> |
| <code class="sig-name descname">begin_state</code><span class="sig-paren">(</span><em class="sig-param">batch_size=0</em>, <em class="sig-param">func=<function zeros></em>, <em class="sig-param">**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.rnn.DropoutCell.begin_state" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Initial state for this cell.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>func</strong> (<em>callable</em><em>, </em><em>default symbol.zeros</em>) – <p>Function for creating initial state.</p> |
| <p>For Symbol API, func can be <cite>symbol.zeros</cite>, <cite>symbol.uniform</cite>, |
| <cite>symbol.var etc</cite>. Use <cite>symbol.var</cite> if you want to directly |
| feed input as states.</p> |
| <p>For NDArray API, func can be <cite>ndarray.zeros</cite>, <cite>ndarray.ones</cite>, etc.</p> |
| </p></li> |
| <li><p><strong>batch_size</strong> (<em>int</em><em>, </em><em>default 0</em>) – Only required for NDArray API. Size of the batch (‘N’ in layout) |
| dimension of input.</p></li> |
| <li><p><strong>**kwargs</strong> – Additional keyword arguments passed to func. For example |
| <cite>mean</cite>, <cite>std</cite>, <cite>dtype</cite>, etc.</p></li> |
| </ul> |
| </dd> |
| <dt class="field-even">Returns</dt> |
| <dd class="field-even"><p><strong>states</strong> – Starting states for the first RNN step.</p> |
| </dd> |
| <dt class="field-odd">Return type</dt> |
| <dd class="field-odd"><p>nested list of Symbol</p> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.DropoutCell.cast"> |
| <code class="sig-name descname">cast</code><span class="sig-paren">(</span><em class="sig-param">dtype</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.rnn.DropoutCell.cast" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Cast this Block to use another data type.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><p><strong>dtype</strong> (<em>str</em><em> or </em><em>numpy.dtype</em>) – The new data type.</p> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.DropoutCell.collect_params"> |
| <code class="sig-name descname">collect_params</code><span class="sig-paren">(</span><em class="sig-param">select=None</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.rnn.DropoutCell.collect_params" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Returns a <code class="xref py py-class docutils literal notranslate"><span class="pre">Dict</span></code> containing this <code class="xref py py-class docutils literal notranslate"><span class="pre">Block</span></code> and all of its |
| children’s Parameters(default), also can returns the select <code class="xref py py-class docutils literal notranslate"><span class="pre">Dict</span></code> |
| which match some given regular expressions.</p> |
| <p>For example, collect the specified parameters in [‘conv1.weight’, ‘conv1.bias’, ‘fc.weight’, |
| ‘fc.bias’]:</p> |
| <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">model</span><span class="o">.</span><span class="n">collect_params</span><span class="p">(</span><span class="s1">'conv1.weight|conv1.bias|fc.weight|fc.bias'</span><span class="p">)</span> |
| </pre></div> |
| </div> |
| <p>or collect all parameters whose names end with ‘weight’ or ‘bias’, this can be done |
| using regular expressions:</p> |
| <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">model</span><span class="o">.</span><span class="n">collect_params</span><span class="p">(</span><span class="s1">'.*weight|.*bias'</span><span class="p">)</span> |
| </pre></div> |
| </div> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><p><strong>select</strong> (<em>str</em>) – regular expressions</p> |
| </dd> |
| <dt class="field-even">Returns</dt> |
| <dd class="field-even"><p></p> |
| </dd> |
| <dt class="field-odd">Return type</dt> |
| <dd class="field-odd"><p>The selected <code class="xref py py-class docutils literal notranslate"><span class="pre">Dict</span></code></p> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.DropoutCell.export"> |
| <code class="sig-name descname">export</code><span class="sig-paren">(</span><em class="sig-param">path</em>, <em class="sig-param">epoch=0</em>, <em class="sig-param">remove_amp_cast=True</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.rnn.DropoutCell.export" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Export HybridBlock to json format that can be loaded by |
| <cite>gluon.SymbolBlock.imports</cite> or the C++ interface.</p> |
| <div class="admonition note"> |
| <p class="admonition-title">Note</p> |
| <p>When there are only one input, it will have name <cite>data</cite>. When there |
| Are more than one inputs, they will be named as <cite>data0</cite>, <cite>data1</cite>, etc.</p> |
| </div> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>path</strong> (<em>str</em><em> or </em><em>None</em>) – Path to save model. Two files <cite>path-symbol.json</cite> and <cite>path-xxxx.params</cite> |
| will be created, where xxxx is the 4 digits epoch number. |
| If None, do not export to file but return Python Symbol object and |
| corresponding dictionary of parameters.</p></li> |
| <li><p><strong>epoch</strong> (<em>int</em>) – Epoch number of saved model.</p></li> |
| <li><p><strong>remove_amp_cast</strong> (<em>bool</em><em>, </em><em>optional</em>) – Whether to remove the amp_cast and amp_multicast operators, before saving the model.</p></li> |
| </ul> |
| </dd> |
| <dt class="field-even">Returns</dt> |
| <dd class="field-even"><p><ul class="simple"> |
| <li><p><strong>symbol_filename</strong> (<em>str</em>) – Filename to which model symbols were saved, including <cite>path</cite> prefix.</p></li> |
| <li><p><strong>params_filename</strong> (<em>str</em>) – Filename to which model parameters were saved, including <cite>path</cite> prefix.</p></li> |
| </ul> |
| </p> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.DropoutCell.forward"> |
| <code class="sig-name descname">forward</code><span class="sig-paren">(</span><em class="sig-param">inputs</em>, <em class="sig-param">states</em><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/mxnet/gluon/rnn/rnn_cell.html#DropoutCell.forward"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.gluon.rnn.DropoutCell.forward" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Unrolls the recurrent cell for one time step.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>inputs</strong> (<em>sym.Variable</em>) – Input symbol, 2D, of shape (batch_size * num_units).</p></li> |
| <li><p><strong>states</strong> (<em>list of sym.Variable</em>) – RNN state from previous step or the output of begin_state().</p></li> |
| </ul> |
| </dd> |
| <dt class="field-even">Returns</dt> |
| <dd class="field-even"><p><ul class="simple"> |
| <li><p><strong>output</strong> (<em>Symbol</em>) – Symbol corresponding to the output from the RNN when unrolling |
| for a single time step.</p></li> |
| <li><p><strong>states</strong> (<em>list of Symbol</em>) – The new state of this RNN after this unrolling. |
| The type of this symbol is same as the output of <cite>begin_state()</cite>. |
| This can be used as an input state to the next time step |
| of this RNN.</p></li> |
| </ul> |
| </p> |
| </dd> |
| </dl> |
| <div class="admonition seealso"> |
| <p class="admonition-title">See also</p> |
| <dl class="simple"> |
| <dt><a class="reference internal" href="#mxnet.gluon.rnn.DropoutCell.begin_state" title="mxnet.gluon.rnn.DropoutCell.begin_state"><code class="xref py py-meth docutils literal notranslate"><span class="pre">begin_state()</span></code></a></dt><dd><p>This function can provide the states for the first time step.</p> |
| </dd> |
| <dt><a class="reference internal" href="#mxnet.gluon.rnn.DropoutCell.unroll" title="mxnet.gluon.rnn.DropoutCell.unroll"><code class="xref py py-meth docutils literal notranslate"><span class="pre">unroll()</span></code></a></dt><dd><p>This function unrolls an RNN for a given number of (>=1) time steps.</p> |
| </dd> |
| </dl> |
| </div> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.DropoutCell.hybridize"> |
| <code class="sig-name descname">hybridize</code><span class="sig-paren">(</span><em class="sig-param">active=True</em>, <em class="sig-param">**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.rnn.DropoutCell.hybridize" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Please refer description of HybridBlock hybridize().</p> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.DropoutCell.infer_shape"> |
| <code class="sig-name descname">infer_shape</code><span class="sig-paren">(</span><em class="sig-param">*args</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.rnn.DropoutCell.infer_shape" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Infers shape of Parameters from inputs.</p> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.DropoutCell.infer_type"> |
| <code class="sig-name descname">infer_type</code><span class="sig-paren">(</span><em class="sig-param">*args</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.rnn.DropoutCell.infer_type" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Infers data type of Parameters from inputs.</p> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.DropoutCell.initialize"> |
| <code class="sig-name descname">initialize</code><span class="sig-paren">(</span><em class="sig-param">init=<mxnet.initializer.Uniform object></em>, <em class="sig-param">device=None</em>, <em class="sig-param">verbose=False</em>, <em class="sig-param">force_reinit=False</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.rnn.DropoutCell.initialize" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Initializes <code class="xref py py-class docutils literal notranslate"><span class="pre">Parameter</span></code> s of this <code class="xref py py-class docutils literal notranslate"><span class="pre">Block</span></code> and its children.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>init</strong> (<a class="reference internal" href="../../initializer/index.html#mxnet.initializer.Initializer" title="mxnet.initializer.Initializer"><em>Initializer</em></a>) – Global default Initializer to be used when <code class="xref py py-meth docutils literal notranslate"><span class="pre">Parameter.init()</span></code> is <code class="docutils literal notranslate"><span class="pre">None</span></code>. |
| Otherwise, <code class="xref py py-meth docutils literal notranslate"><span class="pre">Parameter.init()</span></code> takes precedence.</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>) – Keeps a copy of Parameters on one or many device(s).</p></li> |
| <li><p><strong>verbose</strong> (<em>bool</em><em>, </em><em>default False</em>) – Whether to verbosely print out details on initialization.</p></li> |
| <li><p><strong>force_reinit</strong> (<em>bool</em><em>, </em><em>default False</em>) – Whether to force re-initialization if parameter is already initialized.</p></li> |
| </ul> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.DropoutCell.load"> |
| <code class="sig-name descname">load</code><span class="sig-paren">(</span><em class="sig-param">prefix</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.rnn.DropoutCell.load" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Load a model saved using the <cite>save</cite> API</p> |
| <p>Reconfigures a model using the saved configuration. This function |
| does not regenerate the model architecture. It resets each Block’s |
| parameter UUIDs as they were when saved in order to match the names of the |
| saved parameters.</p> |
| <p>This function assumes the Blocks in the model were created in the same |
| order they were when the model was saved. This is because each Block is |
| uniquely identified by Block class name and a unique ID in order (since |
| its an OrderedDict) and uses the unique ID to denote that specific Block.</p> |
| <p>Assumes that the model is created in an identical order every time. |
| If the model is not able to be recreated deterministically do not |
| use this set of APIs to save/load your model.</p> |
| <p>For HybridBlocks, the cached_graph (Symbol & inputs) and settings are |
| restored if it had been hybridized before saving.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><p><strong>prefix</strong> (<em>str</em>) – The prefix to use in filenames for loading this model: |
| <prefix>-model.json and <prefix>-model.params</p> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.DropoutCell.load_dict"> |
| <code class="sig-name descname">load_dict</code><span class="sig-paren">(</span><em class="sig-param">param_dict</em>, <em class="sig-param">device=None</em>, <em class="sig-param">allow_missing=False</em>, <em class="sig-param">ignore_extra=False</em>, <em class="sig-param">cast_dtype=False</em>, <em class="sig-param">dtype_source='current'</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.rnn.DropoutCell.load_dict" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Load parameters from dict</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>param_dict</strong> (<em>dict</em>) – Dictionary containing model 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>, </em><em>optional</em>) – Device context on which the memory is allocated. Default is |
| <cite>mxnet.device.current_device()</cite>.</p></li> |
| <li><p><strong>allow_missing</strong> (<em>bool</em><em>, </em><em>default False</em>) – Whether to silently skip loading parameters not represented in the file.</p></li> |
| <li><p><strong>ignore_extra</strong> (<em>bool</em><em>, </em><em>default False</em>) – Whether to silently ignore parameters from the file that are not |
| present in this dict.</p></li> |
| <li><p><strong>cast_dtype</strong> (<em>bool</em><em>, </em><em>default False</em>) – Cast the data type of the NDArray loaded from the checkpoint to the dtype |
| provided by the Parameter if any</p></li> |
| <li><p><strong>dtype_source</strong> (<em>str</em><em>, </em><em>default 'current'</em>) – must be in {‘current’, ‘saved’} |
| Only valid if cast_dtype=True, specify the source of the dtype for casting |
| the parameters</p></li> |
| </ul> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.DropoutCell.load_parameters"> |
| <code class="sig-name descname">load_parameters</code><span class="sig-paren">(</span><em class="sig-param">filename</em>, <em class="sig-param">device=None</em>, <em class="sig-param">allow_missing=False</em>, <em class="sig-param">ignore_extra=False</em>, <em class="sig-param">cast_dtype=False</em>, <em class="sig-param">dtype_source='current'</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.rnn.DropoutCell.load_parameters" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Load parameters from file previously saved by <cite>save_parameters</cite>.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>filename</strong> (<em>str</em>) – Path to parameter file.</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><em>, </em><em>default cpu</em><em>(</em><em>)</em>) – Device(s) to initialize loaded parameters on.</p></li> |
| <li><p><strong>allow_missing</strong> (<em>bool</em><em>, </em><em>default False</em>) – Whether to silently skip loading parameters not represents in the file.</p></li> |
| <li><p><strong>ignore_extra</strong> (<em>bool</em><em>, </em><em>default False</em>) – Whether to silently ignore parameters from the file that are not |
| present in this Block.</p></li> |
| <li><p><strong>cast_dtype</strong> (<em>bool</em><em>, </em><em>default False</em>) – Cast the data type of the NDArray loaded from the checkpoint to the dtype |
| provided by the Parameter if any.</p></li> |
| <li><p><strong>dtype_source</strong> (<em>str</em><em>, </em><em>default 'current'</em>) – must be in {‘current’, ‘saved’} |
| Only valid if cast_dtype=True, specify the source of the dtype for casting |
| the parameters</p></li> |
| </ul> |
| </dd> |
| </dl> |
| <p class="rubric">References</p> |
| <p><a class="reference external" href="https://mxnet.apache.org/api/python/docs/tutorials/packages/gluon/blocks/save_load_params.html">Saving and Loading Gluon Models</a></p> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.DropoutCell.optimize_for"> |
| <code class="sig-name descname">optimize_for</code><span class="sig-paren">(</span><em class="sig-param">x</em>, <em class="sig-param">*args</em>, <em class="sig-param">backend=None</em>, <em class="sig-param">clear=False</em>, <em class="sig-param">partition_if_dynamic=True</em>, <em class="sig-param">static_alloc=False</em>, <em class="sig-param">static_shape=False</em>, <em class="sig-param">inline_limit=2</em>, <em class="sig-param">forward_bulk_size=None</em>, <em class="sig-param">backward_bulk_size=None</em>, <em class="sig-param">**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.rnn.DropoutCell.optimize_for" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Partitions the current HybridBlock and optimizes it for a given backend |
| without executing a forward pass. Modifies the HybridBlock in-place.</p> |
| <p>Immediately partitions a HybridBlock using the specified backend. Combines |
| the work done in the hybridize API with part of the work done in the forward |
| pass without calling the CachedOp. Can be used in place of hybridize, |
| afterwards <cite>export</cite> can be called or inference can be run. See README.md in |
| example/extensions/lib_subgraph/README.md for more details.</p> |
| <p class="rubric">Examples</p> |
| <p># partition and then export to file |
| block.optimize_for(x, backend=’myPart’) |
| block.export(‘partitioned’)</p> |
| <p># partition and then run inference |
| block.optimize_for(x, backend=’myPart’) |
| block(x)</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>x</strong> (<a class="reference internal" href="../../legacy/ndarray/ndarray.html#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – first input to model</p></li> |
| <li><p><strong>*args</strong> (<a class="reference internal" href="../../legacy/ndarray/ndarray.html#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – other inputs to model</p></li> |
| <li><p><strong>backend</strong> (<em>str</em>) – The name of backend, as registered in <cite>SubgraphBackendRegistry</cite>, default None</p></li> |
| <li><p><strong>backend_opts</strong> (<em>dict of user-specified options to pass to the backend for partitioning</em><em>, </em><em>optional</em>) – Passed on to <cite>PrePartition</cite> and <cite>PostPartition</cite> functions of <cite>SubgraphProperty</cite></p></li> |
| <li><p><strong>clear</strong> (<em>bool</em><em>, </em><em>default False</em>) – clears any previous optimizations</p></li> |
| <li><p><strong>partition_if_dynamic</strong> (<em>bool</em><em>, </em><em>default False</em>) – whether to partition the graph when dynamic shape op exists</p></li> |
| <li><p><strong>static_alloc</strong> (<em>bool</em><em>, </em><em>default False</em>) – Statically allocate memory to improve speed. Memory usage may increase.</p></li> |
| <li><p><strong>static_shape</strong> (<em>bool</em><em>, </em><em>default False</em>) – Optimize for invariant input shapes between iterations. Must also |
| set static_alloc to True. Change of input shapes is still allowed |
| but slower.</p></li> |
| <li><p><strong>inline_limit</strong> (<em>optional int</em><em>, </em><em>default 2</em>) – Maximum number of operators that can be inlined.</p></li> |
| <li><p><strong>forward_bulk_size</strong> (<em>optional int</em><em>, </em><em>default None</em>) – Segment size of bulk execution during forward pass.</p></li> |
| <li><p><strong>backward_bulk_size</strong> (<em>optional int</em><em>, </em><em>default None</em>) – Segment size of bulk execution during backward pass.</p></li> |
| <li><p><strong>**kwargs</strong> (<em>The backend options</em><em>, </em><em>optional</em>) – Passed on to <cite>PrePartition</cite> and <cite>PostPartition</cite> functions of <cite>SubgraphProperty</cite></p></li> |
| </ul> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.DropoutCell.params"> |
| <em class="property">property </em><code class="sig-name descname">params</code><a class="headerlink" href="#mxnet.gluon.rnn.DropoutCell.params" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Returns this <code class="xref py py-class docutils literal notranslate"><span class="pre">Block</span></code>’s parameter dictionary (does not include its |
| children’s parameters).</p> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.DropoutCell.register_child"> |
| <code class="sig-name descname">register_child</code><span class="sig-paren">(</span><em class="sig-param">block</em>, <em class="sig-param">name=None</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.rnn.DropoutCell.register_child" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Registers block as a child of self. <code class="xref py py-class docutils literal notranslate"><span class="pre">Block</span></code> s assigned to self as |
| attributes will be registered automatically.</p> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.DropoutCell.register_forward_hook"> |
| <code class="sig-name descname">register_forward_hook</code><span class="sig-paren">(</span><em class="sig-param">hook</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.rnn.DropoutCell.register_forward_hook" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Registers a forward hook on the block.</p> |
| <p>The hook function is called immediately after <a class="reference internal" href="#mxnet.gluon.rnn.DropoutCell.forward" title="mxnet.gluon.rnn.DropoutCell.forward"><code class="xref py py-func docutils literal notranslate"><span class="pre">forward()</span></code></a>. |
| It should not modify the input or output.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><p><strong>hook</strong> (<em>callable</em>) – The forward hook function of form <cite>hook(block, input, output) -> None</cite>.</p> |
| </dd> |
| <dt class="field-even">Returns</dt> |
| <dd class="field-even"><p></p> |
| </dd> |
| <dt class="field-odd">Return type</dt> |
| <dd class="field-odd"><p><code class="xref py py-class docutils literal notranslate"><span class="pre">mxnet.gluon.utils.HookHandle</span></code></p> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.DropoutCell.register_forward_pre_hook"> |
| <code class="sig-name descname">register_forward_pre_hook</code><span class="sig-paren">(</span><em class="sig-param">hook</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.rnn.DropoutCell.register_forward_pre_hook" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Registers a forward pre-hook on the block.</p> |
| <p>The hook function is called immediately before <a class="reference internal" href="#mxnet.gluon.rnn.DropoutCell.forward" title="mxnet.gluon.rnn.DropoutCell.forward"><code class="xref py py-func docutils literal notranslate"><span class="pre">forward()</span></code></a>. |
| It should not modify the input or output.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><p><strong>hook</strong> (<em>callable</em>) – The forward hook function of form <cite>hook(block, input) -> None</cite>.</p> |
| </dd> |
| <dt class="field-even">Returns</dt> |
| <dd class="field-even"><p></p> |
| </dd> |
| <dt class="field-odd">Return type</dt> |
| <dd class="field-odd"><p><code class="xref py py-class docutils literal notranslate"><span class="pre">mxnet.gluon.utils.HookHandle</span></code></p> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.DropoutCell.register_op_hook"> |
| <code class="sig-name descname">register_op_hook</code><span class="sig-paren">(</span><em class="sig-param">callback</em>, <em class="sig-param">monitor_all=False</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.rnn.DropoutCell.register_op_hook" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Install callback monitor.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>callback</strong> (<em>function</em>) – Function called to inspect the values of the intermediate outputs |
| of blocks after hybridization. It takes 3 parameters: |
| name of the tensor being inspected (str) |
| name of the operator producing or consuming that tensor (str) |
| tensor being inspected (NDArray).</p></li> |
| <li><p><strong>monitor_all</strong> (<em>bool</em><em>, </em><em>default False</em>) – If True, monitor both input and output, otherwise monitor output only.</p></li> |
| </ul> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.DropoutCell.reset"> |
| <code class="sig-name descname">reset</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.rnn.DropoutCell.reset" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Reset before re-using the cell for another graph.</p> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.DropoutCell.reset_ctx"> |
| <code class="sig-name descname">reset_ctx</code><span class="sig-paren">(</span><em class="sig-param">ctx</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.rnn.DropoutCell.reset_ctx" title="Permalink to this definition">¶</a></dt> |
| <dd><p>This function has been deprecated. Please refer to <code class="docutils literal notranslate"><span class="pre">Block.reset_device</span></code>.</p> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.DropoutCell.reset_device"> |
| <code class="sig-name descname">reset_device</code><span class="sig-paren">(</span><em class="sig-param">device</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.rnn.DropoutCell.reset_device" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Re-assign all Parameters to other devices.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><p><strong>device</strong> (Device or list of Device, default <code class="xref py py-meth docutils literal notranslate"><span class="pre">device.current_device()</span></code>.) – Assign Parameter to given device. If device is a list of Device, a |
| copy will be made for each device.</p> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.DropoutCell.save"> |
| <code class="sig-name descname">save</code><span class="sig-paren">(</span><em class="sig-param">prefix</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.rnn.DropoutCell.save" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Save the model architecture and parameters to load again later</p> |
| <p>Saves the model architecture as a nested dictionary where each Block |
| in the model is a dictionary and its children are sub-dictionaries.</p> |
| <p>Each Block is uniquely identified by Block class name and a unique ID. |
| We save each Block’s parameter UUID to restore later in order to match |
| the saved parameters.</p> |
| <p>Recursively traverses a Block’s children in order (since its an |
| OrderedDict) and uses the unique ID to denote that specific Block.</p> |
| <p>Assumes that the model is created in an identical order every time. |
| If the model is not able to be recreated deterministically do not |
| use this set of APIs to save/load your model.</p> |
| <p>For HybridBlocks, the cached_graph is saved (Symbol & inputs) if |
| it has already been hybridized.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><p><strong>prefix</strong> (<em>str</em>) – The prefix to use in filenames for saving this model: |
| <prefix>-model.json and <prefix>-model.params</p> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.DropoutCell.save_parameters"> |
| <code class="sig-name descname">save_parameters</code><span class="sig-paren">(</span><em class="sig-param">filename</em>, <em class="sig-param">deduplicate=False</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.rnn.DropoutCell.save_parameters" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Save parameters to file.</p> |
| <p>Saved parameters can only be loaded with <cite>load_parameters</cite>. Note that this |
| method only saves parameters, not model structure. If you want to save |
| model structures, please use <code class="xref py py-meth docutils literal notranslate"><span class="pre">HybridBlock.export()</span></code>.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>filename</strong> (<em>str</em>) – Path to file.</p></li> |
| <li><p><strong>deduplicate</strong> (<em>bool</em><em>, </em><em>default False</em>) – If True, save shared parameters only once. Otherwise, if a Block |
| contains multiple sub-blocks that share parameters, each of the |
| shared parameters will be separately saved for every sub-block.</p></li> |
| </ul> |
| </dd> |
| </dl> |
| <p class="rubric">References</p> |
| <p><a class="reference external" href="https://mxnet.apache.org/api/python/docs/tutorials/packages/gluon/blocks/save_load_params.html">Saving and Loading Gluon Models</a></p> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.DropoutCell.setattr"> |
| <code class="sig-name descname">setattr</code><span class="sig-paren">(</span><em class="sig-param">name</em>, <em class="sig-param">value</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.rnn.DropoutCell.setattr" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Set an attribute to a new value for all Parameters.</p> |
| <p>For example, set grad_req to null if you don’t need gradient w.r.t a |
| model’s Parameters:</p> |
| <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">model</span><span class="o">.</span><span class="n">setattr</span><span class="p">(</span><span class="s1">'grad_req'</span><span class="p">,</span> <span class="s1">'null'</span><span class="p">)</span> |
| </pre></div> |
| </div> |
| <p>or change the learning rate multiplier:</p> |
| <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">model</span><span class="o">.</span><span class="n">setattr</span><span class="p">(</span><span class="s1">'lr_mult'</span><span class="p">,</span> <span class="mf">0.5</span><span class="p">)</span> |
| </pre></div> |
| </div> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>name</strong> (<em>str</em>) – Name of the attribute.</p></li> |
| <li><p><strong>value</strong> (<em>valid type for attribute name</em>) – The new value for the attribute.</p></li> |
| </ul> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.DropoutCell.share_parameters"> |
| <code class="sig-name descname">share_parameters</code><span class="sig-paren">(</span><em class="sig-param">shared</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.rnn.DropoutCell.share_parameters" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Share parameters recursively inside the model.</p> |
| <p>For example, if you want <code class="docutils literal notranslate"><span class="pre">dense1</span></code> to share <code class="docutils literal notranslate"><span class="pre">dense0</span></code>’s weights, you can do:</p> |
| <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">dense0</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Dense</span><span class="p">(</span><span class="mi">20</span><span class="p">)</span> |
| <span class="n">dense1</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Dense</span><span class="p">(</span><span class="mi">20</span><span class="p">)</span> |
| <span class="n">dense1</span><span class="o">.</span><span class="n">share_parameters</span><span class="p">(</span><span class="n">dense0</span><span class="o">.</span><span class="n">collect_params</span><span class="p">())</span> |
| </pre></div> |
| </div> |
| <dl class="simple"> |
| <dt>which equals to</dt><dd><p>dense1.weight = dense0.weight |
| dense1.bias = dense0.bias</p> |
| </dd> |
| </dl> |
| <p>Note that unlike the <cite>load_parameters</cite> or <cite>load_dict</cite> functions, |
| <cite>share_parameters</cite> results in the <cite>Parameter</cite> object being shared (or |
| tied) between the models, whereas <cite>load_parameters</cite> or <cite>load_dict</cite> only |
| set the value of the data dictionary of a model. If you call |
| <cite>load_parameters</cite> or <cite>load_dict</cite> after <cite>share_parameters</cite>, the loaded |
| value will be reflected in all networks that use the shared (or tied) |
| <cite>Parameter</cite> object.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><p><strong>shared</strong> (<em>Dict</em>) – Dict of the shared parameters.</p> |
| </dd> |
| <dt class="field-even">Returns</dt> |
| <dd class="field-even"><p></p> |
| </dd> |
| <dt class="field-odd">Return type</dt> |
| <dd class="field-odd"><p>this block</p> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.DropoutCell.state_info"> |
| <code class="sig-name descname">state_info</code><span class="sig-paren">(</span><em class="sig-param">batch_size=0</em><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/mxnet/gluon/rnn/rnn_cell.html#DropoutCell.state_info"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.gluon.rnn.DropoutCell.state_info" title="Permalink to this definition">¶</a></dt> |
| <dd><p>shape and layout information of states</p> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.DropoutCell.summary"> |
| <code class="sig-name descname">summary</code><span class="sig-paren">(</span><em class="sig-param">*inputs</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.rnn.DropoutCell.summary" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Print the summary of the model’s output and parameters.</p> |
| <p>The network must have been initialized, and must not have been hybridized.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><p><strong>inputs</strong> (<em>object</em>) – Any input that the model supports. For any tensor in the input, only |
| <a class="reference internal" href="../../legacy/ndarray/ndarray.html#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><code class="xref py py-class docutils literal notranslate"><span class="pre">mxnet.ndarray.NDArray</span></code></a> is supported.</p> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.DropoutCell.unroll"> |
| <code class="sig-name descname">unroll</code><span class="sig-paren">(</span><em class="sig-param">length</em>, <em class="sig-param">inputs</em>, <em class="sig-param">begin_state=None</em>, <em class="sig-param">layout='NTC'</em>, <em class="sig-param">merge_outputs=None</em>, <em class="sig-param">valid_length=None</em><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/mxnet/gluon/rnn/rnn_cell.html#DropoutCell.unroll"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.gluon.rnn.DropoutCell.unroll" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Unrolls an RNN cell across time steps.</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>) – Number of steps to unroll.</p></li> |
| <li><p><strong>inputs</strong> (<a class="reference internal" href="../../legacy/symbol/symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a><em>, </em><em>list of Symbol</em><em>, or </em><em>None</em>) – <p>If <cite>inputs</cite> is a single Symbol (usually the output |
| of Embedding symbol), it should have shape |
| (batch_size, length, …) if <cite>layout</cite> is ‘NTC’, |
| or (length, batch_size, …) if <cite>layout</cite> is ‘TNC’.</p> |
| <p>If <cite>inputs</cite> is a list of symbols (usually output of |
| previous unroll), they should all have shape |
| (batch_size, …).</p> |
| </p></li> |
| <li><p><strong>begin_state</strong> (<em>nested list of Symbol</em><em>, </em><em>optional</em>) – Input states created by <cite>begin_state()</cite> |
| or output state of another cell. |
| Created from <cite>begin_state()</cite> if <cite>None</cite>.</p></li> |
| <li><p><strong>layout</strong> (<em>str</em><em>, </em><em>optional</em>) – <cite>layout</cite> of input symbol. Only used if inputs |
| is a single Symbol.</p></li> |
| <li><p><strong>merge_outputs</strong> (<em>bool</em><em>, </em><em>optional</em>) – If <cite>False</cite>, returns outputs as a list of Symbols. |
| If <cite>True</cite>, concatenates output across time steps |
| and returns a single symbol with shape |
| (batch_size, length, …) if layout is ‘NTC’, |
| or (length, batch_size, …) if layout is ‘TNC’. |
| If <cite>None</cite>, output whatever is faster.</p></li> |
| <li><p><strong>valid_length</strong> (<a class="reference internal" href="../../legacy/symbol/symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a><em>, </em><a class="reference internal" href="../../legacy/ndarray/ndarray.html#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a><em> or </em><em>None</em>) – <cite>valid_length</cite> specifies the length of the sequences in the batch without padding. |
| This option is especially useful for building sequence-to-sequence models where |
| the input and output sequences would potentially be padded. |
| If <cite>valid_length</cite> is None, all sequences are assumed to have the same length. |
| If <cite>valid_length</cite> is a Symbol or NDArray, it should have shape (batch_size,). |
| The ith element will be the length of the ith sequence in the batch. |
| The last valid state will be return and the padded outputs will be masked with 0. |
| Note that <cite>valid_length</cite> must be smaller or equal to <cite>length</cite>.</p></li> |
| </ul> |
| </dd> |
| <dt class="field-even">Returns</dt> |
| <dd class="field-even"><p><ul class="simple"> |
| <li><p><strong>outputs</strong> (<em>list of Symbol or Symbol</em>) – Symbol (if <cite>merge_outputs</cite> is True) or list of Symbols |
| (if <cite>merge_outputs</cite> is False) corresponding to the output from |
| the RNN from this unrolling.</p></li> |
| <li><p><strong>states</strong> (<em>list of Symbol</em>) – The new state of this RNN after this unrolling. |
| The type of this symbol is same as the output of <cite>begin_state()</cite>.</p></li> |
| </ul> |
| </p> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.DropoutCell.zero_grad"> |
| <code class="sig-name descname">zero_grad</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.rnn.DropoutCell.zero_grad" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Sets all Parameters’ gradient buffer to 0.</p> |
| </dd></dl> |
| |
| </dd></dl> |
| |
| <dl class="class"> |
| <dt id="mxnet.gluon.rnn.GRU"> |
| <em class="property">class </em><code class="sig-name descname">GRU</code><span class="sig-paren">(</span><em class="sig-param">hidden_size</em>, <em class="sig-param">num_layers=1</em>, <em class="sig-param">layout='TNC'</em>, <em class="sig-param">dropout=0</em>, <em class="sig-param">bidirectional=False</em>, <em class="sig-param">input_size=0</em>, <em class="sig-param">i2h_weight_initializer=None</em>, <em class="sig-param">h2h_weight_initializer=None</em>, <em class="sig-param">i2h_bias_initializer='zeros'</em>, <em class="sig-param">h2h_bias_initializer='zeros'</em>, <em class="sig-param">dtype='float32'</em>, <em class="sig-param">**kwargs</em><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/mxnet/gluon/rnn/rnn_layer.html#GRU"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.gluon.rnn.GRU" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">mxnet.gluon.rnn.rnn_layer._RNNLayer</span></code></p> |
| <p>Applies a multi-layer gated recurrent unit (GRU) RNN to an input sequence. |
| Note: this is an implementation of the cuDNN version of GRUs |
| (slight modification compared to Cho et al. 2014; the reset gate <span class="math notranslate nohighlight">\(r_t\)</span> |
| is applied after matrix multiplication).</p> |
| <p>For each element in the input sequence, each layer computes the following |
| function:</p> |
| <div class="math notranslate nohighlight"> |
| \[\begin{split}\begin{array}{ll} |
| r_t = sigmoid(W_{ir} x_t + b_{ir} + W_{hr} h_{(t-1)} + b_{hr}) \\ |
| i_t = sigmoid(W_{ii} x_t + b_{ii} + W_{hi} h_{(t-1)} + b_{hi}) \\ |
| n_t = \tanh(W_{in} x_t + b_{in} + r_t * (W_{hn} h_{(t-1)} + b_{hn})) \\ |
| h_t = (1 - i_t) * n_t + i_t * h_{(t-1)} \\ |
| \end{array}\end{split}\]</div> |
| <p>where <span class="math notranslate nohighlight">\(h_t\)</span> is the hidden state at time <cite>t</cite>, <span class="math notranslate nohighlight">\(x_t\)</span> is the hidden |
| state of the previous layer at time <cite>t</cite> or <span class="math notranslate nohighlight">\(input_t\)</span> for the first layer, |
| and <span class="math notranslate nohighlight">\(r_t\)</span>, <span class="math notranslate nohighlight">\(i_t\)</span>, <span class="math notranslate nohighlight">\(n_t\)</span> are the reset, input, and new gates, respectively.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>hidden_size</strong> (<em>int</em>) – The number of features in the hidden state h</p></li> |
| <li><p><strong>num_layers</strong> (<em>int</em><em>, </em><em>default 1</em>) – Number of recurrent layers.</p></li> |
| <li><p><strong>layout</strong> (<em>str</em><em>, </em><em>default 'TNC'</em>) – The format of input and output tensors. T, N and C stand for |
| sequence length, batch size, and feature dimensions respectively.</p></li> |
| <li><p><strong>dropout</strong> (<em>float</em><em>, </em><em>default 0</em>) – If non-zero, introduces a dropout layer on the outputs of each |
| RNN layer except the last layer</p></li> |
| <li><p><strong>bidirectional</strong> (<em>bool</em><em>, </em><em>default False</em>) – If True, becomes a bidirectional RNN.</p></li> |
| <li><p><strong>i2h_weight_initializer</strong> (<em>str</em><em> or </em><a class="reference internal" href="../../initializer/index.html#mxnet.initializer.Initializer" title="mxnet.initializer.Initializer"><em>Initializer</em></a>) – Initializer for the input weights matrix, used for the linear |
| transformation of the inputs.</p></li> |
| <li><p><strong>h2h_weight_initializer</strong> (<em>str</em><em> or </em><a class="reference internal" href="../../initializer/index.html#mxnet.initializer.Initializer" title="mxnet.initializer.Initializer"><em>Initializer</em></a>) – Initializer for the recurrent weights matrix, used for the linear |
| transformation of the recurrent state.</p></li> |
| <li><p><strong>i2h_bias_initializer</strong> (<em>str</em><em> or </em><a class="reference internal" href="../../initializer/index.html#mxnet.initializer.Initializer" title="mxnet.initializer.Initializer"><em>Initializer</em></a>) – Initializer for the bias vector.</p></li> |
| <li><p><strong>h2h_bias_initializer</strong> (<em>str</em><em> or </em><a class="reference internal" href="../../initializer/index.html#mxnet.initializer.Initializer" title="mxnet.initializer.Initializer"><em>Initializer</em></a>) – Initializer for the bias vector.</p></li> |
| <li><p><strong>dtype</strong> (<em>str</em><em>, </em><em>default 'float32'</em>) – Type to initialize the parameters and default states to</p></li> |
| <li><p><strong>input_size</strong> (<em>int</em><em>, </em><em>default 0</em>) – The number of expected features in the input x. |
| If not specified, it will be inferred from input.</p></li> |
| </ul> |
| </dd> |
| </dl> |
| <dl class="simple"> |
| <dt>Inputs:</dt><dd><ul class="simple"> |
| <li><p><strong>data</strong>: input tensor with shape <cite>(sequence_length, batch_size, input_size)</cite> |
| when <cite>layout</cite> is “TNC”. For other layouts, dimensions are permuted accordingly |
| using transpose() operator which adds performance overhead. Consider creating |
| batches in TNC layout during data batching step.</p></li> |
| <li><p><strong>states</strong>: initial recurrent state tensor with shape |
| <cite>(num_layers, batch_size, num_hidden)</cite>. If <cite>bidirectional</cite> is True, |
| shape will instead be <cite>(2*num_layers, batch_size, num_hidden)</cite>. If |
| <cite>states</cite> is None, zeros will be used as default begin states.</p></li> |
| </ul> |
| </dd> |
| <dt>Outputs:</dt><dd><ul class="simple"> |
| <li><p><strong>out</strong>: output tensor with shape <cite>(sequence_length, batch_size, num_hidden)</cite> |
| when <cite>layout</cite> is “TNC”. If <cite>bidirectional</cite> is True, output shape will instead |
| be <cite>(sequence_length, batch_size, 2*num_hidden)</cite></p></li> |
| <li><p><strong>out_states</strong>: output recurrent state tensor with the same shape as <cite>states</cite>. |
| If <cite>states</cite> is None <cite>out_states</cite> will not be returned.</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">layer</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">gluon</span><span class="o">.</span><span class="n">rnn</span><span class="o">.</span><span class="n">GRU</span><span class="p">(</span><span class="mi">100</span><span class="p">,</span> <span class="mi">3</span><span class="p">)</span> |
| <span class="gp">>>> </span><span class="n">layer</span><span class="o">.</span><span class="n">initialize</span><span class="p">()</span> |
| <span class="gp">>>> </span><span class="nb">input</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">uniform</span><span class="p">(</span><span class="n">size</span><span class="o">=</span><span class="p">(</span><span class="mi">5</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">10</span><span class="p">))</span> |
| <span class="gp">>>> </span><span class="c1"># by default zeros are used as begin state</span> |
| <span class="gp">>>> </span><span class="n">output</span> <span class="o">=</span> <span class="n">layer</span><span class="p">(</span><span class="nb">input</span><span class="p">)</span> |
| <span class="gp">>>> </span><span class="c1"># manually specify begin state.</span> |
| <span class="gp">>>> </span><span class="n">h0</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">uniform</span><span class="p">(</span><span class="n">size</span><span class="o">=</span><span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">100</span><span class="p">))</span> |
| <span class="gp">>>> </span><span class="n">output</span><span class="p">,</span> <span class="n">hn</span> <span class="o">=</span> <span class="n">layer</span><span class="p">(</span><span class="nb">input</span><span class="p">,</span> <span class="n">h0</span><span class="p">)</span> |
| </pre></div> |
| </div> |
| </dd></dl> |
| |
| <dl class="class"> |
| <dt id="mxnet.gluon.rnn.GRUCell"> |
| <em class="property">class </em><code class="sig-name descname">GRUCell</code><span class="sig-paren">(</span><em class="sig-param">hidden_size</em>, <em class="sig-param">i2h_weight_initializer=None</em>, <em class="sig-param">h2h_weight_initializer=None</em>, <em class="sig-param">i2h_bias_initializer='zeros'</em>, <em class="sig-param">h2h_bias_initializer='zeros'</em>, <em class="sig-param">input_size=0</em>, <em class="sig-param">activation='tanh'</em>, <em class="sig-param">recurrent_activation='sigmoid'</em><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/mxnet/gluon/rnn/rnn_cell.html#GRUCell"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.gluon.rnn.GRUCell" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">mxnet.gluon.rnn.rnn_cell.HybridRecurrentCell</span></code></p> |
| <p>Gated Rectified Unit (GRU) network cell. |
| Note: this is an implementation of the cuDNN version of GRUs |
| (slight modification compared to Cho et al. 2014; the reset gate <span class="math notranslate nohighlight">\(r_t\)</span> |
| is applied after matrix multiplication).</p> |
| <p>Each call computes the following function:</p> |
| <div class="math notranslate nohighlight"> |
| \[\begin{split}\begin{array}{ll} |
| r_t = sigmoid(W_{ir} x_t + b_{ir} + W_{hr} h_{(t-1)} + b_{hr}) \\ |
| i_t = sigmoid(W_{ii} x_t + b_{ii} + W_{hi} h_{(t-1)} + b_{hi}) \\ |
| n_t = \tanh(W_{in} x_t + b_{in} + r_t * (W_{hn} h_{(t-1)} + b_{hn})) \\ |
| h_t = (1 - i_t) * n_t + i_t * h_{(t-1)} \\ |
| \end{array}\end{split}\]</div> |
| <p><strong>Methods</strong></p> |
| <table class="longtable docutils align-default"> |
| <colgroup> |
| <col style="width: 10%" /> |
| <col style="width: 90%" /> |
| </colgroup> |
| <tbody> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.GRUCell.apply" title="mxnet.gluon.rnn.GRUCell.apply"><code class="xref py py-obj docutils literal notranslate"><span class="pre">apply</span></code></a>(fn)</p></td> |
| <td><p>Applies <code class="docutils literal notranslate"><span class="pre">fn</span></code> recursively to every child block as well as self.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.GRUCell.begin_state" title="mxnet.gluon.rnn.GRUCell.begin_state"><code class="xref py py-obj docutils literal notranslate"><span class="pre">begin_state</span></code></a>([batch_size, func])</p></td> |
| <td><p>Initial state for this cell.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.GRUCell.cast" title="mxnet.gluon.rnn.GRUCell.cast"><code class="xref py py-obj docutils literal notranslate"><span class="pre">cast</span></code></a>(dtype)</p></td> |
| <td><p>Cast this Block to use another data type.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.GRUCell.collect_params" title="mxnet.gluon.rnn.GRUCell.collect_params"><code class="xref py py-obj docutils literal notranslate"><span class="pre">collect_params</span></code></a>([select])</p></td> |
| <td><p>Returns a <code class="xref py py-class docutils literal notranslate"><span class="pre">Dict</span></code> containing this <code class="xref py py-class docutils literal notranslate"><span class="pre">Block</span></code> and all of its children’s Parameters(default), also can returns the select <code class="xref py py-class docutils literal notranslate"><span class="pre">Dict</span></code> which match some given regular expressions.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.GRUCell.export" title="mxnet.gluon.rnn.GRUCell.export"><code class="xref py py-obj docutils literal notranslate"><span class="pre">export</span></code></a>(path[, epoch, remove_amp_cast])</p></td> |
| <td><p>Export HybridBlock to json format that can be loaded by <cite>gluon.SymbolBlock.imports</cite> or the C++ interface.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.GRUCell.forward" title="mxnet.gluon.rnn.GRUCell.forward"><code class="xref py py-obj docutils literal notranslate"><span class="pre">forward</span></code></a>(inputs, states)</p></td> |
| <td><p>Unrolls the recurrent cell for one time step.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.GRUCell.hybridize" title="mxnet.gluon.rnn.GRUCell.hybridize"><code class="xref py py-obj docutils literal notranslate"><span class="pre">hybridize</span></code></a>([active])</p></td> |
| <td><p>Please refer description of HybridBlock hybridize().</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.GRUCell.infer_shape" title="mxnet.gluon.rnn.GRUCell.infer_shape"><code class="xref py py-obj docutils literal notranslate"><span class="pre">infer_shape</span></code></a>(i, x, is_bidirect)</p></td> |
| <td><p>Infers shape of Parameters from inputs.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.GRUCell.infer_type" title="mxnet.gluon.rnn.GRUCell.infer_type"><code class="xref py py-obj docutils literal notranslate"><span class="pre">infer_type</span></code></a>(*args)</p></td> |
| <td><p>Infers data type of Parameters from inputs.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.GRUCell.initialize" title="mxnet.gluon.rnn.GRUCell.initialize"><code class="xref py py-obj docutils literal notranslate"><span class="pre">initialize</span></code></a>([init, device, verbose, force_reinit])</p></td> |
| <td><p>Initializes <code class="xref py py-class docutils literal notranslate"><span class="pre">Parameter</span></code> s of this <code class="xref py py-class docutils literal notranslate"><span class="pre">Block</span></code> and its children.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.GRUCell.load" title="mxnet.gluon.rnn.GRUCell.load"><code class="xref py py-obj docutils literal notranslate"><span class="pre">load</span></code></a>(prefix)</p></td> |
| <td><p>Load a model saved using the <cite>save</cite> API</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.GRUCell.load_dict" title="mxnet.gluon.rnn.GRUCell.load_dict"><code class="xref py py-obj docutils literal notranslate"><span class="pre">load_dict</span></code></a>(param_dict[, device, …])</p></td> |
| <td><p>Load parameters from dict</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.GRUCell.load_parameters" title="mxnet.gluon.rnn.GRUCell.load_parameters"><code class="xref py py-obj docutils literal notranslate"><span class="pre">load_parameters</span></code></a>(filename[, device, …])</p></td> |
| <td><p>Load parameters from file previously saved by <cite>save_parameters</cite>.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.GRUCell.optimize_for" title="mxnet.gluon.rnn.GRUCell.optimize_for"><code class="xref py py-obj docutils literal notranslate"><span class="pre">optimize_for</span></code></a>(x, *args[, backend, clear, …])</p></td> |
| <td><p>Partitions the current HybridBlock and optimizes it for a given backend without executing a forward pass.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.GRUCell.register_child" title="mxnet.gluon.rnn.GRUCell.register_child"><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_child</span></code></a>(block[, name])</p></td> |
| <td><p>Registers block as a child of self.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.GRUCell.register_forward_hook" title="mxnet.gluon.rnn.GRUCell.register_forward_hook"><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_forward_hook</span></code></a>(hook)</p></td> |
| <td><p>Registers a forward hook on the block.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.GRUCell.register_forward_pre_hook" title="mxnet.gluon.rnn.GRUCell.register_forward_pre_hook"><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_forward_pre_hook</span></code></a>(hook)</p></td> |
| <td><p>Registers a forward pre-hook on the block.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.GRUCell.register_op_hook" title="mxnet.gluon.rnn.GRUCell.register_op_hook"><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_op_hook</span></code></a>(callback[, monitor_all])</p></td> |
| <td><p>Install callback monitor.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.GRUCell.reset" title="mxnet.gluon.rnn.GRUCell.reset"><code class="xref py py-obj docutils literal notranslate"><span class="pre">reset</span></code></a>()</p></td> |
| <td><p>Reset before re-using the cell for another graph.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.GRUCell.reset_ctx" title="mxnet.gluon.rnn.GRUCell.reset_ctx"><code class="xref py py-obj docutils literal notranslate"><span class="pre">reset_ctx</span></code></a>(ctx)</p></td> |
| <td><p>This function has been deprecated.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.GRUCell.reset_device" title="mxnet.gluon.rnn.GRUCell.reset_device"><code class="xref py py-obj docutils literal notranslate"><span class="pre">reset_device</span></code></a>(device)</p></td> |
| <td><p>Re-assign all Parameters to other devices.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.GRUCell.save" title="mxnet.gluon.rnn.GRUCell.save"><code class="xref py py-obj docutils literal notranslate"><span class="pre">save</span></code></a>(prefix)</p></td> |
| <td><p>Save the model architecture and parameters to load again later</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.GRUCell.save_parameters" title="mxnet.gluon.rnn.GRUCell.save_parameters"><code class="xref py py-obj docutils literal notranslate"><span class="pre">save_parameters</span></code></a>(filename[, deduplicate])</p></td> |
| <td><p>Save parameters to file.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.GRUCell.setattr" title="mxnet.gluon.rnn.GRUCell.setattr"><code class="xref py py-obj docutils literal notranslate"><span class="pre">setattr</span></code></a>(name, value)</p></td> |
| <td><p>Set an attribute to a new value for all Parameters.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.GRUCell.share_parameters" title="mxnet.gluon.rnn.GRUCell.share_parameters"><code class="xref py py-obj docutils literal notranslate"><span class="pre">share_parameters</span></code></a>(shared)</p></td> |
| <td><p>Share parameters recursively inside the model.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.GRUCell.state_info" title="mxnet.gluon.rnn.GRUCell.state_info"><code class="xref py py-obj docutils literal notranslate"><span class="pre">state_info</span></code></a>([batch_size])</p></td> |
| <td><p>shape and layout information of states</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.GRUCell.summary" title="mxnet.gluon.rnn.GRUCell.summary"><code class="xref py py-obj docutils literal notranslate"><span class="pre">summary</span></code></a>(*inputs)</p></td> |
| <td><p>Print the summary of the model’s output and parameters.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.GRUCell.unroll" title="mxnet.gluon.rnn.GRUCell.unroll"><code class="xref py py-obj docutils literal notranslate"><span class="pre">unroll</span></code></a>(length, inputs[, begin_state, …])</p></td> |
| <td><p>Unrolls an RNN cell across time steps.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.GRUCell.zero_grad" title="mxnet.gluon.rnn.GRUCell.zero_grad"><code class="xref py py-obj docutils literal notranslate"><span class="pre">zero_grad</span></code></a>()</p></td> |
| <td><p>Sets all Parameters’ gradient buffer to 0.</p></td> |
| </tr> |
| </tbody> |
| </table> |
| <p><strong>Attributes</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.rnn.GRUCell.params" title="mxnet.gluon.rnn.GRUCell.params"><code class="xref py py-obj docutils literal notranslate"><span class="pre">params</span></code></a></p></td> |
| <td><p>Returns this <code class="xref py py-class docutils literal notranslate"><span class="pre">Block</span></code>’s parameter dictionary (does not include its children’s parameters).</p></td> |
| </tr> |
| </tbody> |
| </table> |
| <p>where <span class="math notranslate nohighlight">\(h_t\)</span> is the hidden state at time <cite>t</cite>, <span class="math notranslate nohighlight">\(x_t\)</span> is the hidden |
| state of the previous layer at time <cite>t</cite> or <span class="math notranslate nohighlight">\(input_t\)</span> for the first layer, |
| and <span class="math notranslate nohighlight">\(r_t\)</span>, <span class="math notranslate nohighlight">\(i_t\)</span>, <span class="math notranslate nohighlight">\(n_t\)</span> are the reset, input, and new gates, respectively.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>hidden_size</strong> (<em>int</em>) – Number of units in output symbol.</p></li> |
| <li><p><strong>i2h_weight_initializer</strong> (<em>str</em><em> or </em><a class="reference internal" href="../../initializer/index.html#mxnet.initializer.Initializer" title="mxnet.initializer.Initializer"><em>Initializer</em></a>) – Initializer for the input weights matrix, used for the linear |
| transformation of the inputs.</p></li> |
| <li><p><strong>h2h_weight_initializer</strong> (<em>str</em><em> or </em><a class="reference internal" href="../../initializer/index.html#mxnet.initializer.Initializer" title="mxnet.initializer.Initializer"><em>Initializer</em></a>) – Initializer for the recurrent weights matrix, used for the linear |
| transformation of the recurrent state.</p></li> |
| <li><p><strong>i2h_bias_initializer</strong> (<em>str</em><em> or </em><a class="reference internal" href="../../initializer/index.html#mxnet.initializer.Initializer" title="mxnet.initializer.Initializer"><em>Initializer</em></a><em>, </em><em>default 'zeros'</em>) – Initializer for the bias vector.</p></li> |
| <li><p><strong>h2h_bias_initializer</strong> (<em>str</em><em> or </em><a class="reference internal" href="../../initializer/index.html#mxnet.initializer.Initializer" title="mxnet.initializer.Initializer"><em>Initializer</em></a><em>, </em><em>default 'zeros'</em>) – Initializer for the bias vector.</p></li> |
| <li><p><strong>input_size</strong> (<em>int</em><em>, </em><em>default 0</em>) – The number of expected features in the input x. |
| If not specified, it will be inferred from input.</p></li> |
| <li><p><strong>activation</strong> (<em>str</em><em>, </em><em>default 'tanh'</em>) – Activation type to use. See nd/symbol Activation |
| for supported types.</p></li> |
| <li><p><strong>recurrent_activation</strong> (<em>str</em><em>, </em><em>default 'sigmoid'</em>) – Activation type to use for the recurrent step. See nd/symbol Activation |
| for supported types.</p></li> |
| </ul> |
| </dd> |
| </dl> |
| <dl class="simple"> |
| <dt>Inputs:</dt><dd><ul class="simple"> |
| <li><p><strong>data</strong>: input tensor with shape <cite>(batch_size, input_size)</cite>.</p></li> |
| <li><p><strong>states</strong>: a list of one initial recurrent state tensor with shape |
| <cite>(batch_size, num_hidden)</cite>.</p></li> |
| </ul> |
| </dd> |
| <dt>Outputs:</dt><dd><ul class="simple"> |
| <li><p><strong>out</strong>: output tensor with shape <cite>(batch_size, num_hidden)</cite>.</p></li> |
| <li><p><strong>next_states</strong>: a list of one output recurrent state tensor with the |
| same shape as <cite>states</cite>.</p></li> |
| </ul> |
| </dd> |
| </dl> |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.GRUCell.apply"> |
| <code class="sig-name descname">apply</code><span class="sig-paren">(</span><em class="sig-param">fn</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.rnn.GRUCell.apply" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Applies <code class="docutils literal notranslate"><span class="pre">fn</span></code> recursively to every child block as well as self.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><p><strong>fn</strong> (<em>callable</em>) – Function to be applied to each submodule, of form <cite>fn(block)</cite>.</p> |
| </dd> |
| <dt class="field-even">Returns</dt> |
| <dd class="field-even"><p></p> |
| </dd> |
| <dt class="field-odd">Return type</dt> |
| <dd class="field-odd"><p>this block</p> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.GRUCell.begin_state"> |
| <code class="sig-name descname">begin_state</code><span class="sig-paren">(</span><em class="sig-param">batch_size=0</em>, <em class="sig-param">func=<function zeros></em>, <em class="sig-param">**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.rnn.GRUCell.begin_state" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Initial state for this cell.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>func</strong> (<em>callable</em><em>, </em><em>default symbol.zeros</em>) – <p>Function for creating initial state.</p> |
| <p>For Symbol API, func can be <cite>symbol.zeros</cite>, <cite>symbol.uniform</cite>, |
| <cite>symbol.var etc</cite>. Use <cite>symbol.var</cite> if you want to directly |
| feed input as states.</p> |
| <p>For NDArray API, func can be <cite>ndarray.zeros</cite>, <cite>ndarray.ones</cite>, etc.</p> |
| </p></li> |
| <li><p><strong>batch_size</strong> (<em>int</em><em>, </em><em>default 0</em>) – Only required for NDArray API. Size of the batch (‘N’ in layout) |
| dimension of input.</p></li> |
| <li><p><strong>**kwargs</strong> – Additional keyword arguments passed to func. For example |
| <cite>mean</cite>, <cite>std</cite>, <cite>dtype</cite>, etc.</p></li> |
| </ul> |
| </dd> |
| <dt class="field-even">Returns</dt> |
| <dd class="field-even"><p><strong>states</strong> – Starting states for the first RNN step.</p> |
| </dd> |
| <dt class="field-odd">Return type</dt> |
| <dd class="field-odd"><p>nested list of Symbol</p> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.GRUCell.cast"> |
| <code class="sig-name descname">cast</code><span class="sig-paren">(</span><em class="sig-param">dtype</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.rnn.GRUCell.cast" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Cast this Block to use another data type.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><p><strong>dtype</strong> (<em>str</em><em> or </em><em>numpy.dtype</em>) – The new data type.</p> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.GRUCell.collect_params"> |
| <code class="sig-name descname">collect_params</code><span class="sig-paren">(</span><em class="sig-param">select=None</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.rnn.GRUCell.collect_params" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Returns a <code class="xref py py-class docutils literal notranslate"><span class="pre">Dict</span></code> containing this <code class="xref py py-class docutils literal notranslate"><span class="pre">Block</span></code> and all of its |
| children’s Parameters(default), also can returns the select <code class="xref py py-class docutils literal notranslate"><span class="pre">Dict</span></code> |
| which match some given regular expressions.</p> |
| <p>For example, collect the specified parameters in [‘conv1.weight’, ‘conv1.bias’, ‘fc.weight’, |
| ‘fc.bias’]:</p> |
| <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">model</span><span class="o">.</span><span class="n">collect_params</span><span class="p">(</span><span class="s1">'conv1.weight|conv1.bias|fc.weight|fc.bias'</span><span class="p">)</span> |
| </pre></div> |
| </div> |
| <p>or collect all parameters whose names end with ‘weight’ or ‘bias’, this can be done |
| using regular expressions:</p> |
| <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">model</span><span class="o">.</span><span class="n">collect_params</span><span class="p">(</span><span class="s1">'.*weight|.*bias'</span><span class="p">)</span> |
| </pre></div> |
| </div> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><p><strong>select</strong> (<em>str</em>) – regular expressions</p> |
| </dd> |
| <dt class="field-even">Returns</dt> |
| <dd class="field-even"><p></p> |
| </dd> |
| <dt class="field-odd">Return type</dt> |
| <dd class="field-odd"><p>The selected <code class="xref py py-class docutils literal notranslate"><span class="pre">Dict</span></code></p> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.GRUCell.export"> |
| <code class="sig-name descname">export</code><span class="sig-paren">(</span><em class="sig-param">path</em>, <em class="sig-param">epoch=0</em>, <em class="sig-param">remove_amp_cast=True</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.rnn.GRUCell.export" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Export HybridBlock to json format that can be loaded by |
| <cite>gluon.SymbolBlock.imports</cite> or the C++ interface.</p> |
| <div class="admonition note"> |
| <p class="admonition-title">Note</p> |
| <p>When there are only one input, it will have name <cite>data</cite>. When there |
| Are more than one inputs, they will be named as <cite>data0</cite>, <cite>data1</cite>, etc.</p> |
| </div> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>path</strong> (<em>str</em><em> or </em><em>None</em>) – Path to save model. Two files <cite>path-symbol.json</cite> and <cite>path-xxxx.params</cite> |
| will be created, where xxxx is the 4 digits epoch number. |
| If None, do not export to file but return Python Symbol object and |
| corresponding dictionary of parameters.</p></li> |
| <li><p><strong>epoch</strong> (<em>int</em>) – Epoch number of saved model.</p></li> |
| <li><p><strong>remove_amp_cast</strong> (<em>bool</em><em>, </em><em>optional</em>) – Whether to remove the amp_cast and amp_multicast operators, before saving the model.</p></li> |
| </ul> |
| </dd> |
| <dt class="field-even">Returns</dt> |
| <dd class="field-even"><p><ul class="simple"> |
| <li><p><strong>symbol_filename</strong> (<em>str</em>) – Filename to which model symbols were saved, including <cite>path</cite> prefix.</p></li> |
| <li><p><strong>params_filename</strong> (<em>str</em>) – Filename to which model parameters were saved, including <cite>path</cite> prefix.</p></li> |
| </ul> |
| </p> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.GRUCell.forward"> |
| <code class="sig-name descname">forward</code><span class="sig-paren">(</span><em class="sig-param">inputs</em>, <em class="sig-param">states</em><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/mxnet/gluon/rnn/rnn_cell.html#GRUCell.forward"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.gluon.rnn.GRUCell.forward" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Unrolls the recurrent cell for one time step.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>inputs</strong> (<em>sym.Variable</em>) – Input symbol, 2D, of shape (batch_size * num_units).</p></li> |
| <li><p><strong>states</strong> (<em>list of sym.Variable</em>) – RNN state from previous step or the output of begin_state().</p></li> |
| </ul> |
| </dd> |
| <dt class="field-even">Returns</dt> |
| <dd class="field-even"><p><ul class="simple"> |
| <li><p><strong>output</strong> (<em>Symbol</em>) – Symbol corresponding to the output from the RNN when unrolling |
| for a single time step.</p></li> |
| <li><p><strong>states</strong> (<em>list of Symbol</em>) – The new state of this RNN after this unrolling. |
| The type of this symbol is same as the output of <cite>begin_state()</cite>. |
| This can be used as an input state to the next time step |
| of this RNN.</p></li> |
| </ul> |
| </p> |
| </dd> |
| </dl> |
| <div class="admonition seealso"> |
| <p class="admonition-title">See also</p> |
| <dl class="simple"> |
| <dt><a class="reference internal" href="#mxnet.gluon.rnn.GRUCell.begin_state" title="mxnet.gluon.rnn.GRUCell.begin_state"><code class="xref py py-meth docutils literal notranslate"><span class="pre">begin_state()</span></code></a></dt><dd><p>This function can provide the states for the first time step.</p> |
| </dd> |
| <dt><a class="reference internal" href="#mxnet.gluon.rnn.GRUCell.unroll" title="mxnet.gluon.rnn.GRUCell.unroll"><code class="xref py py-meth docutils literal notranslate"><span class="pre">unroll()</span></code></a></dt><dd><p>This function unrolls an RNN for a given number of (>=1) time steps.</p> |
| </dd> |
| </dl> |
| </div> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.GRUCell.hybridize"> |
| <code class="sig-name descname">hybridize</code><span class="sig-paren">(</span><em class="sig-param">active=True</em>, <em class="sig-param">**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.rnn.GRUCell.hybridize" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Please refer description of HybridBlock hybridize().</p> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.GRUCell.infer_shape"> |
| <code class="sig-name descname">infer_shape</code><span class="sig-paren">(</span><em class="sig-param">i</em>, <em class="sig-param">x</em>, <em class="sig-param">is_bidirect</em><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/mxnet/gluon/rnn/rnn_cell.html#GRUCell.infer_shape"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.gluon.rnn.GRUCell.infer_shape" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Infers shape of Parameters from inputs.</p> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.GRUCell.infer_type"> |
| <code class="sig-name descname">infer_type</code><span class="sig-paren">(</span><em class="sig-param">*args</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.rnn.GRUCell.infer_type" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Infers data type of Parameters from inputs.</p> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.GRUCell.initialize"> |
| <code class="sig-name descname">initialize</code><span class="sig-paren">(</span><em class="sig-param">init=<mxnet.initializer.Uniform object></em>, <em class="sig-param">device=None</em>, <em class="sig-param">verbose=False</em>, <em class="sig-param">force_reinit=False</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.rnn.GRUCell.initialize" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Initializes <code class="xref py py-class docutils literal notranslate"><span class="pre">Parameter</span></code> s of this <code class="xref py py-class docutils literal notranslate"><span class="pre">Block</span></code> and its children.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>init</strong> (<a class="reference internal" href="../../initializer/index.html#mxnet.initializer.Initializer" title="mxnet.initializer.Initializer"><em>Initializer</em></a>) – Global default Initializer to be used when <code class="xref py py-meth docutils literal notranslate"><span class="pre">Parameter.init()</span></code> is <code class="docutils literal notranslate"><span class="pre">None</span></code>. |
| Otherwise, <code class="xref py py-meth docutils literal notranslate"><span class="pre">Parameter.init()</span></code> takes precedence.</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>) – Keeps a copy of Parameters on one or many device(s).</p></li> |
| <li><p><strong>verbose</strong> (<em>bool</em><em>, </em><em>default False</em>) – Whether to verbosely print out details on initialization.</p></li> |
| <li><p><strong>force_reinit</strong> (<em>bool</em><em>, </em><em>default False</em>) – Whether to force re-initialization if parameter is already initialized.</p></li> |
| </ul> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.GRUCell.load"> |
| <code class="sig-name descname">load</code><span class="sig-paren">(</span><em class="sig-param">prefix</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.rnn.GRUCell.load" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Load a model saved using the <cite>save</cite> API</p> |
| <p>Reconfigures a model using the saved configuration. This function |
| does not regenerate the model architecture. It resets each Block’s |
| parameter UUIDs as they were when saved in order to match the names of the |
| saved parameters.</p> |
| <p>This function assumes the Blocks in the model were created in the same |
| order they were when the model was saved. This is because each Block is |
| uniquely identified by Block class name and a unique ID in order (since |
| its an OrderedDict) and uses the unique ID to denote that specific Block.</p> |
| <p>Assumes that the model is created in an identical order every time. |
| If the model is not able to be recreated deterministically do not |
| use this set of APIs to save/load your model.</p> |
| <p>For HybridBlocks, the cached_graph (Symbol & inputs) and settings are |
| restored if it had been hybridized before saving.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><p><strong>prefix</strong> (<em>str</em>) – The prefix to use in filenames for loading this model: |
| <prefix>-model.json and <prefix>-model.params</p> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.GRUCell.load_dict"> |
| <code class="sig-name descname">load_dict</code><span class="sig-paren">(</span><em class="sig-param">param_dict</em>, <em class="sig-param">device=None</em>, <em class="sig-param">allow_missing=False</em>, <em class="sig-param">ignore_extra=False</em>, <em class="sig-param">cast_dtype=False</em>, <em class="sig-param">dtype_source='current'</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.rnn.GRUCell.load_dict" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Load parameters from dict</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>param_dict</strong> (<em>dict</em>) – Dictionary containing model 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>, </em><em>optional</em>) – Device context on which the memory is allocated. Default is |
| <cite>mxnet.device.current_device()</cite>.</p></li> |
| <li><p><strong>allow_missing</strong> (<em>bool</em><em>, </em><em>default False</em>) – Whether to silently skip loading parameters not represented in the file.</p></li> |
| <li><p><strong>ignore_extra</strong> (<em>bool</em><em>, </em><em>default False</em>) – Whether to silently ignore parameters from the file that are not |
| present in this dict.</p></li> |
| <li><p><strong>cast_dtype</strong> (<em>bool</em><em>, </em><em>default False</em>) – Cast the data type of the NDArray loaded from the checkpoint to the dtype |
| provided by the Parameter if any</p></li> |
| <li><p><strong>dtype_source</strong> (<em>str</em><em>, </em><em>default 'current'</em>) – must be in {‘current’, ‘saved’} |
| Only valid if cast_dtype=True, specify the source of the dtype for casting |
| the parameters</p></li> |
| </ul> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.GRUCell.load_parameters"> |
| <code class="sig-name descname">load_parameters</code><span class="sig-paren">(</span><em class="sig-param">filename</em>, <em class="sig-param">device=None</em>, <em class="sig-param">allow_missing=False</em>, <em class="sig-param">ignore_extra=False</em>, <em class="sig-param">cast_dtype=False</em>, <em class="sig-param">dtype_source='current'</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.rnn.GRUCell.load_parameters" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Load parameters from file previously saved by <cite>save_parameters</cite>.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>filename</strong> (<em>str</em>) – Path to parameter file.</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><em>, </em><em>default cpu</em><em>(</em><em>)</em>) – Device(s) to initialize loaded parameters on.</p></li> |
| <li><p><strong>allow_missing</strong> (<em>bool</em><em>, </em><em>default False</em>) – Whether to silently skip loading parameters not represents in the file.</p></li> |
| <li><p><strong>ignore_extra</strong> (<em>bool</em><em>, </em><em>default False</em>) – Whether to silently ignore parameters from the file that are not |
| present in this Block.</p></li> |
| <li><p><strong>cast_dtype</strong> (<em>bool</em><em>, </em><em>default False</em>) – Cast the data type of the NDArray loaded from the checkpoint to the dtype |
| provided by the Parameter if any.</p></li> |
| <li><p><strong>dtype_source</strong> (<em>str</em><em>, </em><em>default 'current'</em>) – must be in {‘current’, ‘saved’} |
| Only valid if cast_dtype=True, specify the source of the dtype for casting |
| the parameters</p></li> |
| </ul> |
| </dd> |
| </dl> |
| <p class="rubric">References</p> |
| <p><a class="reference external" href="https://mxnet.apache.org/api/python/docs/tutorials/packages/gluon/blocks/save_load_params.html">Saving and Loading Gluon Models</a></p> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.GRUCell.optimize_for"> |
| <code class="sig-name descname">optimize_for</code><span class="sig-paren">(</span><em class="sig-param">x</em>, <em class="sig-param">*args</em>, <em class="sig-param">backend=None</em>, <em class="sig-param">clear=False</em>, <em class="sig-param">partition_if_dynamic=True</em>, <em class="sig-param">static_alloc=False</em>, <em class="sig-param">static_shape=False</em>, <em class="sig-param">inline_limit=2</em>, <em class="sig-param">forward_bulk_size=None</em>, <em class="sig-param">backward_bulk_size=None</em>, <em class="sig-param">**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.rnn.GRUCell.optimize_for" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Partitions the current HybridBlock and optimizes it for a given backend |
| without executing a forward pass. Modifies the HybridBlock in-place.</p> |
| <p>Immediately partitions a HybridBlock using the specified backend. Combines |
| the work done in the hybridize API with part of the work done in the forward |
| pass without calling the CachedOp. Can be used in place of hybridize, |
| afterwards <cite>export</cite> can be called or inference can be run. See README.md in |
| example/extensions/lib_subgraph/README.md for more details.</p> |
| <p class="rubric">Examples</p> |
| <p># partition and then export to file |
| block.optimize_for(x, backend=’myPart’) |
| block.export(‘partitioned’)</p> |
| <p># partition and then run inference |
| block.optimize_for(x, backend=’myPart’) |
| block(x)</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>x</strong> (<a class="reference internal" href="../../legacy/ndarray/ndarray.html#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – first input to model</p></li> |
| <li><p><strong>*args</strong> (<a class="reference internal" href="../../legacy/ndarray/ndarray.html#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – other inputs to model</p></li> |
| <li><p><strong>backend</strong> (<em>str</em>) – The name of backend, as registered in <cite>SubgraphBackendRegistry</cite>, default None</p></li> |
| <li><p><strong>backend_opts</strong> (<em>dict of user-specified options to pass to the backend for partitioning</em><em>, </em><em>optional</em>) – Passed on to <cite>PrePartition</cite> and <cite>PostPartition</cite> functions of <cite>SubgraphProperty</cite></p></li> |
| <li><p><strong>clear</strong> (<em>bool</em><em>, </em><em>default False</em>) – clears any previous optimizations</p></li> |
| <li><p><strong>partition_if_dynamic</strong> (<em>bool</em><em>, </em><em>default False</em>) – whether to partition the graph when dynamic shape op exists</p></li> |
| <li><p><strong>static_alloc</strong> (<em>bool</em><em>, </em><em>default False</em>) – Statically allocate memory to improve speed. Memory usage may increase.</p></li> |
| <li><p><strong>static_shape</strong> (<em>bool</em><em>, </em><em>default False</em>) – Optimize for invariant input shapes between iterations. Must also |
| set static_alloc to True. Change of input shapes is still allowed |
| but slower.</p></li> |
| <li><p><strong>inline_limit</strong> (<em>optional int</em><em>, </em><em>default 2</em>) – Maximum number of operators that can be inlined.</p></li> |
| <li><p><strong>forward_bulk_size</strong> (<em>optional int</em><em>, </em><em>default None</em>) – Segment size of bulk execution during forward pass.</p></li> |
| <li><p><strong>backward_bulk_size</strong> (<em>optional int</em><em>, </em><em>default None</em>) – Segment size of bulk execution during backward pass.</p></li> |
| <li><p><strong>**kwargs</strong> (<em>The backend options</em><em>, </em><em>optional</em>) – Passed on to <cite>PrePartition</cite> and <cite>PostPartition</cite> functions of <cite>SubgraphProperty</cite></p></li> |
| </ul> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.GRUCell.params"> |
| <em class="property">property </em><code class="sig-name descname">params</code><a class="headerlink" href="#mxnet.gluon.rnn.GRUCell.params" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Returns this <code class="xref py py-class docutils literal notranslate"><span class="pre">Block</span></code>’s parameter dictionary (does not include its |
| children’s parameters).</p> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.GRUCell.register_child"> |
| <code class="sig-name descname">register_child</code><span class="sig-paren">(</span><em class="sig-param">block</em>, <em class="sig-param">name=None</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.rnn.GRUCell.register_child" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Registers block as a child of self. <code class="xref py py-class docutils literal notranslate"><span class="pre">Block</span></code> s assigned to self as |
| attributes will be registered automatically.</p> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.GRUCell.register_forward_hook"> |
| <code class="sig-name descname">register_forward_hook</code><span class="sig-paren">(</span><em class="sig-param">hook</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.rnn.GRUCell.register_forward_hook" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Registers a forward hook on the block.</p> |
| <p>The hook function is called immediately after <a class="reference internal" href="#mxnet.gluon.rnn.GRUCell.forward" title="mxnet.gluon.rnn.GRUCell.forward"><code class="xref py py-func docutils literal notranslate"><span class="pre">forward()</span></code></a>. |
| It should not modify the input or output.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><p><strong>hook</strong> (<em>callable</em>) – The forward hook function of form <cite>hook(block, input, output) -> None</cite>.</p> |
| </dd> |
| <dt class="field-even">Returns</dt> |
| <dd class="field-even"><p></p> |
| </dd> |
| <dt class="field-odd">Return type</dt> |
| <dd class="field-odd"><p><code class="xref py py-class docutils literal notranslate"><span class="pre">mxnet.gluon.utils.HookHandle</span></code></p> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.GRUCell.register_forward_pre_hook"> |
| <code class="sig-name descname">register_forward_pre_hook</code><span class="sig-paren">(</span><em class="sig-param">hook</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.rnn.GRUCell.register_forward_pre_hook" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Registers a forward pre-hook on the block.</p> |
| <p>The hook function is called immediately before <a class="reference internal" href="#mxnet.gluon.rnn.GRUCell.forward" title="mxnet.gluon.rnn.GRUCell.forward"><code class="xref py py-func docutils literal notranslate"><span class="pre">forward()</span></code></a>. |
| It should not modify the input or output.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><p><strong>hook</strong> (<em>callable</em>) – The forward hook function of form <cite>hook(block, input) -> None</cite>.</p> |
| </dd> |
| <dt class="field-even">Returns</dt> |
| <dd class="field-even"><p></p> |
| </dd> |
| <dt class="field-odd">Return type</dt> |
| <dd class="field-odd"><p><code class="xref py py-class docutils literal notranslate"><span class="pre">mxnet.gluon.utils.HookHandle</span></code></p> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.GRUCell.register_op_hook"> |
| <code class="sig-name descname">register_op_hook</code><span class="sig-paren">(</span><em class="sig-param">callback</em>, <em class="sig-param">monitor_all=False</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.rnn.GRUCell.register_op_hook" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Install callback monitor.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>callback</strong> (<em>function</em>) – Function called to inspect the values of the intermediate outputs |
| of blocks after hybridization. It takes 3 parameters: |
| name of the tensor being inspected (str) |
| name of the operator producing or consuming that tensor (str) |
| tensor being inspected (NDArray).</p></li> |
| <li><p><strong>monitor_all</strong> (<em>bool</em><em>, </em><em>default False</em>) – If True, monitor both input and output, otherwise monitor output only.</p></li> |
| </ul> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.GRUCell.reset"> |
| <code class="sig-name descname">reset</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.rnn.GRUCell.reset" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Reset before re-using the cell for another graph.</p> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.GRUCell.reset_ctx"> |
| <code class="sig-name descname">reset_ctx</code><span class="sig-paren">(</span><em class="sig-param">ctx</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.rnn.GRUCell.reset_ctx" title="Permalink to this definition">¶</a></dt> |
| <dd><p>This function has been deprecated. Please refer to <code class="docutils literal notranslate"><span class="pre">Block.reset_device</span></code>.</p> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.GRUCell.reset_device"> |
| <code class="sig-name descname">reset_device</code><span class="sig-paren">(</span><em class="sig-param">device</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.rnn.GRUCell.reset_device" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Re-assign all Parameters to other devices.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><p><strong>device</strong> (Device or list of Device, default <code class="xref py py-meth docutils literal notranslate"><span class="pre">device.current_device()</span></code>.) – Assign Parameter to given device. If device is a list of Device, a |
| copy will be made for each device.</p> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.GRUCell.save"> |
| <code class="sig-name descname">save</code><span class="sig-paren">(</span><em class="sig-param">prefix</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.rnn.GRUCell.save" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Save the model architecture and parameters to load again later</p> |
| <p>Saves the model architecture as a nested dictionary where each Block |
| in the model is a dictionary and its children are sub-dictionaries.</p> |
| <p>Each Block is uniquely identified by Block class name and a unique ID. |
| We save each Block’s parameter UUID to restore later in order to match |
| the saved parameters.</p> |
| <p>Recursively traverses a Block’s children in order (since its an |
| OrderedDict) and uses the unique ID to denote that specific Block.</p> |
| <p>Assumes that the model is created in an identical order every time. |
| If the model is not able to be recreated deterministically do not |
| use this set of APIs to save/load your model.</p> |
| <p>For HybridBlocks, the cached_graph is saved (Symbol & inputs) if |
| it has already been hybridized.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><p><strong>prefix</strong> (<em>str</em>) – The prefix to use in filenames for saving this model: |
| <prefix>-model.json and <prefix>-model.params</p> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.GRUCell.save_parameters"> |
| <code class="sig-name descname">save_parameters</code><span class="sig-paren">(</span><em class="sig-param">filename</em>, <em class="sig-param">deduplicate=False</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.rnn.GRUCell.save_parameters" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Save parameters to file.</p> |
| <p>Saved parameters can only be loaded with <cite>load_parameters</cite>. Note that this |
| method only saves parameters, not model structure. If you want to save |
| model structures, please use <code class="xref py py-meth docutils literal notranslate"><span class="pre">HybridBlock.export()</span></code>.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>filename</strong> (<em>str</em>) – Path to file.</p></li> |
| <li><p><strong>deduplicate</strong> (<em>bool</em><em>, </em><em>default False</em>) – If True, save shared parameters only once. Otherwise, if a Block |
| contains multiple sub-blocks that share parameters, each of the |
| shared parameters will be separately saved for every sub-block.</p></li> |
| </ul> |
| </dd> |
| </dl> |
| <p class="rubric">References</p> |
| <p><a class="reference external" href="https://mxnet.apache.org/api/python/docs/tutorials/packages/gluon/blocks/save_load_params.html">Saving and Loading Gluon Models</a></p> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.GRUCell.setattr"> |
| <code class="sig-name descname">setattr</code><span class="sig-paren">(</span><em class="sig-param">name</em>, <em class="sig-param">value</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.rnn.GRUCell.setattr" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Set an attribute to a new value for all Parameters.</p> |
| <p>For example, set grad_req to null if you don’t need gradient w.r.t a |
| model’s Parameters:</p> |
| <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">model</span><span class="o">.</span><span class="n">setattr</span><span class="p">(</span><span class="s1">'grad_req'</span><span class="p">,</span> <span class="s1">'null'</span><span class="p">)</span> |
| </pre></div> |
| </div> |
| <p>or change the learning rate multiplier:</p> |
| <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">model</span><span class="o">.</span><span class="n">setattr</span><span class="p">(</span><span class="s1">'lr_mult'</span><span class="p">,</span> <span class="mf">0.5</span><span class="p">)</span> |
| </pre></div> |
| </div> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>name</strong> (<em>str</em>) – Name of the attribute.</p></li> |
| <li><p><strong>value</strong> (<em>valid type for attribute name</em>) – The new value for the attribute.</p></li> |
| </ul> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.GRUCell.share_parameters"> |
| <code class="sig-name descname">share_parameters</code><span class="sig-paren">(</span><em class="sig-param">shared</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.rnn.GRUCell.share_parameters" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Share parameters recursively inside the model.</p> |
| <p>For example, if you want <code class="docutils literal notranslate"><span class="pre">dense1</span></code> to share <code class="docutils literal notranslate"><span class="pre">dense0</span></code>’s weights, you can do:</p> |
| <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">dense0</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Dense</span><span class="p">(</span><span class="mi">20</span><span class="p">)</span> |
| <span class="n">dense1</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Dense</span><span class="p">(</span><span class="mi">20</span><span class="p">)</span> |
| <span class="n">dense1</span><span class="o">.</span><span class="n">share_parameters</span><span class="p">(</span><span class="n">dense0</span><span class="o">.</span><span class="n">collect_params</span><span class="p">())</span> |
| </pre></div> |
| </div> |
| <dl class="simple"> |
| <dt>which equals to</dt><dd><p>dense1.weight = dense0.weight |
| dense1.bias = dense0.bias</p> |
| </dd> |
| </dl> |
| <p>Note that unlike the <cite>load_parameters</cite> or <cite>load_dict</cite> functions, |
| <cite>share_parameters</cite> results in the <cite>Parameter</cite> object being shared (or |
| tied) between the models, whereas <cite>load_parameters</cite> or <cite>load_dict</cite> only |
| set the value of the data dictionary of a model. If you call |
| <cite>load_parameters</cite> or <cite>load_dict</cite> after <cite>share_parameters</cite>, the loaded |
| value will be reflected in all networks that use the shared (or tied) |
| <cite>Parameter</cite> object.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><p><strong>shared</strong> (<em>Dict</em>) – Dict of the shared parameters.</p> |
| </dd> |
| <dt class="field-even">Returns</dt> |
| <dd class="field-even"><p></p> |
| </dd> |
| <dt class="field-odd">Return type</dt> |
| <dd class="field-odd"><p>this block</p> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.GRUCell.state_info"> |
| <code class="sig-name descname">state_info</code><span class="sig-paren">(</span><em class="sig-param">batch_size=0</em><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/mxnet/gluon/rnn/rnn_cell.html#GRUCell.state_info"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.gluon.rnn.GRUCell.state_info" title="Permalink to this definition">¶</a></dt> |
| <dd><p>shape and layout information of states</p> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.GRUCell.summary"> |
| <code class="sig-name descname">summary</code><span class="sig-paren">(</span><em class="sig-param">*inputs</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.rnn.GRUCell.summary" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Print the summary of the model’s output and parameters.</p> |
| <p>The network must have been initialized, and must not have been hybridized.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><p><strong>inputs</strong> (<em>object</em>) – Any input that the model supports. For any tensor in the input, only |
| <a class="reference internal" href="../../legacy/ndarray/ndarray.html#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><code class="xref py py-class docutils literal notranslate"><span class="pre">mxnet.ndarray.NDArray</span></code></a> is supported.</p> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.GRUCell.unroll"> |
| <code class="sig-name descname">unroll</code><span class="sig-paren">(</span><em class="sig-param">length</em>, <em class="sig-param">inputs</em>, <em class="sig-param">begin_state=None</em>, <em class="sig-param">layout='NTC'</em>, <em class="sig-param">merge_outputs=None</em>, <em class="sig-param">valid_length=None</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.rnn.GRUCell.unroll" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Unrolls an RNN cell across time steps.</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>) – Number of steps to unroll.</p></li> |
| <li><p><strong>inputs</strong> (<a class="reference internal" href="../../legacy/symbol/symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a><em>, </em><em>list of Symbol</em><em>, or </em><em>None</em>) – <p>If <cite>inputs</cite> is a single Symbol (usually the output |
| of Embedding symbol), it should have shape |
| (batch_size, length, …) if <cite>layout</cite> is ‘NTC’, |
| or (length, batch_size, …) if <cite>layout</cite> is ‘TNC’.</p> |
| <p>If <cite>inputs</cite> is a list of symbols (usually output of |
| previous unroll), they should all have shape |
| (batch_size, …).</p> |
| </p></li> |
| <li><p><strong>begin_state</strong> (<em>nested list of Symbol</em><em>, </em><em>optional</em>) – Input states created by <cite>begin_state()</cite> |
| or output state of another cell. |
| Created from <cite>begin_state()</cite> if <cite>None</cite>.</p></li> |
| <li><p><strong>layout</strong> (<em>str</em><em>, </em><em>optional</em>) – <cite>layout</cite> of input symbol. Only used if inputs |
| is a single Symbol.</p></li> |
| <li><p><strong>merge_outputs</strong> (<em>bool</em><em>, </em><em>optional</em>) – If <cite>False</cite>, returns outputs as a list of Symbols. |
| If <cite>True</cite>, concatenates output across time steps |
| and returns a single symbol with shape |
| (batch_size, length, …) if layout is ‘NTC’, |
| or (length, batch_size, …) if layout is ‘TNC’. |
| If <cite>None</cite>, output whatever is faster.</p></li> |
| <li><p><strong>valid_length</strong> (<a class="reference internal" href="../../legacy/symbol/symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a><em>, </em><a class="reference internal" href="../../legacy/ndarray/ndarray.html#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a><em> or </em><em>None</em>) – <cite>valid_length</cite> specifies the length of the sequences in the batch without padding. |
| This option is especially useful for building sequence-to-sequence models where |
| the input and output sequences would potentially be padded. |
| If <cite>valid_length</cite> is None, all sequences are assumed to have the same length. |
| If <cite>valid_length</cite> is a Symbol or NDArray, it should have shape (batch_size,). |
| The ith element will be the length of the ith sequence in the batch. |
| The last valid state will be return and the padded outputs will be masked with 0. |
| Note that <cite>valid_length</cite> must be smaller or equal to <cite>length</cite>.</p></li> |
| </ul> |
| </dd> |
| <dt class="field-even">Returns</dt> |
| <dd class="field-even"><p><ul class="simple"> |
| <li><p><strong>outputs</strong> (<em>list of Symbol or Symbol</em>) – Symbol (if <cite>merge_outputs</cite> is True) or list of Symbols |
| (if <cite>merge_outputs</cite> is False) corresponding to the output from |
| the RNN from this unrolling.</p></li> |
| <li><p><strong>states</strong> (<em>list of Symbol</em>) – The new state of this RNN after this unrolling. |
| The type of this symbol is same as the output of <cite>begin_state()</cite>.</p></li> |
| </ul> |
| </p> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.GRUCell.zero_grad"> |
| <code class="sig-name descname">zero_grad</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.rnn.GRUCell.zero_grad" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Sets all Parameters’ gradient buffer to 0.</p> |
| </dd></dl> |
| |
| </dd></dl> |
| |
| <dl class="class"> |
| <dt id="mxnet.gluon.rnn.HybridRecurrentCell"> |
| <em class="property">class </em><code class="sig-name descname">HybridRecurrentCell</code><a class="reference internal" href="../../../_modules/mxnet/gluon/rnn/rnn_cell.html#HybridRecurrentCell"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.gluon.rnn.HybridRecurrentCell" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">mxnet.gluon.rnn.rnn_cell.RecurrentCell</span></code>, <code class="xref py py-class docutils literal notranslate"><span class="pre">mxnet.gluon.block.HybridBlock</span></code></p> |
| <p>HybridRecurrentCell supports hybridize.</p> |
| <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.rnn.HybridRecurrentCell.apply" title="mxnet.gluon.rnn.HybridRecurrentCell.apply"><code class="xref py py-obj docutils literal notranslate"><span class="pre">apply</span></code></a>(fn)</p></td> |
| <td><p>Applies <code class="docutils literal notranslate"><span class="pre">fn</span></code> recursively to every child block as well as self.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.HybridRecurrentCell.begin_state" title="mxnet.gluon.rnn.HybridRecurrentCell.begin_state"><code class="xref py py-obj docutils literal notranslate"><span class="pre">begin_state</span></code></a>([batch_size, func])</p></td> |
| <td><p>Initial state for this cell.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.HybridRecurrentCell.cast" title="mxnet.gluon.rnn.HybridRecurrentCell.cast"><code class="xref py py-obj docutils literal notranslate"><span class="pre">cast</span></code></a>(dtype)</p></td> |
| <td><p>Cast this Block to use another data type.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.HybridRecurrentCell.collect_params" title="mxnet.gluon.rnn.HybridRecurrentCell.collect_params"><code class="xref py py-obj docutils literal notranslate"><span class="pre">collect_params</span></code></a>([select])</p></td> |
| <td><p>Returns a <code class="xref py py-class docutils literal notranslate"><span class="pre">Dict</span></code> containing this <code class="xref py py-class docutils literal notranslate"><span class="pre">Block</span></code> and all of its children’s Parameters(default), also can returns the select <code class="xref py py-class docutils literal notranslate"><span class="pre">Dict</span></code> which match some given regular expressions.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.HybridRecurrentCell.export" title="mxnet.gluon.rnn.HybridRecurrentCell.export"><code class="xref py py-obj docutils literal notranslate"><span class="pre">export</span></code></a>(path[, epoch, remove_amp_cast])</p></td> |
| <td><p>Export HybridBlock to json format that can be loaded by <cite>gluon.SymbolBlock.imports</cite> or the C++ interface.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.HybridRecurrentCell.forward" title="mxnet.gluon.rnn.HybridRecurrentCell.forward"><code class="xref py py-obj docutils literal notranslate"><span class="pre">forward</span></code></a>(x, *args, **kwargs)</p></td> |
| <td><p>Unrolls the recurrent cell for one time step.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.HybridRecurrentCell.hybridize" title="mxnet.gluon.rnn.HybridRecurrentCell.hybridize"><code class="xref py py-obj docutils literal notranslate"><span class="pre">hybridize</span></code></a>([active])</p></td> |
| <td><p>Please refer description of HybridBlock hybridize().</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.HybridRecurrentCell.infer_shape" title="mxnet.gluon.rnn.HybridRecurrentCell.infer_shape"><code class="xref py py-obj docutils literal notranslate"><span class="pre">infer_shape</span></code></a>(*args)</p></td> |
| <td><p>Infers shape of Parameters from inputs.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.HybridRecurrentCell.infer_type" title="mxnet.gluon.rnn.HybridRecurrentCell.infer_type"><code class="xref py py-obj docutils literal notranslate"><span class="pre">infer_type</span></code></a>(*args)</p></td> |
| <td><p>Infers data type of Parameters from inputs.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.HybridRecurrentCell.initialize" title="mxnet.gluon.rnn.HybridRecurrentCell.initialize"><code class="xref py py-obj docutils literal notranslate"><span class="pre">initialize</span></code></a>([init, device, verbose, force_reinit])</p></td> |
| <td><p>Initializes <code class="xref py py-class docutils literal notranslate"><span class="pre">Parameter</span></code> s of this <code class="xref py py-class docutils literal notranslate"><span class="pre">Block</span></code> and its children.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.HybridRecurrentCell.load" title="mxnet.gluon.rnn.HybridRecurrentCell.load"><code class="xref py py-obj docutils literal notranslate"><span class="pre">load</span></code></a>(prefix)</p></td> |
| <td><p>Load a model saved using the <cite>save</cite> API</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.HybridRecurrentCell.load_dict" title="mxnet.gluon.rnn.HybridRecurrentCell.load_dict"><code class="xref py py-obj docutils literal notranslate"><span class="pre">load_dict</span></code></a>(param_dict[, device, …])</p></td> |
| <td><p>Load parameters from dict</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.HybridRecurrentCell.load_parameters" title="mxnet.gluon.rnn.HybridRecurrentCell.load_parameters"><code class="xref py py-obj docutils literal notranslate"><span class="pre">load_parameters</span></code></a>(filename[, device, …])</p></td> |
| <td><p>Load parameters from file previously saved by <cite>save_parameters</cite>.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.HybridRecurrentCell.optimize_for" title="mxnet.gluon.rnn.HybridRecurrentCell.optimize_for"><code class="xref py py-obj docutils literal notranslate"><span class="pre">optimize_for</span></code></a>(x, *args[, backend, clear, …])</p></td> |
| <td><p>Partitions the current HybridBlock and optimizes it for a given backend without executing a forward pass.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.HybridRecurrentCell.register_child" title="mxnet.gluon.rnn.HybridRecurrentCell.register_child"><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_child</span></code></a>(block[, name])</p></td> |
| <td><p>Registers block as a child of self.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.HybridRecurrentCell.register_forward_hook" title="mxnet.gluon.rnn.HybridRecurrentCell.register_forward_hook"><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_forward_hook</span></code></a>(hook)</p></td> |
| <td><p>Registers a forward hook on the block.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.HybridRecurrentCell.register_forward_pre_hook" title="mxnet.gluon.rnn.HybridRecurrentCell.register_forward_pre_hook"><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_forward_pre_hook</span></code></a>(hook)</p></td> |
| <td><p>Registers a forward pre-hook on the block.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.HybridRecurrentCell.register_op_hook" title="mxnet.gluon.rnn.HybridRecurrentCell.register_op_hook"><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_op_hook</span></code></a>(callback[, monitor_all])</p></td> |
| <td><p>Install callback monitor.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.HybridRecurrentCell.reset" title="mxnet.gluon.rnn.HybridRecurrentCell.reset"><code class="xref py py-obj docutils literal notranslate"><span class="pre">reset</span></code></a>()</p></td> |
| <td><p>Reset before re-using the cell for another graph.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.HybridRecurrentCell.reset_ctx" title="mxnet.gluon.rnn.HybridRecurrentCell.reset_ctx"><code class="xref py py-obj docutils literal notranslate"><span class="pre">reset_ctx</span></code></a>(ctx)</p></td> |
| <td><p>This function has been deprecated.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.HybridRecurrentCell.reset_device" title="mxnet.gluon.rnn.HybridRecurrentCell.reset_device"><code class="xref py py-obj docutils literal notranslate"><span class="pre">reset_device</span></code></a>(device)</p></td> |
| <td><p>Re-assign all Parameters to other devices.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.HybridRecurrentCell.save" title="mxnet.gluon.rnn.HybridRecurrentCell.save"><code class="xref py py-obj docutils literal notranslate"><span class="pre">save</span></code></a>(prefix)</p></td> |
| <td><p>Save the model architecture and parameters to load again later</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.HybridRecurrentCell.save_parameters" title="mxnet.gluon.rnn.HybridRecurrentCell.save_parameters"><code class="xref py py-obj docutils literal notranslate"><span class="pre">save_parameters</span></code></a>(filename[, deduplicate])</p></td> |
| <td><p>Save parameters to file.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.HybridRecurrentCell.setattr" title="mxnet.gluon.rnn.HybridRecurrentCell.setattr"><code class="xref py py-obj docutils literal notranslate"><span class="pre">setattr</span></code></a>(name, value)</p></td> |
| <td><p>Set an attribute to a new value for all Parameters.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.HybridRecurrentCell.share_parameters" title="mxnet.gluon.rnn.HybridRecurrentCell.share_parameters"><code class="xref py py-obj docutils literal notranslate"><span class="pre">share_parameters</span></code></a>(shared)</p></td> |
| <td><p>Share parameters recursively inside the model.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.HybridRecurrentCell.state_info" title="mxnet.gluon.rnn.HybridRecurrentCell.state_info"><code class="xref py py-obj docutils literal notranslate"><span class="pre">state_info</span></code></a>([batch_size])</p></td> |
| <td><p>shape and layout information of states</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.HybridRecurrentCell.summary" title="mxnet.gluon.rnn.HybridRecurrentCell.summary"><code class="xref py py-obj docutils literal notranslate"><span class="pre">summary</span></code></a>(*inputs)</p></td> |
| <td><p>Print the summary of the model’s output and parameters.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.HybridRecurrentCell.unroll" title="mxnet.gluon.rnn.HybridRecurrentCell.unroll"><code class="xref py py-obj docutils literal notranslate"><span class="pre">unroll</span></code></a>(length, inputs[, begin_state, …])</p></td> |
| <td><p>Unrolls an RNN cell across time steps.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.HybridRecurrentCell.zero_grad" title="mxnet.gluon.rnn.HybridRecurrentCell.zero_grad"><code class="xref py py-obj docutils literal notranslate"><span class="pre">zero_grad</span></code></a>()</p></td> |
| <td><p>Sets all Parameters’ gradient buffer to 0.</p></td> |
| </tr> |
| </tbody> |
| </table> |
| <p><strong>Attributes</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.rnn.HybridRecurrentCell.params" title="mxnet.gluon.rnn.HybridRecurrentCell.params"><code class="xref py py-obj docutils literal notranslate"><span class="pre">params</span></code></a></p></td> |
| <td><p>Returns this <code class="xref py py-class docutils literal notranslate"><span class="pre">Block</span></code>’s parameter dictionary (does not include its children’s parameters).</p></td> |
| </tr> |
| </tbody> |
| </table> |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.HybridRecurrentCell.apply"> |
| <code class="sig-name descname">apply</code><span class="sig-paren">(</span><em class="sig-param">fn</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.rnn.HybridRecurrentCell.apply" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Applies <code class="docutils literal notranslate"><span class="pre">fn</span></code> recursively to every child block as well as self.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><p><strong>fn</strong> (<em>callable</em>) – Function to be applied to each submodule, of form <cite>fn(block)</cite>.</p> |
| </dd> |
| <dt class="field-even">Returns</dt> |
| <dd class="field-even"><p></p> |
| </dd> |
| <dt class="field-odd">Return type</dt> |
| <dd class="field-odd"><p>this block</p> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.HybridRecurrentCell.begin_state"> |
| <code class="sig-name descname">begin_state</code><span class="sig-paren">(</span><em class="sig-param">batch_size=0</em>, <em class="sig-param">func=<function zeros></em>, <em class="sig-param">**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.rnn.HybridRecurrentCell.begin_state" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Initial state for this cell.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>func</strong> (<em>callable</em><em>, </em><em>default symbol.zeros</em>) – <p>Function for creating initial state.</p> |
| <p>For Symbol API, func can be <cite>symbol.zeros</cite>, <cite>symbol.uniform</cite>, |
| <cite>symbol.var etc</cite>. Use <cite>symbol.var</cite> if you want to directly |
| feed input as states.</p> |
| <p>For NDArray API, func can be <cite>ndarray.zeros</cite>, <cite>ndarray.ones</cite>, etc.</p> |
| </p></li> |
| <li><p><strong>batch_size</strong> (<em>int</em><em>, </em><em>default 0</em>) – Only required for NDArray API. Size of the batch (‘N’ in layout) |
| dimension of input.</p></li> |
| <li><p><strong>**kwargs</strong> – Additional keyword arguments passed to func. For example |
| <cite>mean</cite>, <cite>std</cite>, <cite>dtype</cite>, etc.</p></li> |
| </ul> |
| </dd> |
| <dt class="field-even">Returns</dt> |
| <dd class="field-even"><p><strong>states</strong> – Starting states for the first RNN step.</p> |
| </dd> |
| <dt class="field-odd">Return type</dt> |
| <dd class="field-odd"><p>nested list of Symbol</p> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.HybridRecurrentCell.cast"> |
| <code class="sig-name descname">cast</code><span class="sig-paren">(</span><em class="sig-param">dtype</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.rnn.HybridRecurrentCell.cast" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Cast this Block to use another data type.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><p><strong>dtype</strong> (<em>str</em><em> or </em><em>numpy.dtype</em>) – The new data type.</p> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.HybridRecurrentCell.collect_params"> |
| <code class="sig-name descname">collect_params</code><span class="sig-paren">(</span><em class="sig-param">select=None</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.rnn.HybridRecurrentCell.collect_params" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Returns a <code class="xref py py-class docutils literal notranslate"><span class="pre">Dict</span></code> containing this <code class="xref py py-class docutils literal notranslate"><span class="pre">Block</span></code> and all of its |
| children’s Parameters(default), also can returns the select <code class="xref py py-class docutils literal notranslate"><span class="pre">Dict</span></code> |
| which match some given regular expressions.</p> |
| <p>For example, collect the specified parameters in [‘conv1.weight’, ‘conv1.bias’, ‘fc.weight’, |
| ‘fc.bias’]:</p> |
| <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">model</span><span class="o">.</span><span class="n">collect_params</span><span class="p">(</span><span class="s1">'conv1.weight|conv1.bias|fc.weight|fc.bias'</span><span class="p">)</span> |
| </pre></div> |
| </div> |
| <p>or collect all parameters whose names end with ‘weight’ or ‘bias’, this can be done |
| using regular expressions:</p> |
| <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">model</span><span class="o">.</span><span class="n">collect_params</span><span class="p">(</span><span class="s1">'.*weight|.*bias'</span><span class="p">)</span> |
| </pre></div> |
| </div> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><p><strong>select</strong> (<em>str</em>) – regular expressions</p> |
| </dd> |
| <dt class="field-even">Returns</dt> |
| <dd class="field-even"><p></p> |
| </dd> |
| <dt class="field-odd">Return type</dt> |
| <dd class="field-odd"><p>The selected <code class="xref py py-class docutils literal notranslate"><span class="pre">Dict</span></code></p> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.HybridRecurrentCell.export"> |
| <code class="sig-name descname">export</code><span class="sig-paren">(</span><em class="sig-param">path</em>, <em class="sig-param">epoch=0</em>, <em class="sig-param">remove_amp_cast=True</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.rnn.HybridRecurrentCell.export" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Export HybridBlock to json format that can be loaded by |
| <cite>gluon.SymbolBlock.imports</cite> or the C++ interface.</p> |
| <div class="admonition note"> |
| <p class="admonition-title">Note</p> |
| <p>When there are only one input, it will have name <cite>data</cite>. When there |
| Are more than one inputs, they will be named as <cite>data0</cite>, <cite>data1</cite>, etc.</p> |
| </div> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>path</strong> (<em>str</em><em> or </em><em>None</em>) – Path to save model. Two files <cite>path-symbol.json</cite> and <cite>path-xxxx.params</cite> |
| will be created, where xxxx is the 4 digits epoch number. |
| If None, do not export to file but return Python Symbol object and |
| corresponding dictionary of parameters.</p></li> |
| <li><p><strong>epoch</strong> (<em>int</em>) – Epoch number of saved model.</p></li> |
| <li><p><strong>remove_amp_cast</strong> (<em>bool</em><em>, </em><em>optional</em>) – Whether to remove the amp_cast and amp_multicast operators, before saving the model.</p></li> |
| </ul> |
| </dd> |
| <dt class="field-even">Returns</dt> |
| <dd class="field-even"><p><ul class="simple"> |
| <li><p><strong>symbol_filename</strong> (<em>str</em>) – Filename to which model symbols were saved, including <cite>path</cite> prefix.</p></li> |
| <li><p><strong>params_filename</strong> (<em>str</em>) – Filename to which model parameters were saved, including <cite>path</cite> prefix.</p></li> |
| </ul> |
| </p> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.HybridRecurrentCell.forward"> |
| <code class="sig-name descname">forward</code><span class="sig-paren">(</span><em class="sig-param">x</em>, <em class="sig-param">*args</em>, <em class="sig-param">**kwargs</em><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/mxnet/gluon/rnn/rnn_cell.html#HybridRecurrentCell.forward"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.gluon.rnn.HybridRecurrentCell.forward" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Unrolls the recurrent cell for one time step.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>inputs</strong> (<em>sym.Variable</em>) – Input symbol, 2D, of shape (batch_size * num_units).</p></li> |
| <li><p><strong>states</strong> (<em>list of sym.Variable</em>) – RNN state from previous step or the output of begin_state().</p></li> |
| </ul> |
| </dd> |
| <dt class="field-even">Returns</dt> |
| <dd class="field-even"><p><ul class="simple"> |
| <li><p><strong>output</strong> (<em>Symbol</em>) – Symbol corresponding to the output from the RNN when unrolling |
| for a single time step.</p></li> |
| <li><p><strong>states</strong> (<em>list of Symbol</em>) – The new state of this RNN after this unrolling. |
| The type of this symbol is same as the output of <cite>begin_state()</cite>. |
| This can be used as an input state to the next time step |
| of this RNN.</p></li> |
| </ul> |
| </p> |
| </dd> |
| </dl> |
| <div class="admonition seealso"> |
| <p class="admonition-title">See also</p> |
| <dl class="simple"> |
| <dt><a class="reference internal" href="#mxnet.gluon.rnn.HybridRecurrentCell.begin_state" title="mxnet.gluon.rnn.HybridRecurrentCell.begin_state"><code class="xref py py-meth docutils literal notranslate"><span class="pre">begin_state()</span></code></a></dt><dd><p>This function can provide the states for the first time step.</p> |
| </dd> |
| <dt><a class="reference internal" href="#mxnet.gluon.rnn.HybridRecurrentCell.unroll" title="mxnet.gluon.rnn.HybridRecurrentCell.unroll"><code class="xref py py-meth docutils literal notranslate"><span class="pre">unroll()</span></code></a></dt><dd><p>This function unrolls an RNN for a given number of (>=1) time steps.</p> |
| </dd> |
| </dl> |
| </div> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.HybridRecurrentCell.hybridize"> |
| <code class="sig-name descname">hybridize</code><span class="sig-paren">(</span><em class="sig-param">active=True</em>, <em class="sig-param">**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.rnn.HybridRecurrentCell.hybridize" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Please refer description of HybridBlock hybridize().</p> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.HybridRecurrentCell.infer_shape"> |
| <code class="sig-name descname">infer_shape</code><span class="sig-paren">(</span><em class="sig-param">*args</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.rnn.HybridRecurrentCell.infer_shape" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Infers shape of Parameters from inputs.</p> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.HybridRecurrentCell.infer_type"> |
| <code class="sig-name descname">infer_type</code><span class="sig-paren">(</span><em class="sig-param">*args</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.rnn.HybridRecurrentCell.infer_type" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Infers data type of Parameters from inputs.</p> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.HybridRecurrentCell.initialize"> |
| <code class="sig-name descname">initialize</code><span class="sig-paren">(</span><em class="sig-param">init=<mxnet.initializer.Uniform object></em>, <em class="sig-param">device=None</em>, <em class="sig-param">verbose=False</em>, <em class="sig-param">force_reinit=False</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.rnn.HybridRecurrentCell.initialize" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Initializes <code class="xref py py-class docutils literal notranslate"><span class="pre">Parameter</span></code> s of this <code class="xref py py-class docutils literal notranslate"><span class="pre">Block</span></code> and its children.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>init</strong> (<a class="reference internal" href="../../initializer/index.html#mxnet.initializer.Initializer" title="mxnet.initializer.Initializer"><em>Initializer</em></a>) – Global default Initializer to be used when <code class="xref py py-meth docutils literal notranslate"><span class="pre">Parameter.init()</span></code> is <code class="docutils literal notranslate"><span class="pre">None</span></code>. |
| Otherwise, <code class="xref py py-meth docutils literal notranslate"><span class="pre">Parameter.init()</span></code> takes precedence.</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>) – Keeps a copy of Parameters on one or many device(s).</p></li> |
| <li><p><strong>verbose</strong> (<em>bool</em><em>, </em><em>default False</em>) – Whether to verbosely print out details on initialization.</p></li> |
| <li><p><strong>force_reinit</strong> (<em>bool</em><em>, </em><em>default False</em>) – Whether to force re-initialization if parameter is already initialized.</p></li> |
| </ul> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.HybridRecurrentCell.load"> |
| <code class="sig-name descname">load</code><span class="sig-paren">(</span><em class="sig-param">prefix</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.rnn.HybridRecurrentCell.load" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Load a model saved using the <cite>save</cite> API</p> |
| <p>Reconfigures a model using the saved configuration. This function |
| does not regenerate the model architecture. It resets each Block’s |
| parameter UUIDs as they were when saved in order to match the names of the |
| saved parameters.</p> |
| <p>This function assumes the Blocks in the model were created in the same |
| order they were when the model was saved. This is because each Block is |
| uniquely identified by Block class name and a unique ID in order (since |
| its an OrderedDict) and uses the unique ID to denote that specific Block.</p> |
| <p>Assumes that the model is created in an identical order every time. |
| If the model is not able to be recreated deterministically do not |
| use this set of APIs to save/load your model.</p> |
| <p>For HybridBlocks, the cached_graph (Symbol & inputs) and settings are |
| restored if it had been hybridized before saving.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><p><strong>prefix</strong> (<em>str</em>) – The prefix to use in filenames for loading this model: |
| <prefix>-model.json and <prefix>-model.params</p> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.HybridRecurrentCell.load_dict"> |
| <code class="sig-name descname">load_dict</code><span class="sig-paren">(</span><em class="sig-param">param_dict</em>, <em class="sig-param">device=None</em>, <em class="sig-param">allow_missing=False</em>, <em class="sig-param">ignore_extra=False</em>, <em class="sig-param">cast_dtype=False</em>, <em class="sig-param">dtype_source='current'</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.rnn.HybridRecurrentCell.load_dict" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Load parameters from dict</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>param_dict</strong> (<em>dict</em>) – Dictionary containing model 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>, </em><em>optional</em>) – Device context on which the memory is allocated. Default is |
| <cite>mxnet.device.current_device()</cite>.</p></li> |
| <li><p><strong>allow_missing</strong> (<em>bool</em><em>, </em><em>default False</em>) – Whether to silently skip loading parameters not represented in the file.</p></li> |
| <li><p><strong>ignore_extra</strong> (<em>bool</em><em>, </em><em>default False</em>) – Whether to silently ignore parameters from the file that are not |
| present in this dict.</p></li> |
| <li><p><strong>cast_dtype</strong> (<em>bool</em><em>, </em><em>default False</em>) – Cast the data type of the NDArray loaded from the checkpoint to the dtype |
| provided by the Parameter if any</p></li> |
| <li><p><strong>dtype_source</strong> (<em>str</em><em>, </em><em>default 'current'</em>) – must be in {‘current’, ‘saved’} |
| Only valid if cast_dtype=True, specify the source of the dtype for casting |
| the parameters</p></li> |
| </ul> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.HybridRecurrentCell.load_parameters"> |
| <code class="sig-name descname">load_parameters</code><span class="sig-paren">(</span><em class="sig-param">filename</em>, <em class="sig-param">device=None</em>, <em class="sig-param">allow_missing=False</em>, <em class="sig-param">ignore_extra=False</em>, <em class="sig-param">cast_dtype=False</em>, <em class="sig-param">dtype_source='current'</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.rnn.HybridRecurrentCell.load_parameters" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Load parameters from file previously saved by <cite>save_parameters</cite>.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>filename</strong> (<em>str</em>) – Path to parameter file.</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><em>, </em><em>default cpu</em><em>(</em><em>)</em>) – Device(s) to initialize loaded parameters on.</p></li> |
| <li><p><strong>allow_missing</strong> (<em>bool</em><em>, </em><em>default False</em>) – Whether to silently skip loading parameters not represents in the file.</p></li> |
| <li><p><strong>ignore_extra</strong> (<em>bool</em><em>, </em><em>default False</em>) – Whether to silently ignore parameters from the file that are not |
| present in this Block.</p></li> |
| <li><p><strong>cast_dtype</strong> (<em>bool</em><em>, </em><em>default False</em>) – Cast the data type of the NDArray loaded from the checkpoint to the dtype |
| provided by the Parameter if any.</p></li> |
| <li><p><strong>dtype_source</strong> (<em>str</em><em>, </em><em>default 'current'</em>) – must be in {‘current’, ‘saved’} |
| Only valid if cast_dtype=True, specify the source of the dtype for casting |
| the parameters</p></li> |
| </ul> |
| </dd> |
| </dl> |
| <p class="rubric">References</p> |
| <p><a class="reference external" href="https://mxnet.apache.org/api/python/docs/tutorials/packages/gluon/blocks/save_load_params.html">Saving and Loading Gluon Models</a></p> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.HybridRecurrentCell.optimize_for"> |
| <code class="sig-name descname">optimize_for</code><span class="sig-paren">(</span><em class="sig-param">x</em>, <em class="sig-param">*args</em>, <em class="sig-param">backend=None</em>, <em class="sig-param">clear=False</em>, <em class="sig-param">partition_if_dynamic=True</em>, <em class="sig-param">static_alloc=False</em>, <em class="sig-param">static_shape=False</em>, <em class="sig-param">inline_limit=2</em>, <em class="sig-param">forward_bulk_size=None</em>, <em class="sig-param">backward_bulk_size=None</em>, <em class="sig-param">**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.rnn.HybridRecurrentCell.optimize_for" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Partitions the current HybridBlock and optimizes it for a given backend |
| without executing a forward pass. Modifies the HybridBlock in-place.</p> |
| <p>Immediately partitions a HybridBlock using the specified backend. Combines |
| the work done in the hybridize API with part of the work done in the forward |
| pass without calling the CachedOp. Can be used in place of hybridize, |
| afterwards <cite>export</cite> can be called or inference can be run. See README.md in |
| example/extensions/lib_subgraph/README.md for more details.</p> |
| <p class="rubric">Examples</p> |
| <p># partition and then export to file |
| block.optimize_for(x, backend=’myPart’) |
| block.export(‘partitioned’)</p> |
| <p># partition and then run inference |
| block.optimize_for(x, backend=’myPart’) |
| block(x)</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>x</strong> (<a class="reference internal" href="../../legacy/ndarray/ndarray.html#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – first input to model</p></li> |
| <li><p><strong>*args</strong> (<a class="reference internal" href="../../legacy/ndarray/ndarray.html#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – other inputs to model</p></li> |
| <li><p><strong>backend</strong> (<em>str</em>) – The name of backend, as registered in <cite>SubgraphBackendRegistry</cite>, default None</p></li> |
| <li><p><strong>backend_opts</strong> (<em>dict of user-specified options to pass to the backend for partitioning</em><em>, </em><em>optional</em>) – Passed on to <cite>PrePartition</cite> and <cite>PostPartition</cite> functions of <cite>SubgraphProperty</cite></p></li> |
| <li><p><strong>clear</strong> (<em>bool</em><em>, </em><em>default False</em>) – clears any previous optimizations</p></li> |
| <li><p><strong>partition_if_dynamic</strong> (<em>bool</em><em>, </em><em>default False</em>) – whether to partition the graph when dynamic shape op exists</p></li> |
| <li><p><strong>static_alloc</strong> (<em>bool</em><em>, </em><em>default False</em>) – Statically allocate memory to improve speed. Memory usage may increase.</p></li> |
| <li><p><strong>static_shape</strong> (<em>bool</em><em>, </em><em>default False</em>) – Optimize for invariant input shapes between iterations. Must also |
| set static_alloc to True. Change of input shapes is still allowed |
| but slower.</p></li> |
| <li><p><strong>inline_limit</strong> (<em>optional int</em><em>, </em><em>default 2</em>) – Maximum number of operators that can be inlined.</p></li> |
| <li><p><strong>forward_bulk_size</strong> (<em>optional int</em><em>, </em><em>default None</em>) – Segment size of bulk execution during forward pass.</p></li> |
| <li><p><strong>backward_bulk_size</strong> (<em>optional int</em><em>, </em><em>default None</em>) – Segment size of bulk execution during backward pass.</p></li> |
| <li><p><strong>**kwargs</strong> (<em>The backend options</em><em>, </em><em>optional</em>) – Passed on to <cite>PrePartition</cite> and <cite>PostPartition</cite> functions of <cite>SubgraphProperty</cite></p></li> |
| </ul> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.HybridRecurrentCell.params"> |
| <em class="property">property </em><code class="sig-name descname">params</code><a class="headerlink" href="#mxnet.gluon.rnn.HybridRecurrentCell.params" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Returns this <code class="xref py py-class docutils literal notranslate"><span class="pre">Block</span></code>’s parameter dictionary (does not include its |
| children’s parameters).</p> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.HybridRecurrentCell.register_child"> |
| <code class="sig-name descname">register_child</code><span class="sig-paren">(</span><em class="sig-param">block</em>, <em class="sig-param">name=None</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.rnn.HybridRecurrentCell.register_child" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Registers block as a child of self. <code class="xref py py-class docutils literal notranslate"><span class="pre">Block</span></code> s assigned to self as |
| attributes will be registered automatically.</p> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.HybridRecurrentCell.register_forward_hook"> |
| <code class="sig-name descname">register_forward_hook</code><span class="sig-paren">(</span><em class="sig-param">hook</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.rnn.HybridRecurrentCell.register_forward_hook" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Registers a forward hook on the block.</p> |
| <p>The hook function is called immediately after <a class="reference internal" href="#mxnet.gluon.rnn.HybridRecurrentCell.forward" title="mxnet.gluon.rnn.HybridRecurrentCell.forward"><code class="xref py py-func docutils literal notranslate"><span class="pre">forward()</span></code></a>. |
| It should not modify the input or output.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><p><strong>hook</strong> (<em>callable</em>) – The forward hook function of form <cite>hook(block, input, output) -> None</cite>.</p> |
| </dd> |
| <dt class="field-even">Returns</dt> |
| <dd class="field-even"><p></p> |
| </dd> |
| <dt class="field-odd">Return type</dt> |
| <dd class="field-odd"><p><code class="xref py py-class docutils literal notranslate"><span class="pre">mxnet.gluon.utils.HookHandle</span></code></p> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.HybridRecurrentCell.register_forward_pre_hook"> |
| <code class="sig-name descname">register_forward_pre_hook</code><span class="sig-paren">(</span><em class="sig-param">hook</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.rnn.HybridRecurrentCell.register_forward_pre_hook" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Registers a forward pre-hook on the block.</p> |
| <p>The hook function is called immediately before <a class="reference internal" href="#mxnet.gluon.rnn.HybridRecurrentCell.forward" title="mxnet.gluon.rnn.HybridRecurrentCell.forward"><code class="xref py py-func docutils literal notranslate"><span class="pre">forward()</span></code></a>. |
| It should not modify the input or output.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><p><strong>hook</strong> (<em>callable</em>) – The forward hook function of form <cite>hook(block, input) -> None</cite>.</p> |
| </dd> |
| <dt class="field-even">Returns</dt> |
| <dd class="field-even"><p></p> |
| </dd> |
| <dt class="field-odd">Return type</dt> |
| <dd class="field-odd"><p><code class="xref py py-class docutils literal notranslate"><span class="pre">mxnet.gluon.utils.HookHandle</span></code></p> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.HybridRecurrentCell.register_op_hook"> |
| <code class="sig-name descname">register_op_hook</code><span class="sig-paren">(</span><em class="sig-param">callback</em>, <em class="sig-param">monitor_all=False</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.rnn.HybridRecurrentCell.register_op_hook" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Install callback monitor.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>callback</strong> (<em>function</em>) – Function called to inspect the values of the intermediate outputs |
| of blocks after hybridization. It takes 3 parameters: |
| name of the tensor being inspected (str) |
| name of the operator producing or consuming that tensor (str) |
| tensor being inspected (NDArray).</p></li> |
| <li><p><strong>monitor_all</strong> (<em>bool</em><em>, </em><em>default False</em>) – If True, monitor both input and output, otherwise monitor output only.</p></li> |
| </ul> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.HybridRecurrentCell.reset"> |
| <code class="sig-name descname">reset</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.rnn.HybridRecurrentCell.reset" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Reset before re-using the cell for another graph.</p> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.HybridRecurrentCell.reset_ctx"> |
| <code class="sig-name descname">reset_ctx</code><span class="sig-paren">(</span><em class="sig-param">ctx</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.rnn.HybridRecurrentCell.reset_ctx" title="Permalink to this definition">¶</a></dt> |
| <dd><p>This function has been deprecated. Please refer to <code class="docutils literal notranslate"><span class="pre">Block.reset_device</span></code>.</p> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.HybridRecurrentCell.reset_device"> |
| <code class="sig-name descname">reset_device</code><span class="sig-paren">(</span><em class="sig-param">device</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.rnn.HybridRecurrentCell.reset_device" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Re-assign all Parameters to other devices.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><p><strong>device</strong> (Device or list of Device, default <code class="xref py py-meth docutils literal notranslate"><span class="pre">device.current_device()</span></code>.) – Assign Parameter to given device. If device is a list of Device, a |
| copy will be made for each device.</p> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.HybridRecurrentCell.save"> |
| <code class="sig-name descname">save</code><span class="sig-paren">(</span><em class="sig-param">prefix</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.rnn.HybridRecurrentCell.save" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Save the model architecture and parameters to load again later</p> |
| <p>Saves the model architecture as a nested dictionary where each Block |
| in the model is a dictionary and its children are sub-dictionaries.</p> |
| <p>Each Block is uniquely identified by Block class name and a unique ID. |
| We save each Block’s parameter UUID to restore later in order to match |
| the saved parameters.</p> |
| <p>Recursively traverses a Block’s children in order (since its an |
| OrderedDict) and uses the unique ID to denote that specific Block.</p> |
| <p>Assumes that the model is created in an identical order every time. |
| If the model is not able to be recreated deterministically do not |
| use this set of APIs to save/load your model.</p> |
| <p>For HybridBlocks, the cached_graph is saved (Symbol & inputs) if |
| it has already been hybridized.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><p><strong>prefix</strong> (<em>str</em>) – The prefix to use in filenames for saving this model: |
| <prefix>-model.json and <prefix>-model.params</p> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.HybridRecurrentCell.save_parameters"> |
| <code class="sig-name descname">save_parameters</code><span class="sig-paren">(</span><em class="sig-param">filename</em>, <em class="sig-param">deduplicate=False</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.rnn.HybridRecurrentCell.save_parameters" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Save parameters to file.</p> |
| <p>Saved parameters can only be loaded with <cite>load_parameters</cite>. Note that this |
| method only saves parameters, not model structure. If you want to save |
| model structures, please use <code class="xref py py-meth docutils literal notranslate"><span class="pre">HybridBlock.export()</span></code>.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>filename</strong> (<em>str</em>) – Path to file.</p></li> |
| <li><p><strong>deduplicate</strong> (<em>bool</em><em>, </em><em>default False</em>) – If True, save shared parameters only once. Otherwise, if a Block |
| contains multiple sub-blocks that share parameters, each of the |
| shared parameters will be separately saved for every sub-block.</p></li> |
| </ul> |
| </dd> |
| </dl> |
| <p class="rubric">References</p> |
| <p><a class="reference external" href="https://mxnet.apache.org/api/python/docs/tutorials/packages/gluon/blocks/save_load_params.html">Saving and Loading Gluon Models</a></p> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.HybridRecurrentCell.setattr"> |
| <code class="sig-name descname">setattr</code><span class="sig-paren">(</span><em class="sig-param">name</em>, <em class="sig-param">value</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.rnn.HybridRecurrentCell.setattr" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Set an attribute to a new value for all Parameters.</p> |
| <p>For example, set grad_req to null if you don’t need gradient w.r.t a |
| model’s Parameters:</p> |
| <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">model</span><span class="o">.</span><span class="n">setattr</span><span class="p">(</span><span class="s1">'grad_req'</span><span class="p">,</span> <span class="s1">'null'</span><span class="p">)</span> |
| </pre></div> |
| </div> |
| <p>or change the learning rate multiplier:</p> |
| <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">model</span><span class="o">.</span><span class="n">setattr</span><span class="p">(</span><span class="s1">'lr_mult'</span><span class="p">,</span> <span class="mf">0.5</span><span class="p">)</span> |
| </pre></div> |
| </div> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>name</strong> (<em>str</em>) – Name of the attribute.</p></li> |
| <li><p><strong>value</strong> (<em>valid type for attribute name</em>) – The new value for the attribute.</p></li> |
| </ul> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.HybridRecurrentCell.share_parameters"> |
| <code class="sig-name descname">share_parameters</code><span class="sig-paren">(</span><em class="sig-param">shared</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.rnn.HybridRecurrentCell.share_parameters" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Share parameters recursively inside the model.</p> |
| <p>For example, if you want <code class="docutils literal notranslate"><span class="pre">dense1</span></code> to share <code class="docutils literal notranslate"><span class="pre">dense0</span></code>’s weights, you can do:</p> |
| <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">dense0</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Dense</span><span class="p">(</span><span class="mi">20</span><span class="p">)</span> |
| <span class="n">dense1</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Dense</span><span class="p">(</span><span class="mi">20</span><span class="p">)</span> |
| <span class="n">dense1</span><span class="o">.</span><span class="n">share_parameters</span><span class="p">(</span><span class="n">dense0</span><span class="o">.</span><span class="n">collect_params</span><span class="p">())</span> |
| </pre></div> |
| </div> |
| <dl class="simple"> |
| <dt>which equals to</dt><dd><p>dense1.weight = dense0.weight |
| dense1.bias = dense0.bias</p> |
| </dd> |
| </dl> |
| <p>Note that unlike the <cite>load_parameters</cite> or <cite>load_dict</cite> functions, |
| <cite>share_parameters</cite> results in the <cite>Parameter</cite> object being shared (or |
| tied) between the models, whereas <cite>load_parameters</cite> or <cite>load_dict</cite> only |
| set the value of the data dictionary of a model. If you call |
| <cite>load_parameters</cite> or <cite>load_dict</cite> after <cite>share_parameters</cite>, the loaded |
| value will be reflected in all networks that use the shared (or tied) |
| <cite>Parameter</cite> object.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><p><strong>shared</strong> (<em>Dict</em>) – Dict of the shared parameters.</p> |
| </dd> |
| <dt class="field-even">Returns</dt> |
| <dd class="field-even"><p></p> |
| </dd> |
| <dt class="field-odd">Return type</dt> |
| <dd class="field-odd"><p>this block</p> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.HybridRecurrentCell.state_info"> |
| <code class="sig-name descname">state_info</code><span class="sig-paren">(</span><em class="sig-param">batch_size=0</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.rnn.HybridRecurrentCell.state_info" title="Permalink to this definition">¶</a></dt> |
| <dd><p>shape and layout information of states</p> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.HybridRecurrentCell.summary"> |
| <code class="sig-name descname">summary</code><span class="sig-paren">(</span><em class="sig-param">*inputs</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.rnn.HybridRecurrentCell.summary" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Print the summary of the model’s output and parameters.</p> |
| <p>The network must have been initialized, and must not have been hybridized.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><p><strong>inputs</strong> (<em>object</em>) – Any input that the model supports. For any tensor in the input, only |
| <a class="reference internal" href="../../legacy/ndarray/ndarray.html#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><code class="xref py py-class docutils literal notranslate"><span class="pre">mxnet.ndarray.NDArray</span></code></a> is supported.</p> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.HybridRecurrentCell.unroll"> |
| <code class="sig-name descname">unroll</code><span class="sig-paren">(</span><em class="sig-param">length</em>, <em class="sig-param">inputs</em>, <em class="sig-param">begin_state=None</em>, <em class="sig-param">layout='NTC'</em>, <em class="sig-param">merge_outputs=None</em>, <em class="sig-param">valid_length=None</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.rnn.HybridRecurrentCell.unroll" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Unrolls an RNN cell across time steps.</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>) – Number of steps to unroll.</p></li> |
| <li><p><strong>inputs</strong> (<a class="reference internal" href="../../legacy/symbol/symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a><em>, </em><em>list of Symbol</em><em>, or </em><em>None</em>) – <p>If <cite>inputs</cite> is a single Symbol (usually the output |
| of Embedding symbol), it should have shape |
| (batch_size, length, …) if <cite>layout</cite> is ‘NTC’, |
| or (length, batch_size, …) if <cite>layout</cite> is ‘TNC’.</p> |
| <p>If <cite>inputs</cite> is a list of symbols (usually output of |
| previous unroll), they should all have shape |
| (batch_size, …).</p> |
| </p></li> |
| <li><p><strong>begin_state</strong> (<em>nested list of Symbol</em><em>, </em><em>optional</em>) – Input states created by <cite>begin_state()</cite> |
| or output state of another cell. |
| Created from <cite>begin_state()</cite> if <cite>None</cite>.</p></li> |
| <li><p><strong>layout</strong> (<em>str</em><em>, </em><em>optional</em>) – <cite>layout</cite> of input symbol. Only used if inputs |
| is a single Symbol.</p></li> |
| <li><p><strong>merge_outputs</strong> (<em>bool</em><em>, </em><em>optional</em>) – If <cite>False</cite>, returns outputs as a list of Symbols. |
| If <cite>True</cite>, concatenates output across time steps |
| and returns a single symbol with shape |
| (batch_size, length, …) if layout is ‘NTC’, |
| or (length, batch_size, …) if layout is ‘TNC’. |
| If <cite>None</cite>, output whatever is faster.</p></li> |
| <li><p><strong>valid_length</strong> (<a class="reference internal" href="../../legacy/symbol/symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a><em>, </em><a class="reference internal" href="../../legacy/ndarray/ndarray.html#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a><em> or </em><em>None</em>) – <cite>valid_length</cite> specifies the length of the sequences in the batch without padding. |
| This option is especially useful for building sequence-to-sequence models where |
| the input and output sequences would potentially be padded. |
| If <cite>valid_length</cite> is None, all sequences are assumed to have the same length. |
| If <cite>valid_length</cite> is a Symbol or NDArray, it should have shape (batch_size,). |
| The ith element will be the length of the ith sequence in the batch. |
| The last valid state will be return and the padded outputs will be masked with 0. |
| Note that <cite>valid_length</cite> must be smaller or equal to <cite>length</cite>.</p></li> |
| </ul> |
| </dd> |
| <dt class="field-even">Returns</dt> |
| <dd class="field-even"><p><ul class="simple"> |
| <li><p><strong>outputs</strong> (<em>list of Symbol or Symbol</em>) – Symbol (if <cite>merge_outputs</cite> is True) or list of Symbols |
| (if <cite>merge_outputs</cite> is False) corresponding to the output from |
| the RNN from this unrolling.</p></li> |
| <li><p><strong>states</strong> (<em>list of Symbol</em>) – The new state of this RNN after this unrolling. |
| The type of this symbol is same as the output of <cite>begin_state()</cite>.</p></li> |
| </ul> |
| </p> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.HybridRecurrentCell.zero_grad"> |
| <code class="sig-name descname">zero_grad</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.rnn.HybridRecurrentCell.zero_grad" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Sets all Parameters’ gradient buffer to 0.</p> |
| </dd></dl> |
| |
| </dd></dl> |
| |
| <dl class="class"> |
| <dt id="mxnet.gluon.rnn.HybridSequentialRNNCell"> |
| <em class="property">class </em><code class="sig-name descname">HybridSequentialRNNCell</code><a class="reference internal" href="../../../_modules/mxnet/gluon/rnn/rnn_cell.html#HybridSequentialRNNCell"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.gluon.rnn.HybridSequentialRNNCell" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">mxnet.gluon.rnn.rnn_cell.HybridRecurrentCell</span></code></p> |
| <p>Sequentially stacking multiple HybridRNN cells.</p> |
| <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.rnn.HybridSequentialRNNCell.add" title="mxnet.gluon.rnn.HybridSequentialRNNCell.add"><code class="xref py py-obj docutils literal notranslate"><span class="pre">add</span></code></a>(cell)</p></td> |
| <td><p>Appends a cell into the stack.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.HybridSequentialRNNCell.apply" title="mxnet.gluon.rnn.HybridSequentialRNNCell.apply"><code class="xref py py-obj docutils literal notranslate"><span class="pre">apply</span></code></a>(fn)</p></td> |
| <td><p>Applies <code class="docutils literal notranslate"><span class="pre">fn</span></code> recursively to every child block as well as self.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.HybridSequentialRNNCell.begin_state" title="mxnet.gluon.rnn.HybridSequentialRNNCell.begin_state"><code class="xref py py-obj docutils literal notranslate"><span class="pre">begin_state</span></code></a>(**kwargs)</p></td> |
| <td><p>Initial state for this cell.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.HybridSequentialRNNCell.cast" title="mxnet.gluon.rnn.HybridSequentialRNNCell.cast"><code class="xref py py-obj docutils literal notranslate"><span class="pre">cast</span></code></a>(dtype)</p></td> |
| <td><p>Cast this Block to use another data type.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.HybridSequentialRNNCell.collect_params" title="mxnet.gluon.rnn.HybridSequentialRNNCell.collect_params"><code class="xref py py-obj docutils literal notranslate"><span class="pre">collect_params</span></code></a>([select])</p></td> |
| <td><p>Returns a <code class="xref py py-class docutils literal notranslate"><span class="pre">Dict</span></code> containing this <code class="xref py py-class docutils literal notranslate"><span class="pre">Block</span></code> and all of its children’s Parameters(default), also can returns the select <code class="xref py py-class docutils literal notranslate"><span class="pre">Dict</span></code> which match some given regular expressions.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.HybridSequentialRNNCell.export" title="mxnet.gluon.rnn.HybridSequentialRNNCell.export"><code class="xref py py-obj docutils literal notranslate"><span class="pre">export</span></code></a>(path[, epoch, remove_amp_cast])</p></td> |
| <td><p>Export HybridBlock to json format that can be loaded by <cite>gluon.SymbolBlock.imports</cite> or the C++ interface.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.HybridSequentialRNNCell.forward" title="mxnet.gluon.rnn.HybridSequentialRNNCell.forward"><code class="xref py py-obj docutils literal notranslate"><span class="pre">forward</span></code></a>(inputs, states)</p></td> |
| <td><p>Unrolls the recurrent cell for one time step.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.HybridSequentialRNNCell.hybridize" title="mxnet.gluon.rnn.HybridSequentialRNNCell.hybridize"><code class="xref py py-obj docutils literal notranslate"><span class="pre">hybridize</span></code></a>([active])</p></td> |
| <td><p>Please refer description of HybridBlock hybridize().</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.HybridSequentialRNNCell.infer_shape" title="mxnet.gluon.rnn.HybridSequentialRNNCell.infer_shape"><code class="xref py py-obj docutils literal notranslate"><span class="pre">infer_shape</span></code></a>(_, x, is_bidirect)</p></td> |
| <td><p>Infers shape of Parameters from inputs.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.HybridSequentialRNNCell.infer_type" title="mxnet.gluon.rnn.HybridSequentialRNNCell.infer_type"><code class="xref py py-obj docutils literal notranslate"><span class="pre">infer_type</span></code></a>(*args)</p></td> |
| <td><p>Infers data type of Parameters from inputs.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.HybridSequentialRNNCell.initialize" title="mxnet.gluon.rnn.HybridSequentialRNNCell.initialize"><code class="xref py py-obj docutils literal notranslate"><span class="pre">initialize</span></code></a>([init, device, verbose, force_reinit])</p></td> |
| <td><p>Initializes <code class="xref py py-class docutils literal notranslate"><span class="pre">Parameter</span></code> s of this <code class="xref py py-class docutils literal notranslate"><span class="pre">Block</span></code> and its children.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.HybridSequentialRNNCell.load" title="mxnet.gluon.rnn.HybridSequentialRNNCell.load"><code class="xref py py-obj docutils literal notranslate"><span class="pre">load</span></code></a>(prefix)</p></td> |
| <td><p>Load a model saved using the <cite>save</cite> API</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.HybridSequentialRNNCell.load_dict" title="mxnet.gluon.rnn.HybridSequentialRNNCell.load_dict"><code class="xref py py-obj docutils literal notranslate"><span class="pre">load_dict</span></code></a>(param_dict[, device, …])</p></td> |
| <td><p>Load parameters from dict</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.HybridSequentialRNNCell.load_parameters" title="mxnet.gluon.rnn.HybridSequentialRNNCell.load_parameters"><code class="xref py py-obj docutils literal notranslate"><span class="pre">load_parameters</span></code></a>(filename[, device, …])</p></td> |
| <td><p>Load parameters from file previously saved by <cite>save_parameters</cite>.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.HybridSequentialRNNCell.optimize_for" title="mxnet.gluon.rnn.HybridSequentialRNNCell.optimize_for"><code class="xref py py-obj docutils literal notranslate"><span class="pre">optimize_for</span></code></a>(x, *args[, backend, clear, …])</p></td> |
| <td><p>Partitions the current HybridBlock and optimizes it for a given backend without executing a forward pass.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.HybridSequentialRNNCell.register_child" title="mxnet.gluon.rnn.HybridSequentialRNNCell.register_child"><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_child</span></code></a>(block[, name])</p></td> |
| <td><p>Registers block as a child of self.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.HybridSequentialRNNCell.register_forward_hook" title="mxnet.gluon.rnn.HybridSequentialRNNCell.register_forward_hook"><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_forward_hook</span></code></a>(hook)</p></td> |
| <td><p>Registers a forward hook on the block.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.HybridSequentialRNNCell.register_forward_pre_hook" title="mxnet.gluon.rnn.HybridSequentialRNNCell.register_forward_pre_hook"><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_forward_pre_hook</span></code></a>(hook)</p></td> |
| <td><p>Registers a forward pre-hook on the block.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.HybridSequentialRNNCell.register_op_hook" title="mxnet.gluon.rnn.HybridSequentialRNNCell.register_op_hook"><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_op_hook</span></code></a>(callback[, monitor_all])</p></td> |
| <td><p>Install callback monitor.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.HybridSequentialRNNCell.reset" title="mxnet.gluon.rnn.HybridSequentialRNNCell.reset"><code class="xref py py-obj docutils literal notranslate"><span class="pre">reset</span></code></a>()</p></td> |
| <td><p>Reset before re-using the cell for another graph.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.HybridSequentialRNNCell.reset_ctx" title="mxnet.gluon.rnn.HybridSequentialRNNCell.reset_ctx"><code class="xref py py-obj docutils literal notranslate"><span class="pre">reset_ctx</span></code></a>(ctx)</p></td> |
| <td><p>This function has been deprecated.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.HybridSequentialRNNCell.reset_device" title="mxnet.gluon.rnn.HybridSequentialRNNCell.reset_device"><code class="xref py py-obj docutils literal notranslate"><span class="pre">reset_device</span></code></a>(device)</p></td> |
| <td><p>Re-assign all Parameters to other devices.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.HybridSequentialRNNCell.save" title="mxnet.gluon.rnn.HybridSequentialRNNCell.save"><code class="xref py py-obj docutils literal notranslate"><span class="pre">save</span></code></a>(prefix)</p></td> |
| <td><p>Save the model architecture and parameters to load again later</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.HybridSequentialRNNCell.save_parameters" title="mxnet.gluon.rnn.HybridSequentialRNNCell.save_parameters"><code class="xref py py-obj docutils literal notranslate"><span class="pre">save_parameters</span></code></a>(filename[, deduplicate])</p></td> |
| <td><p>Save parameters to file.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.HybridSequentialRNNCell.setattr" title="mxnet.gluon.rnn.HybridSequentialRNNCell.setattr"><code class="xref py py-obj docutils literal notranslate"><span class="pre">setattr</span></code></a>(name, value)</p></td> |
| <td><p>Set an attribute to a new value for all Parameters.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.HybridSequentialRNNCell.share_parameters" title="mxnet.gluon.rnn.HybridSequentialRNNCell.share_parameters"><code class="xref py py-obj docutils literal notranslate"><span class="pre">share_parameters</span></code></a>(shared)</p></td> |
| <td><p>Share parameters recursively inside the model.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.HybridSequentialRNNCell.state_info" title="mxnet.gluon.rnn.HybridSequentialRNNCell.state_info"><code class="xref py py-obj docutils literal notranslate"><span class="pre">state_info</span></code></a>([batch_size])</p></td> |
| <td><p>shape and layout information of states</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.HybridSequentialRNNCell.summary" title="mxnet.gluon.rnn.HybridSequentialRNNCell.summary"><code class="xref py py-obj docutils literal notranslate"><span class="pre">summary</span></code></a>(*inputs)</p></td> |
| <td><p>Print the summary of the model’s output and parameters.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.HybridSequentialRNNCell.unroll" title="mxnet.gluon.rnn.HybridSequentialRNNCell.unroll"><code class="xref py py-obj docutils literal notranslate"><span class="pre">unroll</span></code></a>(length, inputs[, begin_state, …])</p></td> |
| <td><p>Unrolls an RNN cell across time steps.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.HybridSequentialRNNCell.zero_grad" title="mxnet.gluon.rnn.HybridSequentialRNNCell.zero_grad"><code class="xref py py-obj docutils literal notranslate"><span class="pre">zero_grad</span></code></a>()</p></td> |
| <td><p>Sets all Parameters’ gradient buffer to 0.</p></td> |
| </tr> |
| </tbody> |
| </table> |
| <p><strong>Attributes</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.rnn.HybridSequentialRNNCell.params" title="mxnet.gluon.rnn.HybridSequentialRNNCell.params"><code class="xref py py-obj docutils literal notranslate"><span class="pre">params</span></code></a></p></td> |
| <td><p>Returns this <code class="xref py py-class docutils literal notranslate"><span class="pre">Block</span></code>’s parameter dictionary (does not include its children’s parameters).</p></td> |
| </tr> |
| </tbody> |
| </table> |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.HybridSequentialRNNCell.add"> |
| <code class="sig-name descname">add</code><span class="sig-paren">(</span><em class="sig-param">cell</em><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/mxnet/gluon/rnn/rnn_cell.html#HybridSequentialRNNCell.add"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.gluon.rnn.HybridSequentialRNNCell.add" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Appends a cell into the stack.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><p><strong>cell</strong> (<a class="reference internal" href="#mxnet.gluon.rnn.RecurrentCell" title="mxnet.gluon.rnn.RecurrentCell"><em>RecurrentCell</em></a>) – The cell to add.</p> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.HybridSequentialRNNCell.apply"> |
| <code class="sig-name descname">apply</code><span class="sig-paren">(</span><em class="sig-param">fn</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.rnn.HybridSequentialRNNCell.apply" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Applies <code class="docutils literal notranslate"><span class="pre">fn</span></code> recursively to every child block as well as self.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><p><strong>fn</strong> (<em>callable</em>) – Function to be applied to each submodule, of form <cite>fn(block)</cite>.</p> |
| </dd> |
| <dt class="field-even">Returns</dt> |
| <dd class="field-even"><p></p> |
| </dd> |
| <dt class="field-odd">Return type</dt> |
| <dd class="field-odd"><p>this block</p> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.HybridSequentialRNNCell.begin_state"> |
| <code class="sig-name descname">begin_state</code><span class="sig-paren">(</span><em class="sig-param">**kwargs</em><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/mxnet/gluon/rnn/rnn_cell.html#HybridSequentialRNNCell.begin_state"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.gluon.rnn.HybridSequentialRNNCell.begin_state" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Initial state for this cell.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>func</strong> (<em>callable</em><em>, </em><em>default symbol.zeros</em>) – <p>Function for creating initial state.</p> |
| <p>For Symbol API, func can be <cite>symbol.zeros</cite>, <cite>symbol.uniform</cite>, |
| <cite>symbol.var etc</cite>. Use <cite>symbol.var</cite> if you want to directly |
| feed input as states.</p> |
| <p>For NDArray API, func can be <cite>ndarray.zeros</cite>, <cite>ndarray.ones</cite>, etc.</p> |
| </p></li> |
| <li><p><strong>batch_size</strong> (<em>int</em><em>, </em><em>default 0</em>) – Only required for NDArray API. Size of the batch (‘N’ in layout) |
| dimension of input.</p></li> |
| <li><p><strong>**kwargs</strong> – Additional keyword arguments passed to func. For example |
| <cite>mean</cite>, <cite>std</cite>, <cite>dtype</cite>, etc.</p></li> |
| </ul> |
| </dd> |
| <dt class="field-even">Returns</dt> |
| <dd class="field-even"><p><strong>states</strong> – Starting states for the first RNN step.</p> |
| </dd> |
| <dt class="field-odd">Return type</dt> |
| <dd class="field-odd"><p>nested list of Symbol</p> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.HybridSequentialRNNCell.cast"> |
| <code class="sig-name descname">cast</code><span class="sig-paren">(</span><em class="sig-param">dtype</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.rnn.HybridSequentialRNNCell.cast" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Cast this Block to use another data type.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><p><strong>dtype</strong> (<em>str</em><em> or </em><em>numpy.dtype</em>) – The new data type.</p> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.HybridSequentialRNNCell.collect_params"> |
| <code class="sig-name descname">collect_params</code><span class="sig-paren">(</span><em class="sig-param">select=None</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.rnn.HybridSequentialRNNCell.collect_params" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Returns a <code class="xref py py-class docutils literal notranslate"><span class="pre">Dict</span></code> containing this <code class="xref py py-class docutils literal notranslate"><span class="pre">Block</span></code> and all of its |
| children’s Parameters(default), also can returns the select <code class="xref py py-class docutils literal notranslate"><span class="pre">Dict</span></code> |
| which match some given regular expressions.</p> |
| <p>For example, collect the specified parameters in [‘conv1.weight’, ‘conv1.bias’, ‘fc.weight’, |
| ‘fc.bias’]:</p> |
| <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">model</span><span class="o">.</span><span class="n">collect_params</span><span class="p">(</span><span class="s1">'conv1.weight|conv1.bias|fc.weight|fc.bias'</span><span class="p">)</span> |
| </pre></div> |
| </div> |
| <p>or collect all parameters whose names end with ‘weight’ or ‘bias’, this can be done |
| using regular expressions:</p> |
| <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">model</span><span class="o">.</span><span class="n">collect_params</span><span class="p">(</span><span class="s1">'.*weight|.*bias'</span><span class="p">)</span> |
| </pre></div> |
| </div> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><p><strong>select</strong> (<em>str</em>) – regular expressions</p> |
| </dd> |
| <dt class="field-even">Returns</dt> |
| <dd class="field-even"><p></p> |
| </dd> |
| <dt class="field-odd">Return type</dt> |
| <dd class="field-odd"><p>The selected <code class="xref py py-class docutils literal notranslate"><span class="pre">Dict</span></code></p> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.HybridSequentialRNNCell.export"> |
| <code class="sig-name descname">export</code><span class="sig-paren">(</span><em class="sig-param">path</em>, <em class="sig-param">epoch=0</em>, <em class="sig-param">remove_amp_cast=True</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.rnn.HybridSequentialRNNCell.export" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Export HybridBlock to json format that can be loaded by |
| <cite>gluon.SymbolBlock.imports</cite> or the C++ interface.</p> |
| <div class="admonition note"> |
| <p class="admonition-title">Note</p> |
| <p>When there are only one input, it will have name <cite>data</cite>. When there |
| Are more than one inputs, they will be named as <cite>data0</cite>, <cite>data1</cite>, etc.</p> |
| </div> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>path</strong> (<em>str</em><em> or </em><em>None</em>) – Path to save model. Two files <cite>path-symbol.json</cite> and <cite>path-xxxx.params</cite> |
| will be created, where xxxx is the 4 digits epoch number. |
| If None, do not export to file but return Python Symbol object and |
| corresponding dictionary of parameters.</p></li> |
| <li><p><strong>epoch</strong> (<em>int</em>) – Epoch number of saved model.</p></li> |
| <li><p><strong>remove_amp_cast</strong> (<em>bool</em><em>, </em><em>optional</em>) – Whether to remove the amp_cast and amp_multicast operators, before saving the model.</p></li> |
| </ul> |
| </dd> |
| <dt class="field-even">Returns</dt> |
| <dd class="field-even"><p><ul class="simple"> |
| <li><p><strong>symbol_filename</strong> (<em>str</em>) – Filename to which model symbols were saved, including <cite>path</cite> prefix.</p></li> |
| <li><p><strong>params_filename</strong> (<em>str</em>) – Filename to which model parameters were saved, including <cite>path</cite> prefix.</p></li> |
| </ul> |
| </p> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.HybridSequentialRNNCell.forward"> |
| <code class="sig-name descname">forward</code><span class="sig-paren">(</span><em class="sig-param">inputs</em>, <em class="sig-param">states</em><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/mxnet/gluon/rnn/rnn_cell.html#HybridSequentialRNNCell.forward"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.gluon.rnn.HybridSequentialRNNCell.forward" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Unrolls the recurrent cell for one time step.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>inputs</strong> (<em>sym.Variable</em>) – Input symbol, 2D, of shape (batch_size * num_units).</p></li> |
| <li><p><strong>states</strong> (<em>list of sym.Variable</em>) – RNN state from previous step or the output of begin_state().</p></li> |
| </ul> |
| </dd> |
| <dt class="field-even">Returns</dt> |
| <dd class="field-even"><p><ul class="simple"> |
| <li><p><strong>output</strong> (<em>Symbol</em>) – Symbol corresponding to the output from the RNN when unrolling |
| for a single time step.</p></li> |
| <li><p><strong>states</strong> (<em>list of Symbol</em>) – The new state of this RNN after this unrolling. |
| The type of this symbol is same as the output of <cite>begin_state()</cite>. |
| This can be used as an input state to the next time step |
| of this RNN.</p></li> |
| </ul> |
| </p> |
| </dd> |
| </dl> |
| <div class="admonition seealso"> |
| <p class="admonition-title">See also</p> |
| <dl class="simple"> |
| <dt><a class="reference internal" href="#mxnet.gluon.rnn.HybridSequentialRNNCell.begin_state" title="mxnet.gluon.rnn.HybridSequentialRNNCell.begin_state"><code class="xref py py-meth docutils literal notranslate"><span class="pre">begin_state()</span></code></a></dt><dd><p>This function can provide the states for the first time step.</p> |
| </dd> |
| <dt><a class="reference internal" href="#mxnet.gluon.rnn.HybridSequentialRNNCell.unroll" title="mxnet.gluon.rnn.HybridSequentialRNNCell.unroll"><code class="xref py py-meth docutils literal notranslate"><span class="pre">unroll()</span></code></a></dt><dd><p>This function unrolls an RNN for a given number of (>=1) time steps.</p> |
| </dd> |
| </dl> |
| </div> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.HybridSequentialRNNCell.hybridize"> |
| <code class="sig-name descname">hybridize</code><span class="sig-paren">(</span><em class="sig-param">active=True</em>, <em class="sig-param">**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.rnn.HybridSequentialRNNCell.hybridize" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Please refer description of HybridBlock hybridize().</p> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.HybridSequentialRNNCell.infer_shape"> |
| <code class="sig-name descname">infer_shape</code><span class="sig-paren">(</span><em class="sig-param">_</em>, <em class="sig-param">x</em>, <em class="sig-param">is_bidirect</em><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/mxnet/gluon/rnn/rnn_cell.html#HybridSequentialRNNCell.infer_shape"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.gluon.rnn.HybridSequentialRNNCell.infer_shape" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Infers shape of Parameters from inputs.</p> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.HybridSequentialRNNCell.infer_type"> |
| <code class="sig-name descname">infer_type</code><span class="sig-paren">(</span><em class="sig-param">*args</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.rnn.HybridSequentialRNNCell.infer_type" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Infers data type of Parameters from inputs.</p> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.HybridSequentialRNNCell.initialize"> |
| <code class="sig-name descname">initialize</code><span class="sig-paren">(</span><em class="sig-param">init=<mxnet.initializer.Uniform object></em>, <em class="sig-param">device=None</em>, <em class="sig-param">verbose=False</em>, <em class="sig-param">force_reinit=False</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.rnn.HybridSequentialRNNCell.initialize" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Initializes <code class="xref py py-class docutils literal notranslate"><span class="pre">Parameter</span></code> s of this <code class="xref py py-class docutils literal notranslate"><span class="pre">Block</span></code> and its children.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>init</strong> (<a class="reference internal" href="../../initializer/index.html#mxnet.initializer.Initializer" title="mxnet.initializer.Initializer"><em>Initializer</em></a>) – Global default Initializer to be used when <code class="xref py py-meth docutils literal notranslate"><span class="pre">Parameter.init()</span></code> is <code class="docutils literal notranslate"><span class="pre">None</span></code>. |
| Otherwise, <code class="xref py py-meth docutils literal notranslate"><span class="pre">Parameter.init()</span></code> takes precedence.</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>) – Keeps a copy of Parameters on one or many device(s).</p></li> |
| <li><p><strong>verbose</strong> (<em>bool</em><em>, </em><em>default False</em>) – Whether to verbosely print out details on initialization.</p></li> |
| <li><p><strong>force_reinit</strong> (<em>bool</em><em>, </em><em>default False</em>) – Whether to force re-initialization if parameter is already initialized.</p></li> |
| </ul> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.HybridSequentialRNNCell.load"> |
| <code class="sig-name descname">load</code><span class="sig-paren">(</span><em class="sig-param">prefix</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.rnn.HybridSequentialRNNCell.load" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Load a model saved using the <cite>save</cite> API</p> |
| <p>Reconfigures a model using the saved configuration. This function |
| does not regenerate the model architecture. It resets each Block’s |
| parameter UUIDs as they were when saved in order to match the names of the |
| saved parameters.</p> |
| <p>This function assumes the Blocks in the model were created in the same |
| order they were when the model was saved. This is because each Block is |
| uniquely identified by Block class name and a unique ID in order (since |
| its an OrderedDict) and uses the unique ID to denote that specific Block.</p> |
| <p>Assumes that the model is created in an identical order every time. |
| If the model is not able to be recreated deterministically do not |
| use this set of APIs to save/load your model.</p> |
| <p>For HybridBlocks, the cached_graph (Symbol & inputs) and settings are |
| restored if it had been hybridized before saving.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><p><strong>prefix</strong> (<em>str</em>) – The prefix to use in filenames for loading this model: |
| <prefix>-model.json and <prefix>-model.params</p> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.HybridSequentialRNNCell.load_dict"> |
| <code class="sig-name descname">load_dict</code><span class="sig-paren">(</span><em class="sig-param">param_dict</em>, <em class="sig-param">device=None</em>, <em class="sig-param">allow_missing=False</em>, <em class="sig-param">ignore_extra=False</em>, <em class="sig-param">cast_dtype=False</em>, <em class="sig-param">dtype_source='current'</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.rnn.HybridSequentialRNNCell.load_dict" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Load parameters from dict</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>param_dict</strong> (<em>dict</em>) – Dictionary containing model 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>, </em><em>optional</em>) – Device context on which the memory is allocated. Default is |
| <cite>mxnet.device.current_device()</cite>.</p></li> |
| <li><p><strong>allow_missing</strong> (<em>bool</em><em>, </em><em>default False</em>) – Whether to silently skip loading parameters not represented in the file.</p></li> |
| <li><p><strong>ignore_extra</strong> (<em>bool</em><em>, </em><em>default False</em>) – Whether to silently ignore parameters from the file that are not |
| present in this dict.</p></li> |
| <li><p><strong>cast_dtype</strong> (<em>bool</em><em>, </em><em>default False</em>) – Cast the data type of the NDArray loaded from the checkpoint to the dtype |
| provided by the Parameter if any</p></li> |
| <li><p><strong>dtype_source</strong> (<em>str</em><em>, </em><em>default 'current'</em>) – must be in {‘current’, ‘saved’} |
| Only valid if cast_dtype=True, specify the source of the dtype for casting |
| the parameters</p></li> |
| </ul> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.HybridSequentialRNNCell.load_parameters"> |
| <code class="sig-name descname">load_parameters</code><span class="sig-paren">(</span><em class="sig-param">filename</em>, <em class="sig-param">device=None</em>, <em class="sig-param">allow_missing=False</em>, <em class="sig-param">ignore_extra=False</em>, <em class="sig-param">cast_dtype=False</em>, <em class="sig-param">dtype_source='current'</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.rnn.HybridSequentialRNNCell.load_parameters" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Load parameters from file previously saved by <cite>save_parameters</cite>.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>filename</strong> (<em>str</em>) – Path to parameter file.</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><em>, </em><em>default cpu</em><em>(</em><em>)</em>) – Device(s) to initialize loaded parameters on.</p></li> |
| <li><p><strong>allow_missing</strong> (<em>bool</em><em>, </em><em>default False</em>) – Whether to silently skip loading parameters not represents in the file.</p></li> |
| <li><p><strong>ignore_extra</strong> (<em>bool</em><em>, </em><em>default False</em>) – Whether to silently ignore parameters from the file that are not |
| present in this Block.</p></li> |
| <li><p><strong>cast_dtype</strong> (<em>bool</em><em>, </em><em>default False</em>) – Cast the data type of the NDArray loaded from the checkpoint to the dtype |
| provided by the Parameter if any.</p></li> |
| <li><p><strong>dtype_source</strong> (<em>str</em><em>, </em><em>default 'current'</em>) – must be in {‘current’, ‘saved’} |
| Only valid if cast_dtype=True, specify the source of the dtype for casting |
| the parameters</p></li> |
| </ul> |
| </dd> |
| </dl> |
| <p class="rubric">References</p> |
| <p><a class="reference external" href="https://mxnet.apache.org/api/python/docs/tutorials/packages/gluon/blocks/save_load_params.html">Saving and Loading Gluon Models</a></p> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.HybridSequentialRNNCell.optimize_for"> |
| <code class="sig-name descname">optimize_for</code><span class="sig-paren">(</span><em class="sig-param">x</em>, <em class="sig-param">*args</em>, <em class="sig-param">backend=None</em>, <em class="sig-param">clear=False</em>, <em class="sig-param">partition_if_dynamic=True</em>, <em class="sig-param">static_alloc=False</em>, <em class="sig-param">static_shape=False</em>, <em class="sig-param">inline_limit=2</em>, <em class="sig-param">forward_bulk_size=None</em>, <em class="sig-param">backward_bulk_size=None</em>, <em class="sig-param">**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.rnn.HybridSequentialRNNCell.optimize_for" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Partitions the current HybridBlock and optimizes it for a given backend |
| without executing a forward pass. Modifies the HybridBlock in-place.</p> |
| <p>Immediately partitions a HybridBlock using the specified backend. Combines |
| the work done in the hybridize API with part of the work done in the forward |
| pass without calling the CachedOp. Can be used in place of hybridize, |
| afterwards <cite>export</cite> can be called or inference can be run. See README.md in |
| example/extensions/lib_subgraph/README.md for more details.</p> |
| <p class="rubric">Examples</p> |
| <p># partition and then export to file |
| block.optimize_for(x, backend=’myPart’) |
| block.export(‘partitioned’)</p> |
| <p># partition and then run inference |
| block.optimize_for(x, backend=’myPart’) |
| block(x)</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>x</strong> (<a class="reference internal" href="../../legacy/ndarray/ndarray.html#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – first input to model</p></li> |
| <li><p><strong>*args</strong> (<a class="reference internal" href="../../legacy/ndarray/ndarray.html#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – other inputs to model</p></li> |
| <li><p><strong>backend</strong> (<em>str</em>) – The name of backend, as registered in <cite>SubgraphBackendRegistry</cite>, default None</p></li> |
| <li><p><strong>backend_opts</strong> (<em>dict of user-specified options to pass to the backend for partitioning</em><em>, </em><em>optional</em>) – Passed on to <cite>PrePartition</cite> and <cite>PostPartition</cite> functions of <cite>SubgraphProperty</cite></p></li> |
| <li><p><strong>clear</strong> (<em>bool</em><em>, </em><em>default False</em>) – clears any previous optimizations</p></li> |
| <li><p><strong>partition_if_dynamic</strong> (<em>bool</em><em>, </em><em>default False</em>) – whether to partition the graph when dynamic shape op exists</p></li> |
| <li><p><strong>static_alloc</strong> (<em>bool</em><em>, </em><em>default False</em>) – Statically allocate memory to improve speed. Memory usage may increase.</p></li> |
| <li><p><strong>static_shape</strong> (<em>bool</em><em>, </em><em>default False</em>) – Optimize for invariant input shapes between iterations. Must also |
| set static_alloc to True. Change of input shapes is still allowed |
| but slower.</p></li> |
| <li><p><strong>inline_limit</strong> (<em>optional int</em><em>, </em><em>default 2</em>) – Maximum number of operators that can be inlined.</p></li> |
| <li><p><strong>forward_bulk_size</strong> (<em>optional int</em><em>, </em><em>default None</em>) – Segment size of bulk execution during forward pass.</p></li> |
| <li><p><strong>backward_bulk_size</strong> (<em>optional int</em><em>, </em><em>default None</em>) – Segment size of bulk execution during backward pass.</p></li> |
| <li><p><strong>**kwargs</strong> (<em>The backend options</em><em>, </em><em>optional</em>) – Passed on to <cite>PrePartition</cite> and <cite>PostPartition</cite> functions of <cite>SubgraphProperty</cite></p></li> |
| </ul> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.HybridSequentialRNNCell.params"> |
| <em class="property">property </em><code class="sig-name descname">params</code><a class="headerlink" href="#mxnet.gluon.rnn.HybridSequentialRNNCell.params" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Returns this <code class="xref py py-class docutils literal notranslate"><span class="pre">Block</span></code>’s parameter dictionary (does not include its |
| children’s parameters).</p> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.HybridSequentialRNNCell.register_child"> |
| <code class="sig-name descname">register_child</code><span class="sig-paren">(</span><em class="sig-param">block</em>, <em class="sig-param">name=None</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.rnn.HybridSequentialRNNCell.register_child" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Registers block as a child of self. <code class="xref py py-class docutils literal notranslate"><span class="pre">Block</span></code> s assigned to self as |
| attributes will be registered automatically.</p> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.HybridSequentialRNNCell.register_forward_hook"> |
| <code class="sig-name descname">register_forward_hook</code><span class="sig-paren">(</span><em class="sig-param">hook</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.rnn.HybridSequentialRNNCell.register_forward_hook" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Registers a forward hook on the block.</p> |
| <p>The hook function is called immediately after <a class="reference internal" href="#mxnet.gluon.rnn.HybridSequentialRNNCell.forward" title="mxnet.gluon.rnn.HybridSequentialRNNCell.forward"><code class="xref py py-func docutils literal notranslate"><span class="pre">forward()</span></code></a>. |
| It should not modify the input or output.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><p><strong>hook</strong> (<em>callable</em>) – The forward hook function of form <cite>hook(block, input, output) -> None</cite>.</p> |
| </dd> |
| <dt class="field-even">Returns</dt> |
| <dd class="field-even"><p></p> |
| </dd> |
| <dt class="field-odd">Return type</dt> |
| <dd class="field-odd"><p><code class="xref py py-class docutils literal notranslate"><span class="pre">mxnet.gluon.utils.HookHandle</span></code></p> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.HybridSequentialRNNCell.register_forward_pre_hook"> |
| <code class="sig-name descname">register_forward_pre_hook</code><span class="sig-paren">(</span><em class="sig-param">hook</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.rnn.HybridSequentialRNNCell.register_forward_pre_hook" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Registers a forward pre-hook on the block.</p> |
| <p>The hook function is called immediately before <a class="reference internal" href="#mxnet.gluon.rnn.HybridSequentialRNNCell.forward" title="mxnet.gluon.rnn.HybridSequentialRNNCell.forward"><code class="xref py py-func docutils literal notranslate"><span class="pre">forward()</span></code></a>. |
| It should not modify the input or output.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><p><strong>hook</strong> (<em>callable</em>) – The forward hook function of form <cite>hook(block, input) -> None</cite>.</p> |
| </dd> |
| <dt class="field-even">Returns</dt> |
| <dd class="field-even"><p></p> |
| </dd> |
| <dt class="field-odd">Return type</dt> |
| <dd class="field-odd"><p><code class="xref py py-class docutils literal notranslate"><span class="pre">mxnet.gluon.utils.HookHandle</span></code></p> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.HybridSequentialRNNCell.register_op_hook"> |
| <code class="sig-name descname">register_op_hook</code><span class="sig-paren">(</span><em class="sig-param">callback</em>, <em class="sig-param">monitor_all=False</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.rnn.HybridSequentialRNNCell.register_op_hook" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Install callback monitor.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>callback</strong> (<em>function</em>) – Function called to inspect the values of the intermediate outputs |
| of blocks after hybridization. It takes 3 parameters: |
| name of the tensor being inspected (str) |
| name of the operator producing or consuming that tensor (str) |
| tensor being inspected (NDArray).</p></li> |
| <li><p><strong>monitor_all</strong> (<em>bool</em><em>, </em><em>default False</em>) – If True, monitor both input and output, otherwise monitor output only.</p></li> |
| </ul> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.HybridSequentialRNNCell.reset"> |
| <code class="sig-name descname">reset</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.rnn.HybridSequentialRNNCell.reset" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Reset before re-using the cell for another graph.</p> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.HybridSequentialRNNCell.reset_ctx"> |
| <code class="sig-name descname">reset_ctx</code><span class="sig-paren">(</span><em class="sig-param">ctx</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.rnn.HybridSequentialRNNCell.reset_ctx" title="Permalink to this definition">¶</a></dt> |
| <dd><p>This function has been deprecated. Please refer to <code class="docutils literal notranslate"><span class="pre">Block.reset_device</span></code>.</p> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.HybridSequentialRNNCell.reset_device"> |
| <code class="sig-name descname">reset_device</code><span class="sig-paren">(</span><em class="sig-param">device</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.rnn.HybridSequentialRNNCell.reset_device" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Re-assign all Parameters to other devices.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><p><strong>device</strong> (Device or list of Device, default <code class="xref py py-meth docutils literal notranslate"><span class="pre">device.current_device()</span></code>.) – Assign Parameter to given device. If device is a list of Device, a |
| copy will be made for each device.</p> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.HybridSequentialRNNCell.save"> |
| <code class="sig-name descname">save</code><span class="sig-paren">(</span><em class="sig-param">prefix</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.rnn.HybridSequentialRNNCell.save" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Save the model architecture and parameters to load again later</p> |
| <p>Saves the model architecture as a nested dictionary where each Block |
| in the model is a dictionary and its children are sub-dictionaries.</p> |
| <p>Each Block is uniquely identified by Block class name and a unique ID. |
| We save each Block’s parameter UUID to restore later in order to match |
| the saved parameters.</p> |
| <p>Recursively traverses a Block’s children in order (since its an |
| OrderedDict) and uses the unique ID to denote that specific Block.</p> |
| <p>Assumes that the model is created in an identical order every time. |
| If the model is not able to be recreated deterministically do not |
| use this set of APIs to save/load your model.</p> |
| <p>For HybridBlocks, the cached_graph is saved (Symbol & inputs) if |
| it has already been hybridized.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><p><strong>prefix</strong> (<em>str</em>) – The prefix to use in filenames for saving this model: |
| <prefix>-model.json and <prefix>-model.params</p> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.HybridSequentialRNNCell.save_parameters"> |
| <code class="sig-name descname">save_parameters</code><span class="sig-paren">(</span><em class="sig-param">filename</em>, <em class="sig-param">deduplicate=False</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.rnn.HybridSequentialRNNCell.save_parameters" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Save parameters to file.</p> |
| <p>Saved parameters can only be loaded with <cite>load_parameters</cite>. Note that this |
| method only saves parameters, not model structure. If you want to save |
| model structures, please use <code class="xref py py-meth docutils literal notranslate"><span class="pre">HybridBlock.export()</span></code>.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>filename</strong> (<em>str</em>) – Path to file.</p></li> |
| <li><p><strong>deduplicate</strong> (<em>bool</em><em>, </em><em>default False</em>) – If True, save shared parameters only once. Otherwise, if a Block |
| contains multiple sub-blocks that share parameters, each of the |
| shared parameters will be separately saved for every sub-block.</p></li> |
| </ul> |
| </dd> |
| </dl> |
| <p class="rubric">References</p> |
| <p><a class="reference external" href="https://mxnet.apache.org/api/python/docs/tutorials/packages/gluon/blocks/save_load_params.html">Saving and Loading Gluon Models</a></p> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.HybridSequentialRNNCell.setattr"> |
| <code class="sig-name descname">setattr</code><span class="sig-paren">(</span><em class="sig-param">name</em>, <em class="sig-param">value</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.rnn.HybridSequentialRNNCell.setattr" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Set an attribute to a new value for all Parameters.</p> |
| <p>For example, set grad_req to null if you don’t need gradient w.r.t a |
| model’s Parameters:</p> |
| <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">model</span><span class="o">.</span><span class="n">setattr</span><span class="p">(</span><span class="s1">'grad_req'</span><span class="p">,</span> <span class="s1">'null'</span><span class="p">)</span> |
| </pre></div> |
| </div> |
| <p>or change the learning rate multiplier:</p> |
| <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">model</span><span class="o">.</span><span class="n">setattr</span><span class="p">(</span><span class="s1">'lr_mult'</span><span class="p">,</span> <span class="mf">0.5</span><span class="p">)</span> |
| </pre></div> |
| </div> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>name</strong> (<em>str</em>) – Name of the attribute.</p></li> |
| <li><p><strong>value</strong> (<em>valid type for attribute name</em>) – The new value for the attribute.</p></li> |
| </ul> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.HybridSequentialRNNCell.share_parameters"> |
| <code class="sig-name descname">share_parameters</code><span class="sig-paren">(</span><em class="sig-param">shared</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.rnn.HybridSequentialRNNCell.share_parameters" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Share parameters recursively inside the model.</p> |
| <p>For example, if you want <code class="docutils literal notranslate"><span class="pre">dense1</span></code> to share <code class="docutils literal notranslate"><span class="pre">dense0</span></code>’s weights, you can do:</p> |
| <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">dense0</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Dense</span><span class="p">(</span><span class="mi">20</span><span class="p">)</span> |
| <span class="n">dense1</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Dense</span><span class="p">(</span><span class="mi">20</span><span class="p">)</span> |
| <span class="n">dense1</span><span class="o">.</span><span class="n">share_parameters</span><span class="p">(</span><span class="n">dense0</span><span class="o">.</span><span class="n">collect_params</span><span class="p">())</span> |
| </pre></div> |
| </div> |
| <dl class="simple"> |
| <dt>which equals to</dt><dd><p>dense1.weight = dense0.weight |
| dense1.bias = dense0.bias</p> |
| </dd> |
| </dl> |
| <p>Note that unlike the <cite>load_parameters</cite> or <cite>load_dict</cite> functions, |
| <cite>share_parameters</cite> results in the <cite>Parameter</cite> object being shared (or |
| tied) between the models, whereas <cite>load_parameters</cite> or <cite>load_dict</cite> only |
| set the value of the data dictionary of a model. If you call |
| <cite>load_parameters</cite> or <cite>load_dict</cite> after <cite>share_parameters</cite>, the loaded |
| value will be reflected in all networks that use the shared (or tied) |
| <cite>Parameter</cite> object.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><p><strong>shared</strong> (<em>Dict</em>) – Dict of the shared parameters.</p> |
| </dd> |
| <dt class="field-even">Returns</dt> |
| <dd class="field-even"><p></p> |
| </dd> |
| <dt class="field-odd">Return type</dt> |
| <dd class="field-odd"><p>this block</p> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.HybridSequentialRNNCell.state_info"> |
| <code class="sig-name descname">state_info</code><span class="sig-paren">(</span><em class="sig-param">batch_size=0</em><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/mxnet/gluon/rnn/rnn_cell.html#HybridSequentialRNNCell.state_info"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.gluon.rnn.HybridSequentialRNNCell.state_info" title="Permalink to this definition">¶</a></dt> |
| <dd><p>shape and layout information of states</p> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.HybridSequentialRNNCell.summary"> |
| <code class="sig-name descname">summary</code><span class="sig-paren">(</span><em class="sig-param">*inputs</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.rnn.HybridSequentialRNNCell.summary" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Print the summary of the model’s output and parameters.</p> |
| <p>The network must have been initialized, and must not have been hybridized.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><p><strong>inputs</strong> (<em>object</em>) – Any input that the model supports. For any tensor in the input, only |
| <a class="reference internal" href="../../legacy/ndarray/ndarray.html#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><code class="xref py py-class docutils literal notranslate"><span class="pre">mxnet.ndarray.NDArray</span></code></a> is supported.</p> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.HybridSequentialRNNCell.unroll"> |
| <code class="sig-name descname">unroll</code><span class="sig-paren">(</span><em class="sig-param">length</em>, <em class="sig-param">inputs</em>, <em class="sig-param">begin_state=None</em>, <em class="sig-param">layout='NTC'</em>, <em class="sig-param">merge_outputs=None</em>, <em class="sig-param">valid_length=None</em><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/mxnet/gluon/rnn/rnn_cell.html#HybridSequentialRNNCell.unroll"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.gluon.rnn.HybridSequentialRNNCell.unroll" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Unrolls an RNN cell across time steps.</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>) – Number of steps to unroll.</p></li> |
| <li><p><strong>inputs</strong> (<a class="reference internal" href="../../legacy/symbol/symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a><em>, </em><em>list of Symbol</em><em>, or </em><em>None</em>) – <p>If <cite>inputs</cite> is a single Symbol (usually the output |
| of Embedding symbol), it should have shape |
| (batch_size, length, …) if <cite>layout</cite> is ‘NTC’, |
| or (length, batch_size, …) if <cite>layout</cite> is ‘TNC’.</p> |
| <p>If <cite>inputs</cite> is a list of symbols (usually output of |
| previous unroll), they should all have shape |
| (batch_size, …).</p> |
| </p></li> |
| <li><p><strong>begin_state</strong> (<em>nested list of Symbol</em><em>, </em><em>optional</em>) – Input states created by <cite>begin_state()</cite> |
| or output state of another cell. |
| Created from <cite>begin_state()</cite> if <cite>None</cite>.</p></li> |
| <li><p><strong>layout</strong> (<em>str</em><em>, </em><em>optional</em>) – <cite>layout</cite> of input symbol. Only used if inputs |
| is a single Symbol.</p></li> |
| <li><p><strong>merge_outputs</strong> (<em>bool</em><em>, </em><em>optional</em>) – If <cite>False</cite>, returns outputs as a list of Symbols. |
| If <cite>True</cite>, concatenates output across time steps |
| and returns a single symbol with shape |
| (batch_size, length, …) if layout is ‘NTC’, |
| or (length, batch_size, …) if layout is ‘TNC’. |
| If <cite>None</cite>, output whatever is faster.</p></li> |
| <li><p><strong>valid_length</strong> (<a class="reference internal" href="../../legacy/symbol/symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a><em>, </em><a class="reference internal" href="../../legacy/ndarray/ndarray.html#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a><em> or </em><em>None</em>) – <cite>valid_length</cite> specifies the length of the sequences in the batch without padding. |
| This option is especially useful for building sequence-to-sequence models where |
| the input and output sequences would potentially be padded. |
| If <cite>valid_length</cite> is None, all sequences are assumed to have the same length. |
| If <cite>valid_length</cite> is a Symbol or NDArray, it should have shape (batch_size,). |
| The ith element will be the length of the ith sequence in the batch. |
| The last valid state will be return and the padded outputs will be masked with 0. |
| Note that <cite>valid_length</cite> must be smaller or equal to <cite>length</cite>.</p></li> |
| </ul> |
| </dd> |
| <dt class="field-even">Returns</dt> |
| <dd class="field-even"><p><ul class="simple"> |
| <li><p><strong>outputs</strong> (<em>list of Symbol or Symbol</em>) – Symbol (if <cite>merge_outputs</cite> is True) or list of Symbols |
| (if <cite>merge_outputs</cite> is False) corresponding to the output from |
| the RNN from this unrolling.</p></li> |
| <li><p><strong>states</strong> (<em>list of Symbol</em>) – The new state of this RNN after this unrolling. |
| The type of this symbol is same as the output of <cite>begin_state()</cite>.</p></li> |
| </ul> |
| </p> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.HybridSequentialRNNCell.zero_grad"> |
| <code class="sig-name descname">zero_grad</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.rnn.HybridSequentialRNNCell.zero_grad" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Sets all Parameters’ gradient buffer to 0.</p> |
| </dd></dl> |
| |
| </dd></dl> |
| |
| <dl class="class"> |
| <dt id="mxnet.gluon.rnn.LSTM"> |
| <em class="property">class </em><code class="sig-name descname">LSTM</code><span class="sig-paren">(</span><em class="sig-param">hidden_size</em>, <em class="sig-param">num_layers=1</em>, <em class="sig-param">layout='TNC'</em>, <em class="sig-param">dropout=0</em>, <em class="sig-param">bidirectional=False</em>, <em class="sig-param">input_size=0</em>, <em class="sig-param">i2h_weight_initializer=None</em>, <em class="sig-param">h2h_weight_initializer=None</em>, <em class="sig-param">i2h_bias_initializer='zeros'</em>, <em class="sig-param">h2h_bias_initializer='zeros'</em>, <em class="sig-param">projection_size=None</em>, <em class="sig-param">h2r_weight_initializer=None</em>, <em class="sig-param">state_clip_min=None</em>, <em class="sig-param">state_clip_max=None</em>, <em class="sig-param">state_clip_nan=False</em>, <em class="sig-param">dtype='float32'</em>, <em class="sig-param">**kwargs</em><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/mxnet/gluon/rnn/rnn_layer.html#LSTM"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.gluon.rnn.LSTM" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">mxnet.gluon.rnn.rnn_layer._RNNLayer</span></code></p> |
| <p>Applies a multi-layer long short-term memory (LSTM) RNN to an input sequence.</p> |
| <p>For each element in the input sequence, each layer computes the following |
| function:</p> |
| <div class="math notranslate nohighlight"> |
| \[\begin{split}\begin{array}{ll} |
| i_t = sigmoid(W_{ii} x_t + b_{ii} + W_{hi} h_{(t-1)} + b_{hi}) \\ |
| f_t = sigmoid(W_{if} x_t + b_{if} + W_{hf} h_{(t-1)} + b_{hf}) \\ |
| g_t = \tanh(W_{ig} x_t + b_{ig} + W_{hc} h_{(t-1)} + b_{hg}) \\ |
| o_t = sigmoid(W_{io} x_t + b_{io} + W_{ho} h_{(t-1)} + b_{ho}) \\ |
| c_t = f_t * c_{(t-1)} + i_t * g_t \\ |
| h_t = o_t * \tanh(c_t) |
| \end{array}\end{split}\]</div> |
| <p>where <span class="math notranslate nohighlight">\(h_t\)</span> is the hidden state at time <cite>t</cite>, <span class="math notranslate nohighlight">\(c_t\)</span> is the |
| cell state at time <cite>t</cite>, <span class="math notranslate nohighlight">\(x_t\)</span> is the hidden state of the previous |
| layer at time <cite>t</cite> or <span class="math notranslate nohighlight">\(input_t\)</span> for the first layer, and <span class="math notranslate nohighlight">\(i_t\)</span>, |
| <span class="math notranslate nohighlight">\(f_t\)</span>, <span class="math notranslate nohighlight">\(g_t\)</span>, <span class="math notranslate nohighlight">\(o_t\)</span> are the input, forget, cell, and |
| out gates, respectively.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>hidden_size</strong> (<em>int</em>) – The number of features in the hidden state h.</p></li> |
| <li><p><strong>num_layers</strong> (<em>int</em><em>, </em><em>default 1</em>) – Number of recurrent layers.</p></li> |
| <li><p><strong>layout</strong> (<em>str</em><em>, </em><em>default 'TNC'</em>) – The format of input and output tensors. T, N and C stand for |
| sequence length, batch size, and feature dimensions respectively.</p></li> |
| <li><p><strong>dropout</strong> (<em>float</em><em>, </em><em>default 0</em>) – If non-zero, introduces a dropout layer on the outputs of each |
| RNN layer except the last layer.</p></li> |
| <li><p><strong>bidirectional</strong> (<em>bool</em><em>, </em><em>default False</em>) – If <cite>True</cite>, becomes a bidirectional RNN.</p></li> |
| <li><p><strong>i2h_weight_initializer</strong> (<em>str</em><em> or </em><a class="reference internal" href="../../initializer/index.html#mxnet.initializer.Initializer" title="mxnet.initializer.Initializer"><em>Initializer</em></a>) – Initializer for the input weights matrix, used for the linear |
| transformation of the inputs.</p></li> |
| <li><p><strong>h2h_weight_initializer</strong> (<em>str</em><em> or </em><a class="reference internal" href="../../initializer/index.html#mxnet.initializer.Initializer" title="mxnet.initializer.Initializer"><em>Initializer</em></a>) – Initializer for the recurrent weights matrix, used for the linear |
| transformation of the recurrent state.</p></li> |
| <li><p><strong>i2h_bias_initializer</strong> (<em>str</em><em> or </em><a class="reference internal" href="../../initializer/index.html#mxnet.initializer.Initializer" title="mxnet.initializer.Initializer"><em>Initializer</em></a><em>, </em><em>default 'lstmbias'</em>) – Initializer for the bias vector. By default, bias for the forget |
| gate is initialized to 1 while all other biases are initialized |
| to zero.</p></li> |
| <li><p><strong>h2h_bias_initializer</strong> (<em>str</em><em> or </em><a class="reference internal" href="../../initializer/index.html#mxnet.initializer.Initializer" title="mxnet.initializer.Initializer"><em>Initializer</em></a>) – Initializer for the bias vector.</p></li> |
| <li><p><strong>projection_size</strong> (<em>int</em><em>, </em><em>default None</em>) – The number of features after projection.</p></li> |
| <li><p><strong>h2r_weight_initializer</strong> (<em>str</em><em> or </em><a class="reference internal" href="../../initializer/index.html#mxnet.initializer.Initializer" title="mxnet.initializer.Initializer"><em>Initializer</em></a><em>, </em><em>default None</em>) – Initializer for the projected recurrent weights matrix, used for the linear |
| transformation of the recurrent state to the projected space.</p></li> |
| <li><p><strong>state_clip_min</strong> (<em>float</em><em> or </em><em>None</em><em>, </em><em>default None</em>) – Minimum clip value of LSTM states. This option must be used together with |
| state_clip_max. If None, clipping is not applied.</p></li> |
| <li><p><strong>state_clip_max</strong> (<em>float</em><em> or </em><em>None</em><em>, </em><em>default None</em>) – Maximum clip value of LSTM states. This option must be used together with |
| state_clip_min. If None, clipping is not applied.</p></li> |
| <li><p><strong>state_clip_nan</strong> (<em>boolean</em><em>, </em><em>default False</em>) – Whether to stop NaN from propagating in state by clipping it to min/max. |
| If the clipping range is not specified, this option is ignored.</p></li> |
| <li><p><strong>dtype</strong> (<em>str</em><em>, </em><em>default 'float32'</em>) – Type to initialize the parameters and default states to</p></li> |
| <li><p><strong>input_size</strong> (<em>int</em><em>, </em><em>default 0</em>) – The number of expected features in the input x. |
| If not specified, it will be inferred from input.</p></li> |
| </ul> |
| </dd> |
| </dl> |
| <dl class="simple"> |
| <dt>Inputs:</dt><dd><ul class="simple"> |
| <li><p><strong>data</strong>: input tensor with shape <cite>(sequence_length, batch_size, input_size)</cite> |
| when <cite>layout</cite> is “TNC”. For other layouts, dimensions are permuted accordingly |
| using transpose() operator which adds performance overhead. Consider creating |
| batches in TNC layout during data batching step.</p></li> |
| <li><p><strong>states</strong>: a list of two initial recurrent state tensors. Each has shape |
| <cite>(num_layers, batch_size, num_hidden)</cite>. If <cite>bidirectional</cite> is True, |
| shape will instead be <cite>(2*num_layers, batch_size, num_hidden)</cite>. If |
| <cite>states</cite> is None, zeros will be used as default begin states.</p></li> |
| </ul> |
| </dd> |
| <dt>Outputs:</dt><dd><ul class="simple"> |
| <li><p><strong>out</strong>: output tensor with shape <cite>(sequence_length, batch_size, num_hidden)</cite> |
| when <cite>layout</cite> is “TNC”. If <cite>bidirectional</cite> is True, output shape will instead |
| be <cite>(sequence_length, batch_size, 2*num_hidden)</cite></p></li> |
| <li><p><strong>out_states</strong>: a list of two output recurrent state tensors with the same |
| shape as in <cite>states</cite>. If <cite>states</cite> is None <cite>out_states</cite> will not be returned.</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">layer</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">gluon</span><span class="o">.</span><span class="n">rnn</span><span class="o">.</span><span class="n">LSTM</span><span class="p">(</span><span class="mi">100</span><span class="p">,</span> <span class="mi">3</span><span class="p">)</span> |
| <span class="gp">>>> </span><span class="n">layer</span><span class="o">.</span><span class="n">initialize</span><span class="p">()</span> |
| <span class="gp">>>> </span><span class="nb">input</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">uniform</span><span class="p">(</span><span class="n">size</span><span class="o">=</span><span class="p">(</span><span class="mi">5</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">10</span><span class="p">))</span> |
| <span class="gp">>>> </span><span class="c1"># by default zeros are used as begin state</span> |
| <span class="gp">>>> </span><span class="n">output</span> <span class="o">=</span> <span class="n">layer</span><span class="p">(</span><span class="nb">input</span><span class="p">)</span> |
| <span class="gp">>>> </span><span class="c1"># manually specify begin state.</span> |
| <span class="gp">>>> </span><span class="n">h0</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">uniform</span><span class="p">(</span><span class="n">size</span><span class="o">=</span><span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">100</span><span class="p">))</span> |
| <span class="gp">>>> </span><span class="n">c0</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">uniform</span><span class="p">(</span><span class="n">size</span><span class="o">=</span><span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">100</span><span class="p">))</span> |
| <span class="gp">>>> </span><span class="n">output</span><span class="p">,</span> <span class="n">hn</span> <span class="o">=</span> <span class="n">layer</span><span class="p">(</span><span class="nb">input</span><span class="p">,</span> <span class="p">[</span><span class="n">h0</span><span class="p">,</span> <span class="n">c0</span><span class="p">])</span> |
| </pre></div> |
| </div> |
| </dd></dl> |
| |
| <dl class="class"> |
| <dt id="mxnet.gluon.rnn.LSTMCell"> |
| <em class="property">class </em><code class="sig-name descname">LSTMCell</code><span class="sig-paren">(</span><em class="sig-param">hidden_size</em>, <em class="sig-param">i2h_weight_initializer=None</em>, <em class="sig-param">h2h_weight_initializer=None</em>, <em class="sig-param">i2h_bias_initializer='zeros'</em>, <em class="sig-param">h2h_bias_initializer='zeros'</em>, <em class="sig-param">input_size=0</em>, <em class="sig-param">activation='tanh'</em>, <em class="sig-param">recurrent_activation='sigmoid'</em><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/mxnet/gluon/rnn/rnn_cell.html#LSTMCell"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.gluon.rnn.LSTMCell" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">mxnet.gluon.rnn.rnn_cell.HybridRecurrentCell</span></code></p> |
| <p>Long-Short Term Memory (LSTM) network cell.</p> |
| <p>Each call computes the following function:</p> |
| <div class="math notranslate nohighlight"> |
| \[\begin{split}\begin{array}{ll} |
| i_t = sigmoid(W_{ii} x_t + b_{ii} + W_{hi} h_{(t-1)} + b_{hi}) \\ |
| f_t = sigmoid(W_{if} x_t + b_{if} + W_{hf} h_{(t-1)} + b_{hf}) \\ |
| g_t = \tanh(W_{ig} x_t + b_{ig} + W_{hc} h_{(t-1)} + b_{hg}) \\ |
| o_t = sigmoid(W_{io} x_t + b_{io} + W_{ho} h_{(t-1)} + b_{ho}) \\ |
| c_t = f_t * c_{(t-1)} + i_t * g_t \\ |
| h_t = o_t * \tanh(c_t) |
| \end{array}\end{split}\]</div> |
| <p><strong>Methods</strong></p> |
| <table class="longtable docutils align-default"> |
| <colgroup> |
| <col style="width: 10%" /> |
| <col style="width: 90%" /> |
| </colgroup> |
| <tbody> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.LSTMCell.apply" title="mxnet.gluon.rnn.LSTMCell.apply"><code class="xref py py-obj docutils literal notranslate"><span class="pre">apply</span></code></a>(fn)</p></td> |
| <td><p>Applies <code class="docutils literal notranslate"><span class="pre">fn</span></code> recursively to every child block as well as self.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.LSTMCell.begin_state" title="mxnet.gluon.rnn.LSTMCell.begin_state"><code class="xref py py-obj docutils literal notranslate"><span class="pre">begin_state</span></code></a>([batch_size, func])</p></td> |
| <td><p>Initial state for this cell.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.LSTMCell.cast" title="mxnet.gluon.rnn.LSTMCell.cast"><code class="xref py py-obj docutils literal notranslate"><span class="pre">cast</span></code></a>(dtype)</p></td> |
| <td><p>Cast this Block to use another data type.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.LSTMCell.collect_params" title="mxnet.gluon.rnn.LSTMCell.collect_params"><code class="xref py py-obj docutils literal notranslate"><span class="pre">collect_params</span></code></a>([select])</p></td> |
| <td><p>Returns a <code class="xref py py-class docutils literal notranslate"><span class="pre">Dict</span></code> containing this <code class="xref py py-class docutils literal notranslate"><span class="pre">Block</span></code> and all of its children’s Parameters(default), also can returns the select <code class="xref py py-class docutils literal notranslate"><span class="pre">Dict</span></code> which match some given regular expressions.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.LSTMCell.export" title="mxnet.gluon.rnn.LSTMCell.export"><code class="xref py py-obj docutils literal notranslate"><span class="pre">export</span></code></a>(path[, epoch, remove_amp_cast])</p></td> |
| <td><p>Export HybridBlock to json format that can be loaded by <cite>gluon.SymbolBlock.imports</cite> or the C++ interface.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.LSTMCell.forward" title="mxnet.gluon.rnn.LSTMCell.forward"><code class="xref py py-obj docutils literal notranslate"><span class="pre">forward</span></code></a>(inputs, states)</p></td> |
| <td><p>Unrolls the recurrent cell for one time step.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.LSTMCell.hybridize" title="mxnet.gluon.rnn.LSTMCell.hybridize"><code class="xref py py-obj docutils literal notranslate"><span class="pre">hybridize</span></code></a>([active])</p></td> |
| <td><p>Please refer description of HybridBlock hybridize().</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.LSTMCell.infer_shape" title="mxnet.gluon.rnn.LSTMCell.infer_shape"><code class="xref py py-obj docutils literal notranslate"><span class="pre">infer_shape</span></code></a>(i, x, is_bidirect)</p></td> |
| <td><p>Infers shape of Parameters from inputs.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.LSTMCell.infer_type" title="mxnet.gluon.rnn.LSTMCell.infer_type"><code class="xref py py-obj docutils literal notranslate"><span class="pre">infer_type</span></code></a>(*args)</p></td> |
| <td><p>Infers data type of Parameters from inputs.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.LSTMCell.initialize" title="mxnet.gluon.rnn.LSTMCell.initialize"><code class="xref py py-obj docutils literal notranslate"><span class="pre">initialize</span></code></a>([init, device, verbose, force_reinit])</p></td> |
| <td><p>Initializes <code class="xref py py-class docutils literal notranslate"><span class="pre">Parameter</span></code> s of this <code class="xref py py-class docutils literal notranslate"><span class="pre">Block</span></code> and its children.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.LSTMCell.load" title="mxnet.gluon.rnn.LSTMCell.load"><code class="xref py py-obj docutils literal notranslate"><span class="pre">load</span></code></a>(prefix)</p></td> |
| <td><p>Load a model saved using the <cite>save</cite> API</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.LSTMCell.load_dict" title="mxnet.gluon.rnn.LSTMCell.load_dict"><code class="xref py py-obj docutils literal notranslate"><span class="pre">load_dict</span></code></a>(param_dict[, device, …])</p></td> |
| <td><p>Load parameters from dict</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.LSTMCell.load_parameters" title="mxnet.gluon.rnn.LSTMCell.load_parameters"><code class="xref py py-obj docutils literal notranslate"><span class="pre">load_parameters</span></code></a>(filename[, device, …])</p></td> |
| <td><p>Load parameters from file previously saved by <cite>save_parameters</cite>.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.LSTMCell.optimize_for" title="mxnet.gluon.rnn.LSTMCell.optimize_for"><code class="xref py py-obj docutils literal notranslate"><span class="pre">optimize_for</span></code></a>(x, *args[, backend, clear, …])</p></td> |
| <td><p>Partitions the current HybridBlock and optimizes it for a given backend without executing a forward pass.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.LSTMCell.register_child" title="mxnet.gluon.rnn.LSTMCell.register_child"><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_child</span></code></a>(block[, name])</p></td> |
| <td><p>Registers block as a child of self.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.LSTMCell.register_forward_hook" title="mxnet.gluon.rnn.LSTMCell.register_forward_hook"><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_forward_hook</span></code></a>(hook)</p></td> |
| <td><p>Registers a forward hook on the block.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.LSTMCell.register_forward_pre_hook" title="mxnet.gluon.rnn.LSTMCell.register_forward_pre_hook"><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_forward_pre_hook</span></code></a>(hook)</p></td> |
| <td><p>Registers a forward pre-hook on the block.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.LSTMCell.register_op_hook" title="mxnet.gluon.rnn.LSTMCell.register_op_hook"><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_op_hook</span></code></a>(callback[, monitor_all])</p></td> |
| <td><p>Install callback monitor.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.LSTMCell.reset" title="mxnet.gluon.rnn.LSTMCell.reset"><code class="xref py py-obj docutils literal notranslate"><span class="pre">reset</span></code></a>()</p></td> |
| <td><p>Reset before re-using the cell for another graph.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.LSTMCell.reset_ctx" title="mxnet.gluon.rnn.LSTMCell.reset_ctx"><code class="xref py py-obj docutils literal notranslate"><span class="pre">reset_ctx</span></code></a>(ctx)</p></td> |
| <td><p>This function has been deprecated.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.LSTMCell.reset_device" title="mxnet.gluon.rnn.LSTMCell.reset_device"><code class="xref py py-obj docutils literal notranslate"><span class="pre">reset_device</span></code></a>(device)</p></td> |
| <td><p>Re-assign all Parameters to other devices.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.LSTMCell.save" title="mxnet.gluon.rnn.LSTMCell.save"><code class="xref py py-obj docutils literal notranslate"><span class="pre">save</span></code></a>(prefix)</p></td> |
| <td><p>Save the model architecture and parameters to load again later</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.LSTMCell.save_parameters" title="mxnet.gluon.rnn.LSTMCell.save_parameters"><code class="xref py py-obj docutils literal notranslate"><span class="pre">save_parameters</span></code></a>(filename[, deduplicate])</p></td> |
| <td><p>Save parameters to file.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.LSTMCell.setattr" title="mxnet.gluon.rnn.LSTMCell.setattr"><code class="xref py py-obj docutils literal notranslate"><span class="pre">setattr</span></code></a>(name, value)</p></td> |
| <td><p>Set an attribute to a new value for all Parameters.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.LSTMCell.share_parameters" title="mxnet.gluon.rnn.LSTMCell.share_parameters"><code class="xref py py-obj docutils literal notranslate"><span class="pre">share_parameters</span></code></a>(shared)</p></td> |
| <td><p>Share parameters recursively inside the model.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.LSTMCell.state_info" title="mxnet.gluon.rnn.LSTMCell.state_info"><code class="xref py py-obj docutils literal notranslate"><span class="pre">state_info</span></code></a>([batch_size])</p></td> |
| <td><p>shape and layout information of states</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.LSTMCell.summary" title="mxnet.gluon.rnn.LSTMCell.summary"><code class="xref py py-obj docutils literal notranslate"><span class="pre">summary</span></code></a>(*inputs)</p></td> |
| <td><p>Print the summary of the model’s output and parameters.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.LSTMCell.unroll" title="mxnet.gluon.rnn.LSTMCell.unroll"><code class="xref py py-obj docutils literal notranslate"><span class="pre">unroll</span></code></a>(length, inputs[, begin_state, …])</p></td> |
| <td><p>Unrolls an RNN cell across time steps.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.LSTMCell.zero_grad" title="mxnet.gluon.rnn.LSTMCell.zero_grad"><code class="xref py py-obj docutils literal notranslate"><span class="pre">zero_grad</span></code></a>()</p></td> |
| <td><p>Sets all Parameters’ gradient buffer to 0.</p></td> |
| </tr> |
| </tbody> |
| </table> |
| <p><strong>Attributes</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.rnn.LSTMCell.params" title="mxnet.gluon.rnn.LSTMCell.params"><code class="xref py py-obj docutils literal notranslate"><span class="pre">params</span></code></a></p></td> |
| <td><p>Returns this <code class="xref py py-class docutils literal notranslate"><span class="pre">Block</span></code>’s parameter dictionary (does not include its children’s parameters).</p></td> |
| </tr> |
| </tbody> |
| </table> |
| <p>where <span class="math notranslate nohighlight">\(h_t\)</span> is the hidden state at time <cite>t</cite>, <span class="math notranslate nohighlight">\(c_t\)</span> is the |
| cell state at time <cite>t</cite>, <span class="math notranslate nohighlight">\(x_t\)</span> is the hidden state of the previous |
| layer at time <cite>t</cite> or <span class="math notranslate nohighlight">\(input_t\)</span> for the first layer, and <span class="math notranslate nohighlight">\(i_t\)</span>, |
| <span class="math notranslate nohighlight">\(f_t\)</span>, <span class="math notranslate nohighlight">\(g_t\)</span>, <span class="math notranslate nohighlight">\(o_t\)</span> are the input, forget, cell, and |
| out gates, respectively.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>hidden_size</strong> (<em>int</em>) – Number of units in output symbol.</p></li> |
| <li><p><strong>i2h_weight_initializer</strong> (<em>str</em><em> or </em><a class="reference internal" href="../../initializer/index.html#mxnet.initializer.Initializer" title="mxnet.initializer.Initializer"><em>Initializer</em></a>) – Initializer for the input weights matrix, used for the linear |
| transformation of the inputs.</p></li> |
| <li><p><strong>h2h_weight_initializer</strong> (<em>str</em><em> or </em><a class="reference internal" href="../../initializer/index.html#mxnet.initializer.Initializer" title="mxnet.initializer.Initializer"><em>Initializer</em></a>) – Initializer for the recurrent weights matrix, used for the linear |
| transformation of the recurrent state.</p></li> |
| <li><p><strong>i2h_bias_initializer</strong> (<em>str</em><em> or </em><a class="reference internal" href="../../initializer/index.html#mxnet.initializer.Initializer" title="mxnet.initializer.Initializer"><em>Initializer</em></a><em>, </em><em>default 'zeros'</em>) – Initializer for the bias vector.</p></li> |
| <li><p><strong>h2h_bias_initializer</strong> (<em>str</em><em> or </em><a class="reference internal" href="../../initializer/index.html#mxnet.initializer.Initializer" title="mxnet.initializer.Initializer"><em>Initializer</em></a><em>, </em><em>default 'zeros'</em>) – Initializer for the bias vector.</p></li> |
| <li><p><strong>input_size</strong> (<em>int</em><em>, </em><em>default 0</em>) – The number of expected features in the input x. |
| If not specified, it will be inferred from input.</p></li> |
| <li><p><strong>activation</strong> (<em>str</em><em>, </em><em>default 'tanh'</em>) – Activation type to use. See nd/symbol Activation |
| for supported types.</p></li> |
| <li><p><strong>recurrent_activation</strong> (<em>str</em><em>, </em><em>default 'sigmoid'</em>) – Activation type to use for the recurrent step. See nd/symbol Activation |
| for supported types.</p></li> |
| <li><p><strong>Inputs</strong> – <ul> |
| <li><p><strong>data</strong>: input tensor with shape <cite>(batch_size, input_size)</cite>.</p></li> |
| <li><p><strong>states</strong>: a list of two initial recurrent state tensors. Each has shape |
| <cite>(batch_size, num_hidden)</cite>.</p></li> |
| </ul> |
| </p></li> |
| <li><p><strong>Outputs</strong> – <ul> |
| <li><p><strong>out</strong>: output tensor with shape <cite>(batch_size, num_hidden)</cite>.</p></li> |
| <li><p><strong>next_states</strong>: a list of two output recurrent state tensors. Each has |
| the same shape as <cite>states</cite>.</p></li> |
| </ul> |
| </p></li> |
| </ul> |
| </dd> |
| </dl> |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.LSTMCell.apply"> |
| <code class="sig-name descname">apply</code><span class="sig-paren">(</span><em class="sig-param">fn</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.rnn.LSTMCell.apply" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Applies <code class="docutils literal notranslate"><span class="pre">fn</span></code> recursively to every child block as well as self.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><p><strong>fn</strong> (<em>callable</em>) – Function to be applied to each submodule, of form <cite>fn(block)</cite>.</p> |
| </dd> |
| <dt class="field-even">Returns</dt> |
| <dd class="field-even"><p></p> |
| </dd> |
| <dt class="field-odd">Return type</dt> |
| <dd class="field-odd"><p>this block</p> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.LSTMCell.begin_state"> |
| <code class="sig-name descname">begin_state</code><span class="sig-paren">(</span><em class="sig-param">batch_size=0</em>, <em class="sig-param">func=<function zeros></em>, <em class="sig-param">**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.rnn.LSTMCell.begin_state" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Initial state for this cell.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>func</strong> (<em>callable</em><em>, </em><em>default symbol.zeros</em>) – <p>Function for creating initial state.</p> |
| <p>For Symbol API, func can be <cite>symbol.zeros</cite>, <cite>symbol.uniform</cite>, |
| <cite>symbol.var etc</cite>. Use <cite>symbol.var</cite> if you want to directly |
| feed input as states.</p> |
| <p>For NDArray API, func can be <cite>ndarray.zeros</cite>, <cite>ndarray.ones</cite>, etc.</p> |
| </p></li> |
| <li><p><strong>batch_size</strong> (<em>int</em><em>, </em><em>default 0</em>) – Only required for NDArray API. Size of the batch (‘N’ in layout) |
| dimension of input.</p></li> |
| <li><p><strong>**kwargs</strong> – Additional keyword arguments passed to func. For example |
| <cite>mean</cite>, <cite>std</cite>, <cite>dtype</cite>, etc.</p></li> |
| </ul> |
| </dd> |
| <dt class="field-even">Returns</dt> |
| <dd class="field-even"><p><strong>states</strong> – Starting states for the first RNN step.</p> |
| </dd> |
| <dt class="field-odd">Return type</dt> |
| <dd class="field-odd"><p>nested list of Symbol</p> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.LSTMCell.cast"> |
| <code class="sig-name descname">cast</code><span class="sig-paren">(</span><em class="sig-param">dtype</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.rnn.LSTMCell.cast" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Cast this Block to use another data type.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><p><strong>dtype</strong> (<em>str</em><em> or </em><em>numpy.dtype</em>) – The new data type.</p> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.LSTMCell.collect_params"> |
| <code class="sig-name descname">collect_params</code><span class="sig-paren">(</span><em class="sig-param">select=None</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.rnn.LSTMCell.collect_params" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Returns a <code class="xref py py-class docutils literal notranslate"><span class="pre">Dict</span></code> containing this <code class="xref py py-class docutils literal notranslate"><span class="pre">Block</span></code> and all of its |
| children’s Parameters(default), also can returns the select <code class="xref py py-class docutils literal notranslate"><span class="pre">Dict</span></code> |
| which match some given regular expressions.</p> |
| <p>For example, collect the specified parameters in [‘conv1.weight’, ‘conv1.bias’, ‘fc.weight’, |
| ‘fc.bias’]:</p> |
| <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">model</span><span class="o">.</span><span class="n">collect_params</span><span class="p">(</span><span class="s1">'conv1.weight|conv1.bias|fc.weight|fc.bias'</span><span class="p">)</span> |
| </pre></div> |
| </div> |
| <p>or collect all parameters whose names end with ‘weight’ or ‘bias’, this can be done |
| using regular expressions:</p> |
| <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">model</span><span class="o">.</span><span class="n">collect_params</span><span class="p">(</span><span class="s1">'.*weight|.*bias'</span><span class="p">)</span> |
| </pre></div> |
| </div> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><p><strong>select</strong> (<em>str</em>) – regular expressions</p> |
| </dd> |
| <dt class="field-even">Returns</dt> |
| <dd class="field-even"><p></p> |
| </dd> |
| <dt class="field-odd">Return type</dt> |
| <dd class="field-odd"><p>The selected <code class="xref py py-class docutils literal notranslate"><span class="pre">Dict</span></code></p> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.LSTMCell.export"> |
| <code class="sig-name descname">export</code><span class="sig-paren">(</span><em class="sig-param">path</em>, <em class="sig-param">epoch=0</em>, <em class="sig-param">remove_amp_cast=True</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.rnn.LSTMCell.export" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Export HybridBlock to json format that can be loaded by |
| <cite>gluon.SymbolBlock.imports</cite> or the C++ interface.</p> |
| <div class="admonition note"> |
| <p class="admonition-title">Note</p> |
| <p>When there are only one input, it will have name <cite>data</cite>. When there |
| Are more than one inputs, they will be named as <cite>data0</cite>, <cite>data1</cite>, etc.</p> |
| </div> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>path</strong> (<em>str</em><em> or </em><em>None</em>) – Path to save model. Two files <cite>path-symbol.json</cite> and <cite>path-xxxx.params</cite> |
| will be created, where xxxx is the 4 digits epoch number. |
| If None, do not export to file but return Python Symbol object and |
| corresponding dictionary of parameters.</p></li> |
| <li><p><strong>epoch</strong> (<em>int</em>) – Epoch number of saved model.</p></li> |
| <li><p><strong>remove_amp_cast</strong> (<em>bool</em><em>, </em><em>optional</em>) – Whether to remove the amp_cast and amp_multicast operators, before saving the model.</p></li> |
| </ul> |
| </dd> |
| <dt class="field-even">Returns</dt> |
| <dd class="field-even"><p><ul class="simple"> |
| <li><p><strong>symbol_filename</strong> (<em>str</em>) – Filename to which model symbols were saved, including <cite>path</cite> prefix.</p></li> |
| <li><p><strong>params_filename</strong> (<em>str</em>) – Filename to which model parameters were saved, including <cite>path</cite> prefix.</p></li> |
| </ul> |
| </p> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.LSTMCell.forward"> |
| <code class="sig-name descname">forward</code><span class="sig-paren">(</span><em class="sig-param">inputs</em>, <em class="sig-param">states</em><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/mxnet/gluon/rnn/rnn_cell.html#LSTMCell.forward"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.gluon.rnn.LSTMCell.forward" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Unrolls the recurrent cell for one time step.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>inputs</strong> (<em>sym.Variable</em>) – Input symbol, 2D, of shape (batch_size * num_units).</p></li> |
| <li><p><strong>states</strong> (<em>list of sym.Variable</em>) – RNN state from previous step or the output of begin_state().</p></li> |
| </ul> |
| </dd> |
| <dt class="field-even">Returns</dt> |
| <dd class="field-even"><p><ul class="simple"> |
| <li><p><strong>output</strong> (<em>Symbol</em>) – Symbol corresponding to the output from the RNN when unrolling |
| for a single time step.</p></li> |
| <li><p><strong>states</strong> (<em>list of Symbol</em>) – The new state of this RNN after this unrolling. |
| The type of this symbol is same as the output of <cite>begin_state()</cite>. |
| This can be used as an input state to the next time step |
| of this RNN.</p></li> |
| </ul> |
| </p> |
| </dd> |
| </dl> |
| <div class="admonition seealso"> |
| <p class="admonition-title">See also</p> |
| <dl class="simple"> |
| <dt><a class="reference internal" href="#mxnet.gluon.rnn.LSTMCell.begin_state" title="mxnet.gluon.rnn.LSTMCell.begin_state"><code class="xref py py-meth docutils literal notranslate"><span class="pre">begin_state()</span></code></a></dt><dd><p>This function can provide the states for the first time step.</p> |
| </dd> |
| <dt><a class="reference internal" href="#mxnet.gluon.rnn.LSTMCell.unroll" title="mxnet.gluon.rnn.LSTMCell.unroll"><code class="xref py py-meth docutils literal notranslate"><span class="pre">unroll()</span></code></a></dt><dd><p>This function unrolls an RNN for a given number of (>=1) time steps.</p> |
| </dd> |
| </dl> |
| </div> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.LSTMCell.hybridize"> |
| <code class="sig-name descname">hybridize</code><span class="sig-paren">(</span><em class="sig-param">active=True</em>, <em class="sig-param">**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.rnn.LSTMCell.hybridize" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Please refer description of HybridBlock hybridize().</p> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.LSTMCell.infer_shape"> |
| <code class="sig-name descname">infer_shape</code><span class="sig-paren">(</span><em class="sig-param">i</em>, <em class="sig-param">x</em>, <em class="sig-param">is_bidirect</em><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/mxnet/gluon/rnn/rnn_cell.html#LSTMCell.infer_shape"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.gluon.rnn.LSTMCell.infer_shape" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Infers shape of Parameters from inputs.</p> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.LSTMCell.infer_type"> |
| <code class="sig-name descname">infer_type</code><span class="sig-paren">(</span><em class="sig-param">*args</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.rnn.LSTMCell.infer_type" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Infers data type of Parameters from inputs.</p> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.LSTMCell.initialize"> |
| <code class="sig-name descname">initialize</code><span class="sig-paren">(</span><em class="sig-param">init=<mxnet.initializer.Uniform object></em>, <em class="sig-param">device=None</em>, <em class="sig-param">verbose=False</em>, <em class="sig-param">force_reinit=False</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.rnn.LSTMCell.initialize" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Initializes <code class="xref py py-class docutils literal notranslate"><span class="pre">Parameter</span></code> s of this <code class="xref py py-class docutils literal notranslate"><span class="pre">Block</span></code> and its children.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>init</strong> (<a class="reference internal" href="../../initializer/index.html#mxnet.initializer.Initializer" title="mxnet.initializer.Initializer"><em>Initializer</em></a>) – Global default Initializer to be used when <code class="xref py py-meth docutils literal notranslate"><span class="pre">Parameter.init()</span></code> is <code class="docutils literal notranslate"><span class="pre">None</span></code>. |
| Otherwise, <code class="xref py py-meth docutils literal notranslate"><span class="pre">Parameter.init()</span></code> takes precedence.</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>) – Keeps a copy of Parameters on one or many device(s).</p></li> |
| <li><p><strong>verbose</strong> (<em>bool</em><em>, </em><em>default False</em>) – Whether to verbosely print out details on initialization.</p></li> |
| <li><p><strong>force_reinit</strong> (<em>bool</em><em>, </em><em>default False</em>) – Whether to force re-initialization if parameter is already initialized.</p></li> |
| </ul> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.LSTMCell.load"> |
| <code class="sig-name descname">load</code><span class="sig-paren">(</span><em class="sig-param">prefix</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.rnn.LSTMCell.load" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Load a model saved using the <cite>save</cite> API</p> |
| <p>Reconfigures a model using the saved configuration. This function |
| does not regenerate the model architecture. It resets each Block’s |
| parameter UUIDs as they were when saved in order to match the names of the |
| saved parameters.</p> |
| <p>This function assumes the Blocks in the model were created in the same |
| order they were when the model was saved. This is because each Block is |
| uniquely identified by Block class name and a unique ID in order (since |
| its an OrderedDict) and uses the unique ID to denote that specific Block.</p> |
| <p>Assumes that the model is created in an identical order every time. |
| If the model is not able to be recreated deterministically do not |
| use this set of APIs to save/load your model.</p> |
| <p>For HybridBlocks, the cached_graph (Symbol & inputs) and settings are |
| restored if it had been hybridized before saving.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><p><strong>prefix</strong> (<em>str</em>) – The prefix to use in filenames for loading this model: |
| <prefix>-model.json and <prefix>-model.params</p> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.LSTMCell.load_dict"> |
| <code class="sig-name descname">load_dict</code><span class="sig-paren">(</span><em class="sig-param">param_dict</em>, <em class="sig-param">device=None</em>, <em class="sig-param">allow_missing=False</em>, <em class="sig-param">ignore_extra=False</em>, <em class="sig-param">cast_dtype=False</em>, <em class="sig-param">dtype_source='current'</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.rnn.LSTMCell.load_dict" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Load parameters from dict</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>param_dict</strong> (<em>dict</em>) – Dictionary containing model 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>, </em><em>optional</em>) – Device context on which the memory is allocated. Default is |
| <cite>mxnet.device.current_device()</cite>.</p></li> |
| <li><p><strong>allow_missing</strong> (<em>bool</em><em>, </em><em>default False</em>) – Whether to silently skip loading parameters not represented in the file.</p></li> |
| <li><p><strong>ignore_extra</strong> (<em>bool</em><em>, </em><em>default False</em>) – Whether to silently ignore parameters from the file that are not |
| present in this dict.</p></li> |
| <li><p><strong>cast_dtype</strong> (<em>bool</em><em>, </em><em>default False</em>) – Cast the data type of the NDArray loaded from the checkpoint to the dtype |
| provided by the Parameter if any</p></li> |
| <li><p><strong>dtype_source</strong> (<em>str</em><em>, </em><em>default 'current'</em>) – must be in {‘current’, ‘saved’} |
| Only valid if cast_dtype=True, specify the source of the dtype for casting |
| the parameters</p></li> |
| </ul> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.LSTMCell.load_parameters"> |
| <code class="sig-name descname">load_parameters</code><span class="sig-paren">(</span><em class="sig-param">filename</em>, <em class="sig-param">device=None</em>, <em class="sig-param">allow_missing=False</em>, <em class="sig-param">ignore_extra=False</em>, <em class="sig-param">cast_dtype=False</em>, <em class="sig-param">dtype_source='current'</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.rnn.LSTMCell.load_parameters" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Load parameters from file previously saved by <cite>save_parameters</cite>.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>filename</strong> (<em>str</em>) – Path to parameter file.</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><em>, </em><em>default cpu</em><em>(</em><em>)</em>) – Device(s) to initialize loaded parameters on.</p></li> |
| <li><p><strong>allow_missing</strong> (<em>bool</em><em>, </em><em>default False</em>) – Whether to silently skip loading parameters not represents in the file.</p></li> |
| <li><p><strong>ignore_extra</strong> (<em>bool</em><em>, </em><em>default False</em>) – Whether to silently ignore parameters from the file that are not |
| present in this Block.</p></li> |
| <li><p><strong>cast_dtype</strong> (<em>bool</em><em>, </em><em>default False</em>) – Cast the data type of the NDArray loaded from the checkpoint to the dtype |
| provided by the Parameter if any.</p></li> |
| <li><p><strong>dtype_source</strong> (<em>str</em><em>, </em><em>default 'current'</em>) – must be in {‘current’, ‘saved’} |
| Only valid if cast_dtype=True, specify the source of the dtype for casting |
| the parameters</p></li> |
| </ul> |
| </dd> |
| </dl> |
| <p class="rubric">References</p> |
| <p><a class="reference external" href="https://mxnet.apache.org/api/python/docs/tutorials/packages/gluon/blocks/save_load_params.html">Saving and Loading Gluon Models</a></p> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.LSTMCell.optimize_for"> |
| <code class="sig-name descname">optimize_for</code><span class="sig-paren">(</span><em class="sig-param">x</em>, <em class="sig-param">*args</em>, <em class="sig-param">backend=None</em>, <em class="sig-param">clear=False</em>, <em class="sig-param">partition_if_dynamic=True</em>, <em class="sig-param">static_alloc=False</em>, <em class="sig-param">static_shape=False</em>, <em class="sig-param">inline_limit=2</em>, <em class="sig-param">forward_bulk_size=None</em>, <em class="sig-param">backward_bulk_size=None</em>, <em class="sig-param">**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.rnn.LSTMCell.optimize_for" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Partitions the current HybridBlock and optimizes it for a given backend |
| without executing a forward pass. Modifies the HybridBlock in-place.</p> |
| <p>Immediately partitions a HybridBlock using the specified backend. Combines |
| the work done in the hybridize API with part of the work done in the forward |
| pass without calling the CachedOp. Can be used in place of hybridize, |
| afterwards <cite>export</cite> can be called or inference can be run. See README.md in |
| example/extensions/lib_subgraph/README.md for more details.</p> |
| <p class="rubric">Examples</p> |
| <p># partition and then export to file |
| block.optimize_for(x, backend=’myPart’) |
| block.export(‘partitioned’)</p> |
| <p># partition and then run inference |
| block.optimize_for(x, backend=’myPart’) |
| block(x)</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>x</strong> (<a class="reference internal" href="../../legacy/ndarray/ndarray.html#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – first input to model</p></li> |
| <li><p><strong>*args</strong> (<a class="reference internal" href="../../legacy/ndarray/ndarray.html#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – other inputs to model</p></li> |
| <li><p><strong>backend</strong> (<em>str</em>) – The name of backend, as registered in <cite>SubgraphBackendRegistry</cite>, default None</p></li> |
| <li><p><strong>backend_opts</strong> (<em>dict of user-specified options to pass to the backend for partitioning</em><em>, </em><em>optional</em>) – Passed on to <cite>PrePartition</cite> and <cite>PostPartition</cite> functions of <cite>SubgraphProperty</cite></p></li> |
| <li><p><strong>clear</strong> (<em>bool</em><em>, </em><em>default False</em>) – clears any previous optimizations</p></li> |
| <li><p><strong>partition_if_dynamic</strong> (<em>bool</em><em>, </em><em>default False</em>) – whether to partition the graph when dynamic shape op exists</p></li> |
| <li><p><strong>static_alloc</strong> (<em>bool</em><em>, </em><em>default False</em>) – Statically allocate memory to improve speed. Memory usage may increase.</p></li> |
| <li><p><strong>static_shape</strong> (<em>bool</em><em>, </em><em>default False</em>) – Optimize for invariant input shapes between iterations. Must also |
| set static_alloc to True. Change of input shapes is still allowed |
| but slower.</p></li> |
| <li><p><strong>inline_limit</strong> (<em>optional int</em><em>, </em><em>default 2</em>) – Maximum number of operators that can be inlined.</p></li> |
| <li><p><strong>forward_bulk_size</strong> (<em>optional int</em><em>, </em><em>default None</em>) – Segment size of bulk execution during forward pass.</p></li> |
| <li><p><strong>backward_bulk_size</strong> (<em>optional int</em><em>, </em><em>default None</em>) – Segment size of bulk execution during backward pass.</p></li> |
| <li><p><strong>**kwargs</strong> (<em>The backend options</em><em>, </em><em>optional</em>) – Passed on to <cite>PrePartition</cite> and <cite>PostPartition</cite> functions of <cite>SubgraphProperty</cite></p></li> |
| </ul> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.LSTMCell.params"> |
| <em class="property">property </em><code class="sig-name descname">params</code><a class="headerlink" href="#mxnet.gluon.rnn.LSTMCell.params" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Returns this <code class="xref py py-class docutils literal notranslate"><span class="pre">Block</span></code>’s parameter dictionary (does not include its |
| children’s parameters).</p> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.LSTMCell.register_child"> |
| <code class="sig-name descname">register_child</code><span class="sig-paren">(</span><em class="sig-param">block</em>, <em class="sig-param">name=None</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.rnn.LSTMCell.register_child" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Registers block as a child of self. <code class="xref py py-class docutils literal notranslate"><span class="pre">Block</span></code> s assigned to self as |
| attributes will be registered automatically.</p> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.LSTMCell.register_forward_hook"> |
| <code class="sig-name descname">register_forward_hook</code><span class="sig-paren">(</span><em class="sig-param">hook</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.rnn.LSTMCell.register_forward_hook" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Registers a forward hook on the block.</p> |
| <p>The hook function is called immediately after <a class="reference internal" href="#mxnet.gluon.rnn.LSTMCell.forward" title="mxnet.gluon.rnn.LSTMCell.forward"><code class="xref py py-func docutils literal notranslate"><span class="pre">forward()</span></code></a>. |
| It should not modify the input or output.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><p><strong>hook</strong> (<em>callable</em>) – The forward hook function of form <cite>hook(block, input, output) -> None</cite>.</p> |
| </dd> |
| <dt class="field-even">Returns</dt> |
| <dd class="field-even"><p></p> |
| </dd> |
| <dt class="field-odd">Return type</dt> |
| <dd class="field-odd"><p><code class="xref py py-class docutils literal notranslate"><span class="pre">mxnet.gluon.utils.HookHandle</span></code></p> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.LSTMCell.register_forward_pre_hook"> |
| <code class="sig-name descname">register_forward_pre_hook</code><span class="sig-paren">(</span><em class="sig-param">hook</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.rnn.LSTMCell.register_forward_pre_hook" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Registers a forward pre-hook on the block.</p> |
| <p>The hook function is called immediately before <a class="reference internal" href="#mxnet.gluon.rnn.LSTMCell.forward" title="mxnet.gluon.rnn.LSTMCell.forward"><code class="xref py py-func docutils literal notranslate"><span class="pre">forward()</span></code></a>. |
| It should not modify the input or output.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><p><strong>hook</strong> (<em>callable</em>) – The forward hook function of form <cite>hook(block, input) -> None</cite>.</p> |
| </dd> |
| <dt class="field-even">Returns</dt> |
| <dd class="field-even"><p></p> |
| </dd> |
| <dt class="field-odd">Return type</dt> |
| <dd class="field-odd"><p><code class="xref py py-class docutils literal notranslate"><span class="pre">mxnet.gluon.utils.HookHandle</span></code></p> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.LSTMCell.register_op_hook"> |
| <code class="sig-name descname">register_op_hook</code><span class="sig-paren">(</span><em class="sig-param">callback</em>, <em class="sig-param">monitor_all=False</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.rnn.LSTMCell.register_op_hook" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Install callback monitor.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>callback</strong> (<em>function</em>) – Function called to inspect the values of the intermediate outputs |
| of blocks after hybridization. It takes 3 parameters: |
| name of the tensor being inspected (str) |
| name of the operator producing or consuming that tensor (str) |
| tensor being inspected (NDArray).</p></li> |
| <li><p><strong>monitor_all</strong> (<em>bool</em><em>, </em><em>default False</em>) – If True, monitor both input and output, otherwise monitor output only.</p></li> |
| </ul> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.LSTMCell.reset"> |
| <code class="sig-name descname">reset</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.rnn.LSTMCell.reset" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Reset before re-using the cell for another graph.</p> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.LSTMCell.reset_ctx"> |
| <code class="sig-name descname">reset_ctx</code><span class="sig-paren">(</span><em class="sig-param">ctx</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.rnn.LSTMCell.reset_ctx" title="Permalink to this definition">¶</a></dt> |
| <dd><p>This function has been deprecated. Please refer to <code class="docutils literal notranslate"><span class="pre">Block.reset_device</span></code>.</p> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.LSTMCell.reset_device"> |
| <code class="sig-name descname">reset_device</code><span class="sig-paren">(</span><em class="sig-param">device</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.rnn.LSTMCell.reset_device" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Re-assign all Parameters to other devices.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><p><strong>device</strong> (Device or list of Device, default <code class="xref py py-meth docutils literal notranslate"><span class="pre">device.current_device()</span></code>.) – Assign Parameter to given device. If device is a list of Device, a |
| copy will be made for each device.</p> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.LSTMCell.save"> |
| <code class="sig-name descname">save</code><span class="sig-paren">(</span><em class="sig-param">prefix</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.rnn.LSTMCell.save" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Save the model architecture and parameters to load again later</p> |
| <p>Saves the model architecture as a nested dictionary where each Block |
| in the model is a dictionary and its children are sub-dictionaries.</p> |
| <p>Each Block is uniquely identified by Block class name and a unique ID. |
| We save each Block’s parameter UUID to restore later in order to match |
| the saved parameters.</p> |
| <p>Recursively traverses a Block’s children in order (since its an |
| OrderedDict) and uses the unique ID to denote that specific Block.</p> |
| <p>Assumes that the model is created in an identical order every time. |
| If the model is not able to be recreated deterministically do not |
| use this set of APIs to save/load your model.</p> |
| <p>For HybridBlocks, the cached_graph is saved (Symbol & inputs) if |
| it has already been hybridized.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><p><strong>prefix</strong> (<em>str</em>) – The prefix to use in filenames for saving this model: |
| <prefix>-model.json and <prefix>-model.params</p> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.LSTMCell.save_parameters"> |
| <code class="sig-name descname">save_parameters</code><span class="sig-paren">(</span><em class="sig-param">filename</em>, <em class="sig-param">deduplicate=False</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.rnn.LSTMCell.save_parameters" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Save parameters to file.</p> |
| <p>Saved parameters can only be loaded with <cite>load_parameters</cite>. Note that this |
| method only saves parameters, not model structure. If you want to save |
| model structures, please use <code class="xref py py-meth docutils literal notranslate"><span class="pre">HybridBlock.export()</span></code>.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>filename</strong> (<em>str</em>) – Path to file.</p></li> |
| <li><p><strong>deduplicate</strong> (<em>bool</em><em>, </em><em>default False</em>) – If True, save shared parameters only once. Otherwise, if a Block |
| contains multiple sub-blocks that share parameters, each of the |
| shared parameters will be separately saved for every sub-block.</p></li> |
| </ul> |
| </dd> |
| </dl> |
| <p class="rubric">References</p> |
| <p><a class="reference external" href="https://mxnet.apache.org/api/python/docs/tutorials/packages/gluon/blocks/save_load_params.html">Saving and Loading Gluon Models</a></p> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.LSTMCell.setattr"> |
| <code class="sig-name descname">setattr</code><span class="sig-paren">(</span><em class="sig-param">name</em>, <em class="sig-param">value</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.rnn.LSTMCell.setattr" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Set an attribute to a new value for all Parameters.</p> |
| <p>For example, set grad_req to null if you don’t need gradient w.r.t a |
| model’s Parameters:</p> |
| <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">model</span><span class="o">.</span><span class="n">setattr</span><span class="p">(</span><span class="s1">'grad_req'</span><span class="p">,</span> <span class="s1">'null'</span><span class="p">)</span> |
| </pre></div> |
| </div> |
| <p>or change the learning rate multiplier:</p> |
| <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">model</span><span class="o">.</span><span class="n">setattr</span><span class="p">(</span><span class="s1">'lr_mult'</span><span class="p">,</span> <span class="mf">0.5</span><span class="p">)</span> |
| </pre></div> |
| </div> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>name</strong> (<em>str</em>) – Name of the attribute.</p></li> |
| <li><p><strong>value</strong> (<em>valid type for attribute name</em>) – The new value for the attribute.</p></li> |
| </ul> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.LSTMCell.share_parameters"> |
| <code class="sig-name descname">share_parameters</code><span class="sig-paren">(</span><em class="sig-param">shared</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.rnn.LSTMCell.share_parameters" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Share parameters recursively inside the model.</p> |
| <p>For example, if you want <code class="docutils literal notranslate"><span class="pre">dense1</span></code> to share <code class="docutils literal notranslate"><span class="pre">dense0</span></code>’s weights, you can do:</p> |
| <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">dense0</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Dense</span><span class="p">(</span><span class="mi">20</span><span class="p">)</span> |
| <span class="n">dense1</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Dense</span><span class="p">(</span><span class="mi">20</span><span class="p">)</span> |
| <span class="n">dense1</span><span class="o">.</span><span class="n">share_parameters</span><span class="p">(</span><span class="n">dense0</span><span class="o">.</span><span class="n">collect_params</span><span class="p">())</span> |
| </pre></div> |
| </div> |
| <dl class="simple"> |
| <dt>which equals to</dt><dd><p>dense1.weight = dense0.weight |
| dense1.bias = dense0.bias</p> |
| </dd> |
| </dl> |
| <p>Note that unlike the <cite>load_parameters</cite> or <cite>load_dict</cite> functions, |
| <cite>share_parameters</cite> results in the <cite>Parameter</cite> object being shared (or |
| tied) between the models, whereas <cite>load_parameters</cite> or <cite>load_dict</cite> only |
| set the value of the data dictionary of a model. If you call |
| <cite>load_parameters</cite> or <cite>load_dict</cite> after <cite>share_parameters</cite>, the loaded |
| value will be reflected in all networks that use the shared (or tied) |
| <cite>Parameter</cite> object.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><p><strong>shared</strong> (<em>Dict</em>) – Dict of the shared parameters.</p> |
| </dd> |
| <dt class="field-even">Returns</dt> |
| <dd class="field-even"><p></p> |
| </dd> |
| <dt class="field-odd">Return type</dt> |
| <dd class="field-odd"><p>this block</p> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.LSTMCell.state_info"> |
| <code class="sig-name descname">state_info</code><span class="sig-paren">(</span><em class="sig-param">batch_size=0</em><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/mxnet/gluon/rnn/rnn_cell.html#LSTMCell.state_info"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.gluon.rnn.LSTMCell.state_info" title="Permalink to this definition">¶</a></dt> |
| <dd><p>shape and layout information of states</p> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.LSTMCell.summary"> |
| <code class="sig-name descname">summary</code><span class="sig-paren">(</span><em class="sig-param">*inputs</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.rnn.LSTMCell.summary" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Print the summary of the model’s output and parameters.</p> |
| <p>The network must have been initialized, and must not have been hybridized.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><p><strong>inputs</strong> (<em>object</em>) – Any input that the model supports. For any tensor in the input, only |
| <a class="reference internal" href="../../legacy/ndarray/ndarray.html#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><code class="xref py py-class docutils literal notranslate"><span class="pre">mxnet.ndarray.NDArray</span></code></a> is supported.</p> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.LSTMCell.unroll"> |
| <code class="sig-name descname">unroll</code><span class="sig-paren">(</span><em class="sig-param">length</em>, <em class="sig-param">inputs</em>, <em class="sig-param">begin_state=None</em>, <em class="sig-param">layout='NTC'</em>, <em class="sig-param">merge_outputs=None</em>, <em class="sig-param">valid_length=None</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.rnn.LSTMCell.unroll" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Unrolls an RNN cell across time steps.</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>) – Number of steps to unroll.</p></li> |
| <li><p><strong>inputs</strong> (<a class="reference internal" href="../../legacy/symbol/symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a><em>, </em><em>list of Symbol</em><em>, or </em><em>None</em>) – <p>If <cite>inputs</cite> is a single Symbol (usually the output |
| of Embedding symbol), it should have shape |
| (batch_size, length, …) if <cite>layout</cite> is ‘NTC’, |
| or (length, batch_size, …) if <cite>layout</cite> is ‘TNC’.</p> |
| <p>If <cite>inputs</cite> is a list of symbols (usually output of |
| previous unroll), they should all have shape |
| (batch_size, …).</p> |
| </p></li> |
| <li><p><strong>begin_state</strong> (<em>nested list of Symbol</em><em>, </em><em>optional</em>) – Input states created by <cite>begin_state()</cite> |
| or output state of another cell. |
| Created from <cite>begin_state()</cite> if <cite>None</cite>.</p></li> |
| <li><p><strong>layout</strong> (<em>str</em><em>, </em><em>optional</em>) – <cite>layout</cite> of input symbol. Only used if inputs |
| is a single Symbol.</p></li> |
| <li><p><strong>merge_outputs</strong> (<em>bool</em><em>, </em><em>optional</em>) – If <cite>False</cite>, returns outputs as a list of Symbols. |
| If <cite>True</cite>, concatenates output across time steps |
| and returns a single symbol with shape |
| (batch_size, length, …) if layout is ‘NTC’, |
| or (length, batch_size, …) if layout is ‘TNC’. |
| If <cite>None</cite>, output whatever is faster.</p></li> |
| <li><p><strong>valid_length</strong> (<a class="reference internal" href="../../legacy/symbol/symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a><em>, </em><a class="reference internal" href="../../legacy/ndarray/ndarray.html#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a><em> or </em><em>None</em>) – <cite>valid_length</cite> specifies the length of the sequences in the batch without padding. |
| This option is especially useful for building sequence-to-sequence models where |
| the input and output sequences would potentially be padded. |
| If <cite>valid_length</cite> is None, all sequences are assumed to have the same length. |
| If <cite>valid_length</cite> is a Symbol or NDArray, it should have shape (batch_size,). |
| The ith element will be the length of the ith sequence in the batch. |
| The last valid state will be return and the padded outputs will be masked with 0. |
| Note that <cite>valid_length</cite> must be smaller or equal to <cite>length</cite>.</p></li> |
| </ul> |
| </dd> |
| <dt class="field-even">Returns</dt> |
| <dd class="field-even"><p><ul class="simple"> |
| <li><p><strong>outputs</strong> (<em>list of Symbol or Symbol</em>) – Symbol (if <cite>merge_outputs</cite> is True) or list of Symbols |
| (if <cite>merge_outputs</cite> is False) corresponding to the output from |
| the RNN from this unrolling.</p></li> |
| <li><p><strong>states</strong> (<em>list of Symbol</em>) – The new state of this RNN after this unrolling. |
| The type of this symbol is same as the output of <cite>begin_state()</cite>.</p></li> |
| </ul> |
| </p> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.LSTMCell.zero_grad"> |
| <code class="sig-name descname">zero_grad</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.rnn.LSTMCell.zero_grad" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Sets all Parameters’ gradient buffer to 0.</p> |
| </dd></dl> |
| |
| </dd></dl> |
| |
| <dl class="class"> |
| <dt id="mxnet.gluon.rnn.LSTMPCell"> |
| <em class="property">class </em><code class="sig-name descname">LSTMPCell</code><span class="sig-paren">(</span><em class="sig-param">hidden_size</em>, <em class="sig-param">projection_size</em>, <em class="sig-param">i2h_weight_initializer=None</em>, <em class="sig-param">h2h_weight_initializer=None</em>, <em class="sig-param">h2r_weight_initializer=None</em>, <em class="sig-param">i2h_bias_initializer='zeros'</em>, <em class="sig-param">h2h_bias_initializer='zeros'</em>, <em class="sig-param">input_size=0</em><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/mxnet/gluon/rnn/rnn_cell.html#LSTMPCell"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.gluon.rnn.LSTMPCell" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">mxnet.gluon.rnn.rnn_cell.HybridRecurrentCell</span></code></p> |
| <p>Long-Short Term Memory Projected (LSTMP) network cell. |
| (<a class="reference external" href="https://arxiv.org/abs/1402.1128">https://arxiv.org/abs/1402.1128</a>)</p> |
| <p>Each call computes the following function:</p> |
| <div class="math notranslate nohighlight"> |
| \[\begin{split}\begin{array}{ll} |
| i_t = sigmoid(W_{ii} x_t + b_{ii} + W_{ri} r_{(t-1)} + b_{ri}) \\ |
| f_t = sigmoid(W_{if} x_t + b_{if} + W_{rf} r_{(t-1)} + b_{rf}) \\ |
| g_t = \tanh(W_{ig} x_t + b_{ig} + W_{rc} r_{(t-1)} + b_{rg}) \\ |
| o_t = sigmoid(W_{io} x_t + b_{io} + W_{ro} r_{(t-1)} + b_{ro}) \\ |
| c_t = f_t * c_{(t-1)} + i_t * g_t \\ |
| h_t = o_t * \tanh(c_t) \\ |
| r_t = W_{hr} h_t |
| \end{array}\end{split}\]</div> |
| <p><strong>Methods</strong></p> |
| <table class="longtable docutils align-default"> |
| <colgroup> |
| <col style="width: 10%" /> |
| <col style="width: 90%" /> |
| </colgroup> |
| <tbody> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.LSTMPCell.apply" title="mxnet.gluon.rnn.LSTMPCell.apply"><code class="xref py py-obj docutils literal notranslate"><span class="pre">apply</span></code></a>(fn)</p></td> |
| <td><p>Applies <code class="docutils literal notranslate"><span class="pre">fn</span></code> recursively to every child block as well as self.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.LSTMPCell.begin_state" title="mxnet.gluon.rnn.LSTMPCell.begin_state"><code class="xref py py-obj docutils literal notranslate"><span class="pre">begin_state</span></code></a>([batch_size, func])</p></td> |
| <td><p>Initial state for this cell.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.LSTMPCell.cast" title="mxnet.gluon.rnn.LSTMPCell.cast"><code class="xref py py-obj docutils literal notranslate"><span class="pre">cast</span></code></a>(dtype)</p></td> |
| <td><p>Cast this Block to use another data type.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.LSTMPCell.collect_params" title="mxnet.gluon.rnn.LSTMPCell.collect_params"><code class="xref py py-obj docutils literal notranslate"><span class="pre">collect_params</span></code></a>([select])</p></td> |
| <td><p>Returns a <code class="xref py py-class docutils literal notranslate"><span class="pre">Dict</span></code> containing this <code class="xref py py-class docutils literal notranslate"><span class="pre">Block</span></code> and all of its children’s Parameters(default), also can returns the select <code class="xref py py-class docutils literal notranslate"><span class="pre">Dict</span></code> which match some given regular expressions.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.LSTMPCell.export" title="mxnet.gluon.rnn.LSTMPCell.export"><code class="xref py py-obj docutils literal notranslate"><span class="pre">export</span></code></a>(path[, epoch, remove_amp_cast])</p></td> |
| <td><p>Export HybridBlock to json format that can be loaded by <cite>gluon.SymbolBlock.imports</cite> or the C++ interface.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.LSTMPCell.forward" title="mxnet.gluon.rnn.LSTMPCell.forward"><code class="xref py py-obj docutils literal notranslate"><span class="pre">forward</span></code></a>(inputs, states)</p></td> |
| <td><p>Unrolls the recurrent cell for one time step.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.LSTMPCell.hybridize" title="mxnet.gluon.rnn.LSTMPCell.hybridize"><code class="xref py py-obj docutils literal notranslate"><span class="pre">hybridize</span></code></a>([active])</p></td> |
| <td><p>Please refer description of HybridBlock hybridize().</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.LSTMPCell.infer_shape" title="mxnet.gluon.rnn.LSTMPCell.infer_shape"><code class="xref py py-obj docutils literal notranslate"><span class="pre">infer_shape</span></code></a>(i, x, is_bidirect)</p></td> |
| <td><p>Infers shape of Parameters from inputs.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.LSTMPCell.infer_type" title="mxnet.gluon.rnn.LSTMPCell.infer_type"><code class="xref py py-obj docutils literal notranslate"><span class="pre">infer_type</span></code></a>(*args)</p></td> |
| <td><p>Infers data type of Parameters from inputs.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.LSTMPCell.initialize" title="mxnet.gluon.rnn.LSTMPCell.initialize"><code class="xref py py-obj docutils literal notranslate"><span class="pre">initialize</span></code></a>([init, device, verbose, force_reinit])</p></td> |
| <td><p>Initializes <code class="xref py py-class docutils literal notranslate"><span class="pre">Parameter</span></code> s of this <code class="xref py py-class docutils literal notranslate"><span class="pre">Block</span></code> and its children.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.LSTMPCell.load" title="mxnet.gluon.rnn.LSTMPCell.load"><code class="xref py py-obj docutils literal notranslate"><span class="pre">load</span></code></a>(prefix)</p></td> |
| <td><p>Load a model saved using the <cite>save</cite> API</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.LSTMPCell.load_dict" title="mxnet.gluon.rnn.LSTMPCell.load_dict"><code class="xref py py-obj docutils literal notranslate"><span class="pre">load_dict</span></code></a>(param_dict[, device, …])</p></td> |
| <td><p>Load parameters from dict</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.LSTMPCell.load_parameters" title="mxnet.gluon.rnn.LSTMPCell.load_parameters"><code class="xref py py-obj docutils literal notranslate"><span class="pre">load_parameters</span></code></a>(filename[, device, …])</p></td> |
| <td><p>Load parameters from file previously saved by <cite>save_parameters</cite>.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.LSTMPCell.optimize_for" title="mxnet.gluon.rnn.LSTMPCell.optimize_for"><code class="xref py py-obj docutils literal notranslate"><span class="pre">optimize_for</span></code></a>(x, *args[, backend, clear, …])</p></td> |
| <td><p>Partitions the current HybridBlock and optimizes it for a given backend without executing a forward pass.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.LSTMPCell.register_child" title="mxnet.gluon.rnn.LSTMPCell.register_child"><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_child</span></code></a>(block[, name])</p></td> |
| <td><p>Registers block as a child of self.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.LSTMPCell.register_forward_hook" title="mxnet.gluon.rnn.LSTMPCell.register_forward_hook"><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_forward_hook</span></code></a>(hook)</p></td> |
| <td><p>Registers a forward hook on the block.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.LSTMPCell.register_forward_pre_hook" title="mxnet.gluon.rnn.LSTMPCell.register_forward_pre_hook"><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_forward_pre_hook</span></code></a>(hook)</p></td> |
| <td><p>Registers a forward pre-hook on the block.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.LSTMPCell.register_op_hook" title="mxnet.gluon.rnn.LSTMPCell.register_op_hook"><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_op_hook</span></code></a>(callback[, monitor_all])</p></td> |
| <td><p>Install callback monitor.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.LSTMPCell.reset" title="mxnet.gluon.rnn.LSTMPCell.reset"><code class="xref py py-obj docutils literal notranslate"><span class="pre">reset</span></code></a>()</p></td> |
| <td><p>Reset before re-using the cell for another graph.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.LSTMPCell.reset_ctx" title="mxnet.gluon.rnn.LSTMPCell.reset_ctx"><code class="xref py py-obj docutils literal notranslate"><span class="pre">reset_ctx</span></code></a>(ctx)</p></td> |
| <td><p>This function has been deprecated.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.LSTMPCell.reset_device" title="mxnet.gluon.rnn.LSTMPCell.reset_device"><code class="xref py py-obj docutils literal notranslate"><span class="pre">reset_device</span></code></a>(device)</p></td> |
| <td><p>Re-assign all Parameters to other devices.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.LSTMPCell.save" title="mxnet.gluon.rnn.LSTMPCell.save"><code class="xref py py-obj docutils literal notranslate"><span class="pre">save</span></code></a>(prefix)</p></td> |
| <td><p>Save the model architecture and parameters to load again later</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.LSTMPCell.save_parameters" title="mxnet.gluon.rnn.LSTMPCell.save_parameters"><code class="xref py py-obj docutils literal notranslate"><span class="pre">save_parameters</span></code></a>(filename[, deduplicate])</p></td> |
| <td><p>Save parameters to file.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.LSTMPCell.setattr" title="mxnet.gluon.rnn.LSTMPCell.setattr"><code class="xref py py-obj docutils literal notranslate"><span class="pre">setattr</span></code></a>(name, value)</p></td> |
| <td><p>Set an attribute to a new value for all Parameters.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.LSTMPCell.share_parameters" title="mxnet.gluon.rnn.LSTMPCell.share_parameters"><code class="xref py py-obj docutils literal notranslate"><span class="pre">share_parameters</span></code></a>(shared)</p></td> |
| <td><p>Share parameters recursively inside the model.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.LSTMPCell.state_info" title="mxnet.gluon.rnn.LSTMPCell.state_info"><code class="xref py py-obj docutils literal notranslate"><span class="pre">state_info</span></code></a>([batch_size])</p></td> |
| <td><p>shape and layout information of states</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.LSTMPCell.summary" title="mxnet.gluon.rnn.LSTMPCell.summary"><code class="xref py py-obj docutils literal notranslate"><span class="pre">summary</span></code></a>(*inputs)</p></td> |
| <td><p>Print the summary of the model’s output and parameters.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.LSTMPCell.unroll" title="mxnet.gluon.rnn.LSTMPCell.unroll"><code class="xref py py-obj docutils literal notranslate"><span class="pre">unroll</span></code></a>(length, inputs[, begin_state, …])</p></td> |
| <td><p>Unrolls an RNN cell across time steps.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.LSTMPCell.zero_grad" title="mxnet.gluon.rnn.LSTMPCell.zero_grad"><code class="xref py py-obj docutils literal notranslate"><span class="pre">zero_grad</span></code></a>()</p></td> |
| <td><p>Sets all Parameters’ gradient buffer to 0.</p></td> |
| </tr> |
| </tbody> |
| </table> |
| <p><strong>Attributes</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.rnn.LSTMPCell.params" title="mxnet.gluon.rnn.LSTMPCell.params"><code class="xref py py-obj docutils literal notranslate"><span class="pre">params</span></code></a></p></td> |
| <td><p>Returns this <code class="xref py py-class docutils literal notranslate"><span class="pre">Block</span></code>’s parameter dictionary (does not include its children’s parameters).</p></td> |
| </tr> |
| </tbody> |
| </table> |
| <p>where <span class="math notranslate nohighlight">\(r_t\)</span> is the projected recurrent activation at time <cite>t</cite>, |
| <span class="math notranslate nohighlight">\(h_t\)</span> is the hidden state at time <cite>t</cite>, <span class="math notranslate nohighlight">\(c_t\)</span> is the |
| cell state at time <cite>t</cite>, <span class="math notranslate nohighlight">\(x_t\)</span> is the input at time <cite>t</cite>, and <span class="math notranslate nohighlight">\(i_t\)</span>, |
| <span class="math notranslate nohighlight">\(f_t\)</span>, <span class="math notranslate nohighlight">\(g_t\)</span>, <span class="math notranslate nohighlight">\(o_t\)</span> are the input, forget, cell, and |
| out gates, respectively.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>hidden_size</strong> (<em>int</em>) – Number of units in cell state symbol.</p></li> |
| <li><p><strong>projection_size</strong> (<em>int</em>) – Number of units in output symbol.</p></li> |
| <li><p><strong>i2h_weight_initializer</strong> (<em>str</em><em> or </em><a class="reference internal" href="../../initializer/index.html#mxnet.initializer.Initializer" title="mxnet.initializer.Initializer"><em>Initializer</em></a>) – Initializer for the input weights matrix, used for the linear |
| transformation of the inputs.</p></li> |
| <li><p><strong>h2h_weight_initializer</strong> (<em>str</em><em> or </em><a class="reference internal" href="../../initializer/index.html#mxnet.initializer.Initializer" title="mxnet.initializer.Initializer"><em>Initializer</em></a>) – Initializer for the recurrent weights matrix, used for the linear |
| transformation of the hidden state.</p></li> |
| <li><p><strong>h2r_weight_initializer</strong> (<em>str</em><em> or </em><a class="reference internal" href="../../initializer/index.html#mxnet.initializer.Initializer" title="mxnet.initializer.Initializer"><em>Initializer</em></a>) – Initializer for the projection weights matrix, used for the linear |
| transformation of the recurrent state.</p></li> |
| <li><p><strong>i2h_bias_initializer</strong> (<em>str</em><em> or </em><a class="reference internal" href="../../initializer/index.html#mxnet.initializer.Initializer" title="mxnet.initializer.Initializer"><em>Initializer</em></a><em>, </em><em>default 'lstmbias'</em>) – Initializer for the bias vector. By default, bias for the forget |
| gate is initialized to 1 while all other biases are initialized |
| to zero.</p></li> |
| <li><p><strong>h2h_bias_initializer</strong> (<em>str</em><em> or </em><a class="reference internal" href="../../initializer/index.html#mxnet.initializer.Initializer" title="mxnet.initializer.Initializer"><em>Initializer</em></a>) – Initializer for the bias vector.</p></li> |
| <li><p><strong>Inputs</strong> – <ul> |
| <li><p><strong>data</strong>: input tensor with shape <cite>(batch_size, input_size)</cite>.</p></li> |
| <li><p><strong>states</strong>: a list of two initial recurrent state tensors, with shape |
| <cite>(batch_size, projection_size)</cite> and <cite>(batch_size, hidden_size)</cite> respectively.</p></li> |
| </ul> |
| </p></li> |
| <li><p><strong>Outputs</strong> – <ul> |
| <li><p><strong>out</strong>: output tensor with shape <cite>(batch_size, num_hidden)</cite>.</p></li> |
| <li><p><strong>next_states</strong>: a list of two output recurrent state tensors. Each has |
| the same shape as <cite>states</cite>.</p></li> |
| </ul> |
| </p></li> |
| </ul> |
| </dd> |
| </dl> |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.LSTMPCell.apply"> |
| <code class="sig-name descname">apply</code><span class="sig-paren">(</span><em class="sig-param">fn</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.rnn.LSTMPCell.apply" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Applies <code class="docutils literal notranslate"><span class="pre">fn</span></code> recursively to every child block as well as self.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><p><strong>fn</strong> (<em>callable</em>) – Function to be applied to each submodule, of form <cite>fn(block)</cite>.</p> |
| </dd> |
| <dt class="field-even">Returns</dt> |
| <dd class="field-even"><p></p> |
| </dd> |
| <dt class="field-odd">Return type</dt> |
| <dd class="field-odd"><p>this block</p> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.LSTMPCell.begin_state"> |
| <code class="sig-name descname">begin_state</code><span class="sig-paren">(</span><em class="sig-param">batch_size=0</em>, <em class="sig-param">func=<function zeros></em>, <em class="sig-param">**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.rnn.LSTMPCell.begin_state" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Initial state for this cell.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>func</strong> (<em>callable</em><em>, </em><em>default symbol.zeros</em>) – <p>Function for creating initial state.</p> |
| <p>For Symbol API, func can be <cite>symbol.zeros</cite>, <cite>symbol.uniform</cite>, |
| <cite>symbol.var etc</cite>. Use <cite>symbol.var</cite> if you want to directly |
| feed input as states.</p> |
| <p>For NDArray API, func can be <cite>ndarray.zeros</cite>, <cite>ndarray.ones</cite>, etc.</p> |
| </p></li> |
| <li><p><strong>batch_size</strong> (<em>int</em><em>, </em><em>default 0</em>) – Only required for NDArray API. Size of the batch (‘N’ in layout) |
| dimension of input.</p></li> |
| <li><p><strong>**kwargs</strong> – Additional keyword arguments passed to func. For example |
| <cite>mean</cite>, <cite>std</cite>, <cite>dtype</cite>, etc.</p></li> |
| </ul> |
| </dd> |
| <dt class="field-even">Returns</dt> |
| <dd class="field-even"><p><strong>states</strong> – Starting states for the first RNN step.</p> |
| </dd> |
| <dt class="field-odd">Return type</dt> |
| <dd class="field-odd"><p>nested list of Symbol</p> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.LSTMPCell.cast"> |
| <code class="sig-name descname">cast</code><span class="sig-paren">(</span><em class="sig-param">dtype</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.rnn.LSTMPCell.cast" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Cast this Block to use another data type.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><p><strong>dtype</strong> (<em>str</em><em> or </em><em>numpy.dtype</em>) – The new data type.</p> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.LSTMPCell.collect_params"> |
| <code class="sig-name descname">collect_params</code><span class="sig-paren">(</span><em class="sig-param">select=None</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.rnn.LSTMPCell.collect_params" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Returns a <code class="xref py py-class docutils literal notranslate"><span class="pre">Dict</span></code> containing this <code class="xref py py-class docutils literal notranslate"><span class="pre">Block</span></code> and all of its |
| children’s Parameters(default), also can returns the select <code class="xref py py-class docutils literal notranslate"><span class="pre">Dict</span></code> |
| which match some given regular expressions.</p> |
| <p>For example, collect the specified parameters in [‘conv1.weight’, ‘conv1.bias’, ‘fc.weight’, |
| ‘fc.bias’]:</p> |
| <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">model</span><span class="o">.</span><span class="n">collect_params</span><span class="p">(</span><span class="s1">'conv1.weight|conv1.bias|fc.weight|fc.bias'</span><span class="p">)</span> |
| </pre></div> |
| </div> |
| <p>or collect all parameters whose names end with ‘weight’ or ‘bias’, this can be done |
| using regular expressions:</p> |
| <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">model</span><span class="o">.</span><span class="n">collect_params</span><span class="p">(</span><span class="s1">'.*weight|.*bias'</span><span class="p">)</span> |
| </pre></div> |
| </div> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><p><strong>select</strong> (<em>str</em>) – regular expressions</p> |
| </dd> |
| <dt class="field-even">Returns</dt> |
| <dd class="field-even"><p></p> |
| </dd> |
| <dt class="field-odd">Return type</dt> |
| <dd class="field-odd"><p>The selected <code class="xref py py-class docutils literal notranslate"><span class="pre">Dict</span></code></p> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.LSTMPCell.export"> |
| <code class="sig-name descname">export</code><span class="sig-paren">(</span><em class="sig-param">path</em>, <em class="sig-param">epoch=0</em>, <em class="sig-param">remove_amp_cast=True</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.rnn.LSTMPCell.export" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Export HybridBlock to json format that can be loaded by |
| <cite>gluon.SymbolBlock.imports</cite> or the C++ interface.</p> |
| <div class="admonition note"> |
| <p class="admonition-title">Note</p> |
| <p>When there are only one input, it will have name <cite>data</cite>. When there |
| Are more than one inputs, they will be named as <cite>data0</cite>, <cite>data1</cite>, etc.</p> |
| </div> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>path</strong> (<em>str</em><em> or </em><em>None</em>) – Path to save model. Two files <cite>path-symbol.json</cite> and <cite>path-xxxx.params</cite> |
| will be created, where xxxx is the 4 digits epoch number. |
| If None, do not export to file but return Python Symbol object and |
| corresponding dictionary of parameters.</p></li> |
| <li><p><strong>epoch</strong> (<em>int</em>) – Epoch number of saved model.</p></li> |
| <li><p><strong>remove_amp_cast</strong> (<em>bool</em><em>, </em><em>optional</em>) – Whether to remove the amp_cast and amp_multicast operators, before saving the model.</p></li> |
| </ul> |
| </dd> |
| <dt class="field-even">Returns</dt> |
| <dd class="field-even"><p><ul class="simple"> |
| <li><p><strong>symbol_filename</strong> (<em>str</em>) – Filename to which model symbols were saved, including <cite>path</cite> prefix.</p></li> |
| <li><p><strong>params_filename</strong> (<em>str</em>) – Filename to which model parameters were saved, including <cite>path</cite> prefix.</p></li> |
| </ul> |
| </p> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.LSTMPCell.forward"> |
| <code class="sig-name descname">forward</code><span class="sig-paren">(</span><em class="sig-param">inputs</em>, <em class="sig-param">states</em><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/mxnet/gluon/rnn/rnn_cell.html#LSTMPCell.forward"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.gluon.rnn.LSTMPCell.forward" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Unrolls the recurrent cell for one time step.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>inputs</strong> (<em>sym.Variable</em>) – Input symbol, 2D, of shape (batch_size * num_units).</p></li> |
| <li><p><strong>states</strong> (<em>list of sym.Variable</em>) – RNN state from previous step or the output of begin_state().</p></li> |
| </ul> |
| </dd> |
| <dt class="field-even">Returns</dt> |
| <dd class="field-even"><p><ul class="simple"> |
| <li><p><strong>output</strong> (<em>Symbol</em>) – Symbol corresponding to the output from the RNN when unrolling |
| for a single time step.</p></li> |
| <li><p><strong>states</strong> (<em>list of Symbol</em>) – The new state of this RNN after this unrolling. |
| The type of this symbol is same as the output of <cite>begin_state()</cite>. |
| This can be used as an input state to the next time step |
| of this RNN.</p></li> |
| </ul> |
| </p> |
| </dd> |
| </dl> |
| <div class="admonition seealso"> |
| <p class="admonition-title">See also</p> |
| <dl class="simple"> |
| <dt><a class="reference internal" href="#mxnet.gluon.rnn.LSTMPCell.begin_state" title="mxnet.gluon.rnn.LSTMPCell.begin_state"><code class="xref py py-meth docutils literal notranslate"><span class="pre">begin_state()</span></code></a></dt><dd><p>This function can provide the states for the first time step.</p> |
| </dd> |
| <dt><a class="reference internal" href="#mxnet.gluon.rnn.LSTMPCell.unroll" title="mxnet.gluon.rnn.LSTMPCell.unroll"><code class="xref py py-meth docutils literal notranslate"><span class="pre">unroll()</span></code></a></dt><dd><p>This function unrolls an RNN for a given number of (>=1) time steps.</p> |
| </dd> |
| </dl> |
| </div> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.LSTMPCell.hybridize"> |
| <code class="sig-name descname">hybridize</code><span class="sig-paren">(</span><em class="sig-param">active=True</em>, <em class="sig-param">**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.rnn.LSTMPCell.hybridize" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Please refer description of HybridBlock hybridize().</p> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.LSTMPCell.infer_shape"> |
| <code class="sig-name descname">infer_shape</code><span class="sig-paren">(</span><em class="sig-param">i</em>, <em class="sig-param">x</em>, <em class="sig-param">is_bidirect</em><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/mxnet/gluon/rnn/rnn_cell.html#LSTMPCell.infer_shape"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.gluon.rnn.LSTMPCell.infer_shape" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Infers shape of Parameters from inputs.</p> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.LSTMPCell.infer_type"> |
| <code class="sig-name descname">infer_type</code><span class="sig-paren">(</span><em class="sig-param">*args</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.rnn.LSTMPCell.infer_type" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Infers data type of Parameters from inputs.</p> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.LSTMPCell.initialize"> |
| <code class="sig-name descname">initialize</code><span class="sig-paren">(</span><em class="sig-param">init=<mxnet.initializer.Uniform object></em>, <em class="sig-param">device=None</em>, <em class="sig-param">verbose=False</em>, <em class="sig-param">force_reinit=False</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.rnn.LSTMPCell.initialize" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Initializes <code class="xref py py-class docutils literal notranslate"><span class="pre">Parameter</span></code> s of this <code class="xref py py-class docutils literal notranslate"><span class="pre">Block</span></code> and its children.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>init</strong> (<a class="reference internal" href="../../initializer/index.html#mxnet.initializer.Initializer" title="mxnet.initializer.Initializer"><em>Initializer</em></a>) – Global default Initializer to be used when <code class="xref py py-meth docutils literal notranslate"><span class="pre">Parameter.init()</span></code> is <code class="docutils literal notranslate"><span class="pre">None</span></code>. |
| Otherwise, <code class="xref py py-meth docutils literal notranslate"><span class="pre">Parameter.init()</span></code> takes precedence.</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>) – Keeps a copy of Parameters on one or many device(s).</p></li> |
| <li><p><strong>verbose</strong> (<em>bool</em><em>, </em><em>default False</em>) – Whether to verbosely print out details on initialization.</p></li> |
| <li><p><strong>force_reinit</strong> (<em>bool</em><em>, </em><em>default False</em>) – Whether to force re-initialization if parameter is already initialized.</p></li> |
| </ul> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.LSTMPCell.load"> |
| <code class="sig-name descname">load</code><span class="sig-paren">(</span><em class="sig-param">prefix</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.rnn.LSTMPCell.load" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Load a model saved using the <cite>save</cite> API</p> |
| <p>Reconfigures a model using the saved configuration. This function |
| does not regenerate the model architecture. It resets each Block’s |
| parameter UUIDs as they were when saved in order to match the names of the |
| saved parameters.</p> |
| <p>This function assumes the Blocks in the model were created in the same |
| order they were when the model was saved. This is because each Block is |
| uniquely identified by Block class name and a unique ID in order (since |
| its an OrderedDict) and uses the unique ID to denote that specific Block.</p> |
| <p>Assumes that the model is created in an identical order every time. |
| If the model is not able to be recreated deterministically do not |
| use this set of APIs to save/load your model.</p> |
| <p>For HybridBlocks, the cached_graph (Symbol & inputs) and settings are |
| restored if it had been hybridized before saving.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><p><strong>prefix</strong> (<em>str</em>) – The prefix to use in filenames for loading this model: |
| <prefix>-model.json and <prefix>-model.params</p> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.LSTMPCell.load_dict"> |
| <code class="sig-name descname">load_dict</code><span class="sig-paren">(</span><em class="sig-param">param_dict</em>, <em class="sig-param">device=None</em>, <em class="sig-param">allow_missing=False</em>, <em class="sig-param">ignore_extra=False</em>, <em class="sig-param">cast_dtype=False</em>, <em class="sig-param">dtype_source='current'</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.rnn.LSTMPCell.load_dict" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Load parameters from dict</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>param_dict</strong> (<em>dict</em>) – Dictionary containing model 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>, </em><em>optional</em>) – Device context on which the memory is allocated. Default is |
| <cite>mxnet.device.current_device()</cite>.</p></li> |
| <li><p><strong>allow_missing</strong> (<em>bool</em><em>, </em><em>default False</em>) – Whether to silently skip loading parameters not represented in the file.</p></li> |
| <li><p><strong>ignore_extra</strong> (<em>bool</em><em>, </em><em>default False</em>) – Whether to silently ignore parameters from the file that are not |
| present in this dict.</p></li> |
| <li><p><strong>cast_dtype</strong> (<em>bool</em><em>, </em><em>default False</em>) – Cast the data type of the NDArray loaded from the checkpoint to the dtype |
| provided by the Parameter if any</p></li> |
| <li><p><strong>dtype_source</strong> (<em>str</em><em>, </em><em>default 'current'</em>) – must be in {‘current’, ‘saved’} |
| Only valid if cast_dtype=True, specify the source of the dtype for casting |
| the parameters</p></li> |
| </ul> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.LSTMPCell.load_parameters"> |
| <code class="sig-name descname">load_parameters</code><span class="sig-paren">(</span><em class="sig-param">filename</em>, <em class="sig-param">device=None</em>, <em class="sig-param">allow_missing=False</em>, <em class="sig-param">ignore_extra=False</em>, <em class="sig-param">cast_dtype=False</em>, <em class="sig-param">dtype_source='current'</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.rnn.LSTMPCell.load_parameters" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Load parameters from file previously saved by <cite>save_parameters</cite>.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>filename</strong> (<em>str</em>) – Path to parameter file.</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><em>, </em><em>default cpu</em><em>(</em><em>)</em>) – Device(s) to initialize loaded parameters on.</p></li> |
| <li><p><strong>allow_missing</strong> (<em>bool</em><em>, </em><em>default False</em>) – Whether to silently skip loading parameters not represents in the file.</p></li> |
| <li><p><strong>ignore_extra</strong> (<em>bool</em><em>, </em><em>default False</em>) – Whether to silently ignore parameters from the file that are not |
| present in this Block.</p></li> |
| <li><p><strong>cast_dtype</strong> (<em>bool</em><em>, </em><em>default False</em>) – Cast the data type of the NDArray loaded from the checkpoint to the dtype |
| provided by the Parameter if any.</p></li> |
| <li><p><strong>dtype_source</strong> (<em>str</em><em>, </em><em>default 'current'</em>) – must be in {‘current’, ‘saved’} |
| Only valid if cast_dtype=True, specify the source of the dtype for casting |
| the parameters</p></li> |
| </ul> |
| </dd> |
| </dl> |
| <p class="rubric">References</p> |
| <p><a class="reference external" href="https://mxnet.apache.org/api/python/docs/tutorials/packages/gluon/blocks/save_load_params.html">Saving and Loading Gluon Models</a></p> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.LSTMPCell.optimize_for"> |
| <code class="sig-name descname">optimize_for</code><span class="sig-paren">(</span><em class="sig-param">x</em>, <em class="sig-param">*args</em>, <em class="sig-param">backend=None</em>, <em class="sig-param">clear=False</em>, <em class="sig-param">partition_if_dynamic=True</em>, <em class="sig-param">static_alloc=False</em>, <em class="sig-param">static_shape=False</em>, <em class="sig-param">inline_limit=2</em>, <em class="sig-param">forward_bulk_size=None</em>, <em class="sig-param">backward_bulk_size=None</em>, <em class="sig-param">**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.rnn.LSTMPCell.optimize_for" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Partitions the current HybridBlock and optimizes it for a given backend |
| without executing a forward pass. Modifies the HybridBlock in-place.</p> |
| <p>Immediately partitions a HybridBlock using the specified backend. Combines |
| the work done in the hybridize API with part of the work done in the forward |
| pass without calling the CachedOp. Can be used in place of hybridize, |
| afterwards <cite>export</cite> can be called or inference can be run. See README.md in |
| example/extensions/lib_subgraph/README.md for more details.</p> |
| <p class="rubric">Examples</p> |
| <p># partition and then export to file |
| block.optimize_for(x, backend=’myPart’) |
| block.export(‘partitioned’)</p> |
| <p># partition and then run inference |
| block.optimize_for(x, backend=’myPart’) |
| block(x)</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>x</strong> (<a class="reference internal" href="../../legacy/ndarray/ndarray.html#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – first input to model</p></li> |
| <li><p><strong>*args</strong> (<a class="reference internal" href="../../legacy/ndarray/ndarray.html#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – other inputs to model</p></li> |
| <li><p><strong>backend</strong> (<em>str</em>) – The name of backend, as registered in <cite>SubgraphBackendRegistry</cite>, default None</p></li> |
| <li><p><strong>backend_opts</strong> (<em>dict of user-specified options to pass to the backend for partitioning</em><em>, </em><em>optional</em>) – Passed on to <cite>PrePartition</cite> and <cite>PostPartition</cite> functions of <cite>SubgraphProperty</cite></p></li> |
| <li><p><strong>clear</strong> (<em>bool</em><em>, </em><em>default False</em>) – clears any previous optimizations</p></li> |
| <li><p><strong>partition_if_dynamic</strong> (<em>bool</em><em>, </em><em>default False</em>) – whether to partition the graph when dynamic shape op exists</p></li> |
| <li><p><strong>static_alloc</strong> (<em>bool</em><em>, </em><em>default False</em>) – Statically allocate memory to improve speed. Memory usage may increase.</p></li> |
| <li><p><strong>static_shape</strong> (<em>bool</em><em>, </em><em>default False</em>) – Optimize for invariant input shapes between iterations. Must also |
| set static_alloc to True. Change of input shapes is still allowed |
| but slower.</p></li> |
| <li><p><strong>inline_limit</strong> (<em>optional int</em><em>, </em><em>default 2</em>) – Maximum number of operators that can be inlined.</p></li> |
| <li><p><strong>forward_bulk_size</strong> (<em>optional int</em><em>, </em><em>default None</em>) – Segment size of bulk execution during forward pass.</p></li> |
| <li><p><strong>backward_bulk_size</strong> (<em>optional int</em><em>, </em><em>default None</em>) – Segment size of bulk execution during backward pass.</p></li> |
| <li><p><strong>**kwargs</strong> (<em>The backend options</em><em>, </em><em>optional</em>) – Passed on to <cite>PrePartition</cite> and <cite>PostPartition</cite> functions of <cite>SubgraphProperty</cite></p></li> |
| </ul> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.LSTMPCell.params"> |
| <em class="property">property </em><code class="sig-name descname">params</code><a class="headerlink" href="#mxnet.gluon.rnn.LSTMPCell.params" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Returns this <code class="xref py py-class docutils literal notranslate"><span class="pre">Block</span></code>’s parameter dictionary (does not include its |
| children’s parameters).</p> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.LSTMPCell.register_child"> |
| <code class="sig-name descname">register_child</code><span class="sig-paren">(</span><em class="sig-param">block</em>, <em class="sig-param">name=None</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.rnn.LSTMPCell.register_child" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Registers block as a child of self. <code class="xref py py-class docutils literal notranslate"><span class="pre">Block</span></code> s assigned to self as |
| attributes will be registered automatically.</p> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.LSTMPCell.register_forward_hook"> |
| <code class="sig-name descname">register_forward_hook</code><span class="sig-paren">(</span><em class="sig-param">hook</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.rnn.LSTMPCell.register_forward_hook" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Registers a forward hook on the block.</p> |
| <p>The hook function is called immediately after <a class="reference internal" href="#mxnet.gluon.rnn.LSTMPCell.forward" title="mxnet.gluon.rnn.LSTMPCell.forward"><code class="xref py py-func docutils literal notranslate"><span class="pre">forward()</span></code></a>. |
| It should not modify the input or output.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><p><strong>hook</strong> (<em>callable</em>) – The forward hook function of form <cite>hook(block, input, output) -> None</cite>.</p> |
| </dd> |
| <dt class="field-even">Returns</dt> |
| <dd class="field-even"><p></p> |
| </dd> |
| <dt class="field-odd">Return type</dt> |
| <dd class="field-odd"><p><code class="xref py py-class docutils literal notranslate"><span class="pre">mxnet.gluon.utils.HookHandle</span></code></p> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.LSTMPCell.register_forward_pre_hook"> |
| <code class="sig-name descname">register_forward_pre_hook</code><span class="sig-paren">(</span><em class="sig-param">hook</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.rnn.LSTMPCell.register_forward_pre_hook" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Registers a forward pre-hook on the block.</p> |
| <p>The hook function is called immediately before <a class="reference internal" href="#mxnet.gluon.rnn.LSTMPCell.forward" title="mxnet.gluon.rnn.LSTMPCell.forward"><code class="xref py py-func docutils literal notranslate"><span class="pre">forward()</span></code></a>. |
| It should not modify the input or output.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><p><strong>hook</strong> (<em>callable</em>) – The forward hook function of form <cite>hook(block, input) -> None</cite>.</p> |
| </dd> |
| <dt class="field-even">Returns</dt> |
| <dd class="field-even"><p></p> |
| </dd> |
| <dt class="field-odd">Return type</dt> |
| <dd class="field-odd"><p><code class="xref py py-class docutils literal notranslate"><span class="pre">mxnet.gluon.utils.HookHandle</span></code></p> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.LSTMPCell.register_op_hook"> |
| <code class="sig-name descname">register_op_hook</code><span class="sig-paren">(</span><em class="sig-param">callback</em>, <em class="sig-param">monitor_all=False</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.rnn.LSTMPCell.register_op_hook" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Install callback monitor.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>callback</strong> (<em>function</em>) – Function called to inspect the values of the intermediate outputs |
| of blocks after hybridization. It takes 3 parameters: |
| name of the tensor being inspected (str) |
| name of the operator producing or consuming that tensor (str) |
| tensor being inspected (NDArray).</p></li> |
| <li><p><strong>monitor_all</strong> (<em>bool</em><em>, </em><em>default False</em>) – If True, monitor both input and output, otherwise monitor output only.</p></li> |
| </ul> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.LSTMPCell.reset"> |
| <code class="sig-name descname">reset</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.rnn.LSTMPCell.reset" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Reset before re-using the cell for another graph.</p> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.LSTMPCell.reset_ctx"> |
| <code class="sig-name descname">reset_ctx</code><span class="sig-paren">(</span><em class="sig-param">ctx</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.rnn.LSTMPCell.reset_ctx" title="Permalink to this definition">¶</a></dt> |
| <dd><p>This function has been deprecated. Please refer to <code class="docutils literal notranslate"><span class="pre">Block.reset_device</span></code>.</p> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.LSTMPCell.reset_device"> |
| <code class="sig-name descname">reset_device</code><span class="sig-paren">(</span><em class="sig-param">device</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.rnn.LSTMPCell.reset_device" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Re-assign all Parameters to other devices.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><p><strong>device</strong> (Device or list of Device, default <code class="xref py py-meth docutils literal notranslate"><span class="pre">device.current_device()</span></code>.) – Assign Parameter to given device. If device is a list of Device, a |
| copy will be made for each device.</p> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.LSTMPCell.save"> |
| <code class="sig-name descname">save</code><span class="sig-paren">(</span><em class="sig-param">prefix</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.rnn.LSTMPCell.save" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Save the model architecture and parameters to load again later</p> |
| <p>Saves the model architecture as a nested dictionary where each Block |
| in the model is a dictionary and its children are sub-dictionaries.</p> |
| <p>Each Block is uniquely identified by Block class name and a unique ID. |
| We save each Block’s parameter UUID to restore later in order to match |
| the saved parameters.</p> |
| <p>Recursively traverses a Block’s children in order (since its an |
| OrderedDict) and uses the unique ID to denote that specific Block.</p> |
| <p>Assumes that the model is created in an identical order every time. |
| If the model is not able to be recreated deterministically do not |
| use this set of APIs to save/load your model.</p> |
| <p>For HybridBlocks, the cached_graph is saved (Symbol & inputs) if |
| it has already been hybridized.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><p><strong>prefix</strong> (<em>str</em>) – The prefix to use in filenames for saving this model: |
| <prefix>-model.json and <prefix>-model.params</p> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.LSTMPCell.save_parameters"> |
| <code class="sig-name descname">save_parameters</code><span class="sig-paren">(</span><em class="sig-param">filename</em>, <em class="sig-param">deduplicate=False</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.rnn.LSTMPCell.save_parameters" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Save parameters to file.</p> |
| <p>Saved parameters can only be loaded with <cite>load_parameters</cite>. Note that this |
| method only saves parameters, not model structure. If you want to save |
| model structures, please use <code class="xref py py-meth docutils literal notranslate"><span class="pre">HybridBlock.export()</span></code>.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>filename</strong> (<em>str</em>) – Path to file.</p></li> |
| <li><p><strong>deduplicate</strong> (<em>bool</em><em>, </em><em>default False</em>) – If True, save shared parameters only once. Otherwise, if a Block |
| contains multiple sub-blocks that share parameters, each of the |
| shared parameters will be separately saved for every sub-block.</p></li> |
| </ul> |
| </dd> |
| </dl> |
| <p class="rubric">References</p> |
| <p><a class="reference external" href="https://mxnet.apache.org/api/python/docs/tutorials/packages/gluon/blocks/save_load_params.html">Saving and Loading Gluon Models</a></p> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.LSTMPCell.setattr"> |
| <code class="sig-name descname">setattr</code><span class="sig-paren">(</span><em class="sig-param">name</em>, <em class="sig-param">value</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.rnn.LSTMPCell.setattr" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Set an attribute to a new value for all Parameters.</p> |
| <p>For example, set grad_req to null if you don’t need gradient w.r.t a |
| model’s Parameters:</p> |
| <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">model</span><span class="o">.</span><span class="n">setattr</span><span class="p">(</span><span class="s1">'grad_req'</span><span class="p">,</span> <span class="s1">'null'</span><span class="p">)</span> |
| </pre></div> |
| </div> |
| <p>or change the learning rate multiplier:</p> |
| <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">model</span><span class="o">.</span><span class="n">setattr</span><span class="p">(</span><span class="s1">'lr_mult'</span><span class="p">,</span> <span class="mf">0.5</span><span class="p">)</span> |
| </pre></div> |
| </div> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>name</strong> (<em>str</em>) – Name of the attribute.</p></li> |
| <li><p><strong>value</strong> (<em>valid type for attribute name</em>) – The new value for the attribute.</p></li> |
| </ul> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.LSTMPCell.share_parameters"> |
| <code class="sig-name descname">share_parameters</code><span class="sig-paren">(</span><em class="sig-param">shared</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.rnn.LSTMPCell.share_parameters" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Share parameters recursively inside the model.</p> |
| <p>For example, if you want <code class="docutils literal notranslate"><span class="pre">dense1</span></code> to share <code class="docutils literal notranslate"><span class="pre">dense0</span></code>’s weights, you can do:</p> |
| <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">dense0</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Dense</span><span class="p">(</span><span class="mi">20</span><span class="p">)</span> |
| <span class="n">dense1</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Dense</span><span class="p">(</span><span class="mi">20</span><span class="p">)</span> |
| <span class="n">dense1</span><span class="o">.</span><span class="n">share_parameters</span><span class="p">(</span><span class="n">dense0</span><span class="o">.</span><span class="n">collect_params</span><span class="p">())</span> |
| </pre></div> |
| </div> |
| <dl class="simple"> |
| <dt>which equals to</dt><dd><p>dense1.weight = dense0.weight |
| dense1.bias = dense0.bias</p> |
| </dd> |
| </dl> |
| <p>Note that unlike the <cite>load_parameters</cite> or <cite>load_dict</cite> functions, |
| <cite>share_parameters</cite> results in the <cite>Parameter</cite> object being shared (or |
| tied) between the models, whereas <cite>load_parameters</cite> or <cite>load_dict</cite> only |
| set the value of the data dictionary of a model. If you call |
| <cite>load_parameters</cite> or <cite>load_dict</cite> after <cite>share_parameters</cite>, the loaded |
| value will be reflected in all networks that use the shared (or tied) |
| <cite>Parameter</cite> object.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><p><strong>shared</strong> (<em>Dict</em>) – Dict of the shared parameters.</p> |
| </dd> |
| <dt class="field-even">Returns</dt> |
| <dd class="field-even"><p></p> |
| </dd> |
| <dt class="field-odd">Return type</dt> |
| <dd class="field-odd"><p>this block</p> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.LSTMPCell.state_info"> |
| <code class="sig-name descname">state_info</code><span class="sig-paren">(</span><em class="sig-param">batch_size=0</em><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/mxnet/gluon/rnn/rnn_cell.html#LSTMPCell.state_info"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.gluon.rnn.LSTMPCell.state_info" title="Permalink to this definition">¶</a></dt> |
| <dd><p>shape and layout information of states</p> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.LSTMPCell.summary"> |
| <code class="sig-name descname">summary</code><span class="sig-paren">(</span><em class="sig-param">*inputs</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.rnn.LSTMPCell.summary" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Print the summary of the model’s output and parameters.</p> |
| <p>The network must have been initialized, and must not have been hybridized.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><p><strong>inputs</strong> (<em>object</em>) – Any input that the model supports. For any tensor in the input, only |
| <a class="reference internal" href="../../legacy/ndarray/ndarray.html#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><code class="xref py py-class docutils literal notranslate"><span class="pre">mxnet.ndarray.NDArray</span></code></a> is supported.</p> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.LSTMPCell.unroll"> |
| <code class="sig-name descname">unroll</code><span class="sig-paren">(</span><em class="sig-param">length</em>, <em class="sig-param">inputs</em>, <em class="sig-param">begin_state=None</em>, <em class="sig-param">layout='NTC'</em>, <em class="sig-param">merge_outputs=None</em>, <em class="sig-param">valid_length=None</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.rnn.LSTMPCell.unroll" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Unrolls an RNN cell across time steps.</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>) – Number of steps to unroll.</p></li> |
| <li><p><strong>inputs</strong> (<a class="reference internal" href="../../legacy/symbol/symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a><em>, </em><em>list of Symbol</em><em>, or </em><em>None</em>) – <p>If <cite>inputs</cite> is a single Symbol (usually the output |
| of Embedding symbol), it should have shape |
| (batch_size, length, …) if <cite>layout</cite> is ‘NTC’, |
| or (length, batch_size, …) if <cite>layout</cite> is ‘TNC’.</p> |
| <p>If <cite>inputs</cite> is a list of symbols (usually output of |
| previous unroll), they should all have shape |
| (batch_size, …).</p> |
| </p></li> |
| <li><p><strong>begin_state</strong> (<em>nested list of Symbol</em><em>, </em><em>optional</em>) – Input states created by <cite>begin_state()</cite> |
| or output state of another cell. |
| Created from <cite>begin_state()</cite> if <cite>None</cite>.</p></li> |
| <li><p><strong>layout</strong> (<em>str</em><em>, </em><em>optional</em>) – <cite>layout</cite> of input symbol. Only used if inputs |
| is a single Symbol.</p></li> |
| <li><p><strong>merge_outputs</strong> (<em>bool</em><em>, </em><em>optional</em>) – If <cite>False</cite>, returns outputs as a list of Symbols. |
| If <cite>True</cite>, concatenates output across time steps |
| and returns a single symbol with shape |
| (batch_size, length, …) if layout is ‘NTC’, |
| or (length, batch_size, …) if layout is ‘TNC’. |
| If <cite>None</cite>, output whatever is faster.</p></li> |
| <li><p><strong>valid_length</strong> (<a class="reference internal" href="../../legacy/symbol/symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a><em>, </em><a class="reference internal" href="../../legacy/ndarray/ndarray.html#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a><em> or </em><em>None</em>) – <cite>valid_length</cite> specifies the length of the sequences in the batch without padding. |
| This option is especially useful for building sequence-to-sequence models where |
| the input and output sequences would potentially be padded. |
| If <cite>valid_length</cite> is None, all sequences are assumed to have the same length. |
| If <cite>valid_length</cite> is a Symbol or NDArray, it should have shape (batch_size,). |
| The ith element will be the length of the ith sequence in the batch. |
| The last valid state will be return and the padded outputs will be masked with 0. |
| Note that <cite>valid_length</cite> must be smaller or equal to <cite>length</cite>.</p></li> |
| </ul> |
| </dd> |
| <dt class="field-even">Returns</dt> |
| <dd class="field-even"><p><ul class="simple"> |
| <li><p><strong>outputs</strong> (<em>list of Symbol or Symbol</em>) – Symbol (if <cite>merge_outputs</cite> is True) or list of Symbols |
| (if <cite>merge_outputs</cite> is False) corresponding to the output from |
| the RNN from this unrolling.</p></li> |
| <li><p><strong>states</strong> (<em>list of Symbol</em>) – The new state of this RNN after this unrolling. |
| The type of this symbol is same as the output of <cite>begin_state()</cite>.</p></li> |
| </ul> |
| </p> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.LSTMPCell.zero_grad"> |
| <code class="sig-name descname">zero_grad</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.rnn.LSTMPCell.zero_grad" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Sets all Parameters’ gradient buffer to 0.</p> |
| </dd></dl> |
| |
| </dd></dl> |
| |
| <dl class="class"> |
| <dt id="mxnet.gluon.rnn.ModifierCell"> |
| <em class="property">class </em><code class="sig-name descname">ModifierCell</code><span class="sig-paren">(</span><em class="sig-param">base_cell</em><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/mxnet/gluon/rnn/rnn_cell.html#ModifierCell"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.gluon.rnn.ModifierCell" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">mxnet.gluon.rnn.rnn_cell.HybridRecurrentCell</span></code></p> |
| <p>Base class for modifier cells. A modifier |
| cell takes a base cell, apply modifications |
| on it (e.g. Zoneout), and returns a new cell.</p> |
| <p>After applying modifiers the base cell should |
| no longer be called directly. The modifier cell |
| should be used instead.</p> |
| <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.rnn.ModifierCell.apply" title="mxnet.gluon.rnn.ModifierCell.apply"><code class="xref py py-obj docutils literal notranslate"><span class="pre">apply</span></code></a>(fn)</p></td> |
| <td><p>Applies <code class="docutils literal notranslate"><span class="pre">fn</span></code> recursively to every child block as well as self.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.ModifierCell.begin_state" title="mxnet.gluon.rnn.ModifierCell.begin_state"><code class="xref py py-obj docutils literal notranslate"><span class="pre">begin_state</span></code></a>([func])</p></td> |
| <td><p>Initial state for this cell.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.ModifierCell.cast" title="mxnet.gluon.rnn.ModifierCell.cast"><code class="xref py py-obj docutils literal notranslate"><span class="pre">cast</span></code></a>(dtype)</p></td> |
| <td><p>Cast this Block to use another data type.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.ModifierCell.collect_params" title="mxnet.gluon.rnn.ModifierCell.collect_params"><code class="xref py py-obj docutils literal notranslate"><span class="pre">collect_params</span></code></a>([select])</p></td> |
| <td><p>Returns a <code class="xref py py-class docutils literal notranslate"><span class="pre">Dict</span></code> containing this <code class="xref py py-class docutils literal notranslate"><span class="pre">Block</span></code> and all of its children’s Parameters(default), also can returns the select <code class="xref py py-class docutils literal notranslate"><span class="pre">Dict</span></code> which match some given regular expressions.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.ModifierCell.export" title="mxnet.gluon.rnn.ModifierCell.export"><code class="xref py py-obj docutils literal notranslate"><span class="pre">export</span></code></a>(path[, epoch, remove_amp_cast])</p></td> |
| <td><p>Export HybridBlock to json format that can be loaded by <cite>gluon.SymbolBlock.imports</cite> or the C++ interface.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.ModifierCell.forward" title="mxnet.gluon.rnn.ModifierCell.forward"><code class="xref py py-obj docutils literal notranslate"><span class="pre">forward</span></code></a>(inputs, states)</p></td> |
| <td><p>Unrolls the recurrent cell for one time step.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.ModifierCell.hybridize" title="mxnet.gluon.rnn.ModifierCell.hybridize"><code class="xref py py-obj docutils literal notranslate"><span class="pre">hybridize</span></code></a>([active])</p></td> |
| <td><p>Please refer description of HybridBlock hybridize().</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.ModifierCell.infer_shape" title="mxnet.gluon.rnn.ModifierCell.infer_shape"><code class="xref py py-obj docutils literal notranslate"><span class="pre">infer_shape</span></code></a>(*args)</p></td> |
| <td><p>Infers shape of Parameters from inputs.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.ModifierCell.infer_type" title="mxnet.gluon.rnn.ModifierCell.infer_type"><code class="xref py py-obj docutils literal notranslate"><span class="pre">infer_type</span></code></a>(*args)</p></td> |
| <td><p>Infers data type of Parameters from inputs.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.ModifierCell.initialize" title="mxnet.gluon.rnn.ModifierCell.initialize"><code class="xref py py-obj docutils literal notranslate"><span class="pre">initialize</span></code></a>([init, device, verbose, force_reinit])</p></td> |
| <td><p>Initializes <code class="xref py py-class docutils literal notranslate"><span class="pre">Parameter</span></code> s of this <code class="xref py py-class docutils literal notranslate"><span class="pre">Block</span></code> and its children.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.ModifierCell.load" title="mxnet.gluon.rnn.ModifierCell.load"><code class="xref py py-obj docutils literal notranslate"><span class="pre">load</span></code></a>(prefix)</p></td> |
| <td><p>Load a model saved using the <cite>save</cite> API</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.ModifierCell.load_dict" title="mxnet.gluon.rnn.ModifierCell.load_dict"><code class="xref py py-obj docutils literal notranslate"><span class="pre">load_dict</span></code></a>(param_dict[, device, …])</p></td> |
| <td><p>Load parameters from dict</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.ModifierCell.load_parameters" title="mxnet.gluon.rnn.ModifierCell.load_parameters"><code class="xref py py-obj docutils literal notranslate"><span class="pre">load_parameters</span></code></a>(filename[, device, …])</p></td> |
| <td><p>Load parameters from file previously saved by <cite>save_parameters</cite>.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.ModifierCell.optimize_for" title="mxnet.gluon.rnn.ModifierCell.optimize_for"><code class="xref py py-obj docutils literal notranslate"><span class="pre">optimize_for</span></code></a>(x, *args[, backend, clear, …])</p></td> |
| <td><p>Partitions the current HybridBlock and optimizes it for a given backend without executing a forward pass.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.ModifierCell.register_child" title="mxnet.gluon.rnn.ModifierCell.register_child"><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_child</span></code></a>(block[, name])</p></td> |
| <td><p>Registers block as a child of self.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.ModifierCell.register_forward_hook" title="mxnet.gluon.rnn.ModifierCell.register_forward_hook"><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_forward_hook</span></code></a>(hook)</p></td> |
| <td><p>Registers a forward hook on the block.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.ModifierCell.register_forward_pre_hook" title="mxnet.gluon.rnn.ModifierCell.register_forward_pre_hook"><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_forward_pre_hook</span></code></a>(hook)</p></td> |
| <td><p>Registers a forward pre-hook on the block.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.ModifierCell.register_op_hook" title="mxnet.gluon.rnn.ModifierCell.register_op_hook"><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_op_hook</span></code></a>(callback[, monitor_all])</p></td> |
| <td><p>Install callback monitor.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.ModifierCell.reset" title="mxnet.gluon.rnn.ModifierCell.reset"><code class="xref py py-obj docutils literal notranslate"><span class="pre">reset</span></code></a>()</p></td> |
| <td><p>Reset before re-using the cell for another graph.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.ModifierCell.reset_ctx" title="mxnet.gluon.rnn.ModifierCell.reset_ctx"><code class="xref py py-obj docutils literal notranslate"><span class="pre">reset_ctx</span></code></a>(ctx)</p></td> |
| <td><p>This function has been deprecated.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.ModifierCell.reset_device" title="mxnet.gluon.rnn.ModifierCell.reset_device"><code class="xref py py-obj docutils literal notranslate"><span class="pre">reset_device</span></code></a>(device)</p></td> |
| <td><p>Re-assign all Parameters to other devices.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.ModifierCell.save" title="mxnet.gluon.rnn.ModifierCell.save"><code class="xref py py-obj docutils literal notranslate"><span class="pre">save</span></code></a>(prefix)</p></td> |
| <td><p>Save the model architecture and parameters to load again later</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.ModifierCell.save_parameters" title="mxnet.gluon.rnn.ModifierCell.save_parameters"><code class="xref py py-obj docutils literal notranslate"><span class="pre">save_parameters</span></code></a>(filename[, deduplicate])</p></td> |
| <td><p>Save parameters to file.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.ModifierCell.setattr" title="mxnet.gluon.rnn.ModifierCell.setattr"><code class="xref py py-obj docutils literal notranslate"><span class="pre">setattr</span></code></a>(name, value)</p></td> |
| <td><p>Set an attribute to a new value for all Parameters.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.ModifierCell.share_parameters" title="mxnet.gluon.rnn.ModifierCell.share_parameters"><code class="xref py py-obj docutils literal notranslate"><span class="pre">share_parameters</span></code></a>(shared)</p></td> |
| <td><p>Share parameters recursively inside the model.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.ModifierCell.state_info" title="mxnet.gluon.rnn.ModifierCell.state_info"><code class="xref py py-obj docutils literal notranslate"><span class="pre">state_info</span></code></a>([batch_size])</p></td> |
| <td><p>shape and layout information of states</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.ModifierCell.summary" title="mxnet.gluon.rnn.ModifierCell.summary"><code class="xref py py-obj docutils literal notranslate"><span class="pre">summary</span></code></a>(*inputs)</p></td> |
| <td><p>Print the summary of the model’s output and parameters.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.ModifierCell.unroll" title="mxnet.gluon.rnn.ModifierCell.unroll"><code class="xref py py-obj docutils literal notranslate"><span class="pre">unroll</span></code></a>(length, inputs[, begin_state, …])</p></td> |
| <td><p>Unrolls an RNN cell across time steps.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.ModifierCell.zero_grad" title="mxnet.gluon.rnn.ModifierCell.zero_grad"><code class="xref py py-obj docutils literal notranslate"><span class="pre">zero_grad</span></code></a>()</p></td> |
| <td><p>Sets all Parameters’ gradient buffer to 0.</p></td> |
| </tr> |
| </tbody> |
| </table> |
| <p><strong>Attributes</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.rnn.ModifierCell.params" title="mxnet.gluon.rnn.ModifierCell.params"><code class="xref py py-obj docutils literal notranslate"><span class="pre">params</span></code></a></p></td> |
| <td><p>Return an attribute of instance, which is of type owner.</p></td> |
| </tr> |
| </tbody> |
| </table> |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.ModifierCell.apply"> |
| <code class="sig-name descname">apply</code><span class="sig-paren">(</span><em class="sig-param">fn</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.rnn.ModifierCell.apply" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Applies <code class="docutils literal notranslate"><span class="pre">fn</span></code> recursively to every child block as well as self.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><p><strong>fn</strong> (<em>callable</em>) – Function to be applied to each submodule, of form <cite>fn(block)</cite>.</p> |
| </dd> |
| <dt class="field-even">Returns</dt> |
| <dd class="field-even"><p></p> |
| </dd> |
| <dt class="field-odd">Return type</dt> |
| <dd class="field-odd"><p>this block</p> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.ModifierCell.begin_state"> |
| <code class="sig-name descname">begin_state</code><span class="sig-paren">(</span><em class="sig-param">func=<function zeros></em>, <em class="sig-param">**kwargs</em><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/mxnet/gluon/rnn/rnn_cell.html#ModifierCell.begin_state"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.gluon.rnn.ModifierCell.begin_state" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Initial state for this cell.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>func</strong> (<em>callable</em><em>, </em><em>default symbol.zeros</em>) – <p>Function for creating initial state.</p> |
| <p>For Symbol API, func can be <cite>symbol.zeros</cite>, <cite>symbol.uniform</cite>, |
| <cite>symbol.var etc</cite>. Use <cite>symbol.var</cite> if you want to directly |
| feed input as states.</p> |
| <p>For NDArray API, func can be <cite>ndarray.zeros</cite>, <cite>ndarray.ones</cite>, etc.</p> |
| </p></li> |
| <li><p><strong>batch_size</strong> (<em>int</em><em>, </em><em>default 0</em>) – Only required for NDArray API. Size of the batch (‘N’ in layout) |
| dimension of input.</p></li> |
| <li><p><strong>**kwargs</strong> – Additional keyword arguments passed to func. For example |
| <cite>mean</cite>, <cite>std</cite>, <cite>dtype</cite>, etc.</p></li> |
| </ul> |
| </dd> |
| <dt class="field-even">Returns</dt> |
| <dd class="field-even"><p><strong>states</strong> – Starting states for the first RNN step.</p> |
| </dd> |
| <dt class="field-odd">Return type</dt> |
| <dd class="field-odd"><p>nested list of Symbol</p> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.ModifierCell.cast"> |
| <code class="sig-name descname">cast</code><span class="sig-paren">(</span><em class="sig-param">dtype</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.rnn.ModifierCell.cast" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Cast this Block to use another data type.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><p><strong>dtype</strong> (<em>str</em><em> or </em><em>numpy.dtype</em>) – The new data type.</p> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.ModifierCell.collect_params"> |
| <code class="sig-name descname">collect_params</code><span class="sig-paren">(</span><em class="sig-param">select=None</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.rnn.ModifierCell.collect_params" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Returns a <code class="xref py py-class docutils literal notranslate"><span class="pre">Dict</span></code> containing this <code class="xref py py-class docutils literal notranslate"><span class="pre">Block</span></code> and all of its |
| children’s Parameters(default), also can returns the select <code class="xref py py-class docutils literal notranslate"><span class="pre">Dict</span></code> |
| which match some given regular expressions.</p> |
| <p>For example, collect the specified parameters in [‘conv1.weight’, ‘conv1.bias’, ‘fc.weight’, |
| ‘fc.bias’]:</p> |
| <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">model</span><span class="o">.</span><span class="n">collect_params</span><span class="p">(</span><span class="s1">'conv1.weight|conv1.bias|fc.weight|fc.bias'</span><span class="p">)</span> |
| </pre></div> |
| </div> |
| <p>or collect all parameters whose names end with ‘weight’ or ‘bias’, this can be done |
| using regular expressions:</p> |
| <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">model</span><span class="o">.</span><span class="n">collect_params</span><span class="p">(</span><span class="s1">'.*weight|.*bias'</span><span class="p">)</span> |
| </pre></div> |
| </div> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><p><strong>select</strong> (<em>str</em>) – regular expressions</p> |
| </dd> |
| <dt class="field-even">Returns</dt> |
| <dd class="field-even"><p></p> |
| </dd> |
| <dt class="field-odd">Return type</dt> |
| <dd class="field-odd"><p>The selected <code class="xref py py-class docutils literal notranslate"><span class="pre">Dict</span></code></p> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.ModifierCell.export"> |
| <code class="sig-name descname">export</code><span class="sig-paren">(</span><em class="sig-param">path</em>, <em class="sig-param">epoch=0</em>, <em class="sig-param">remove_amp_cast=True</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.rnn.ModifierCell.export" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Export HybridBlock to json format that can be loaded by |
| <cite>gluon.SymbolBlock.imports</cite> or the C++ interface.</p> |
| <div class="admonition note"> |
| <p class="admonition-title">Note</p> |
| <p>When there are only one input, it will have name <cite>data</cite>. When there |
| Are more than one inputs, they will be named as <cite>data0</cite>, <cite>data1</cite>, etc.</p> |
| </div> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>path</strong> (<em>str</em><em> or </em><em>None</em>) – Path to save model. Two files <cite>path-symbol.json</cite> and <cite>path-xxxx.params</cite> |
| will be created, where xxxx is the 4 digits epoch number. |
| If None, do not export to file but return Python Symbol object and |
| corresponding dictionary of parameters.</p></li> |
| <li><p><strong>epoch</strong> (<em>int</em>) – Epoch number of saved model.</p></li> |
| <li><p><strong>remove_amp_cast</strong> (<em>bool</em><em>, </em><em>optional</em>) – Whether to remove the amp_cast and amp_multicast operators, before saving the model.</p></li> |
| </ul> |
| </dd> |
| <dt class="field-even">Returns</dt> |
| <dd class="field-even"><p><ul class="simple"> |
| <li><p><strong>symbol_filename</strong> (<em>str</em>) – Filename to which model symbols were saved, including <cite>path</cite> prefix.</p></li> |
| <li><p><strong>params_filename</strong> (<em>str</em>) – Filename to which model parameters were saved, including <cite>path</cite> prefix.</p></li> |
| </ul> |
| </p> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.ModifierCell.forward"> |
| <code class="sig-name descname">forward</code><span class="sig-paren">(</span><em class="sig-param">inputs</em>, <em class="sig-param">states</em><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/mxnet/gluon/rnn/rnn_cell.html#ModifierCell.forward"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.gluon.rnn.ModifierCell.forward" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Unrolls the recurrent cell for one time step.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>inputs</strong> (<em>sym.Variable</em>) – Input symbol, 2D, of shape (batch_size * num_units).</p></li> |
| <li><p><strong>states</strong> (<em>list of sym.Variable</em>) – RNN state from previous step or the output of begin_state().</p></li> |
| </ul> |
| </dd> |
| <dt class="field-even">Returns</dt> |
| <dd class="field-even"><p><ul class="simple"> |
| <li><p><strong>output</strong> (<em>Symbol</em>) – Symbol corresponding to the output from the RNN when unrolling |
| for a single time step.</p></li> |
| <li><p><strong>states</strong> (<em>list of Symbol</em>) – The new state of this RNN after this unrolling. |
| The type of this symbol is same as the output of <cite>begin_state()</cite>. |
| This can be used as an input state to the next time step |
| of this RNN.</p></li> |
| </ul> |
| </p> |
| </dd> |
| </dl> |
| <div class="admonition seealso"> |
| <p class="admonition-title">See also</p> |
| <dl class="simple"> |
| <dt><a class="reference internal" href="#mxnet.gluon.rnn.ModifierCell.begin_state" title="mxnet.gluon.rnn.ModifierCell.begin_state"><code class="xref py py-meth docutils literal notranslate"><span class="pre">begin_state()</span></code></a></dt><dd><p>This function can provide the states for the first time step.</p> |
| </dd> |
| <dt><a class="reference internal" href="#mxnet.gluon.rnn.ModifierCell.unroll" title="mxnet.gluon.rnn.ModifierCell.unroll"><code class="xref py py-meth docutils literal notranslate"><span class="pre">unroll()</span></code></a></dt><dd><p>This function unrolls an RNN for a given number of (>=1) time steps.</p> |
| </dd> |
| </dl> |
| </div> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.ModifierCell.hybridize"> |
| <code class="sig-name descname">hybridize</code><span class="sig-paren">(</span><em class="sig-param">active=True</em>, <em class="sig-param">**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.rnn.ModifierCell.hybridize" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Please refer description of HybridBlock hybridize().</p> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.ModifierCell.infer_shape"> |
| <code class="sig-name descname">infer_shape</code><span class="sig-paren">(</span><em class="sig-param">*args</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.rnn.ModifierCell.infer_shape" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Infers shape of Parameters from inputs.</p> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.ModifierCell.infer_type"> |
| <code class="sig-name descname">infer_type</code><span class="sig-paren">(</span><em class="sig-param">*args</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.rnn.ModifierCell.infer_type" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Infers data type of Parameters from inputs.</p> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.ModifierCell.initialize"> |
| <code class="sig-name descname">initialize</code><span class="sig-paren">(</span><em class="sig-param">init=<mxnet.initializer.Uniform object></em>, <em class="sig-param">device=None</em>, <em class="sig-param">verbose=False</em>, <em class="sig-param">force_reinit=False</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.rnn.ModifierCell.initialize" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Initializes <code class="xref py py-class docutils literal notranslate"><span class="pre">Parameter</span></code> s of this <code class="xref py py-class docutils literal notranslate"><span class="pre">Block</span></code> and its children.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>init</strong> (<a class="reference internal" href="../../initializer/index.html#mxnet.initializer.Initializer" title="mxnet.initializer.Initializer"><em>Initializer</em></a>) – Global default Initializer to be used when <code class="xref py py-meth docutils literal notranslate"><span class="pre">Parameter.init()</span></code> is <code class="docutils literal notranslate"><span class="pre">None</span></code>. |
| Otherwise, <code class="xref py py-meth docutils literal notranslate"><span class="pre">Parameter.init()</span></code> takes precedence.</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>) – Keeps a copy of Parameters on one or many device(s).</p></li> |
| <li><p><strong>verbose</strong> (<em>bool</em><em>, </em><em>default False</em>) – Whether to verbosely print out details on initialization.</p></li> |
| <li><p><strong>force_reinit</strong> (<em>bool</em><em>, </em><em>default False</em>) – Whether to force re-initialization if parameter is already initialized.</p></li> |
| </ul> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.ModifierCell.load"> |
| <code class="sig-name descname">load</code><span class="sig-paren">(</span><em class="sig-param">prefix</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.rnn.ModifierCell.load" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Load a model saved using the <cite>save</cite> API</p> |
| <p>Reconfigures a model using the saved configuration. This function |
| does not regenerate the model architecture. It resets each Block’s |
| parameter UUIDs as they were when saved in order to match the names of the |
| saved parameters.</p> |
| <p>This function assumes the Blocks in the model were created in the same |
| order they were when the model was saved. This is because each Block is |
| uniquely identified by Block class name and a unique ID in order (since |
| its an OrderedDict) and uses the unique ID to denote that specific Block.</p> |
| <p>Assumes that the model is created in an identical order every time. |
| If the model is not able to be recreated deterministically do not |
| use this set of APIs to save/load your model.</p> |
| <p>For HybridBlocks, the cached_graph (Symbol & inputs) and settings are |
| restored if it had been hybridized before saving.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><p><strong>prefix</strong> (<em>str</em>) – The prefix to use in filenames for loading this model: |
| <prefix>-model.json and <prefix>-model.params</p> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.ModifierCell.load_dict"> |
| <code class="sig-name descname">load_dict</code><span class="sig-paren">(</span><em class="sig-param">param_dict</em>, <em class="sig-param">device=None</em>, <em class="sig-param">allow_missing=False</em>, <em class="sig-param">ignore_extra=False</em>, <em class="sig-param">cast_dtype=False</em>, <em class="sig-param">dtype_source='current'</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.rnn.ModifierCell.load_dict" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Load parameters from dict</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>param_dict</strong> (<em>dict</em>) – Dictionary containing model 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>, </em><em>optional</em>) – Device context on which the memory is allocated. Default is |
| <cite>mxnet.device.current_device()</cite>.</p></li> |
| <li><p><strong>allow_missing</strong> (<em>bool</em><em>, </em><em>default False</em>) – Whether to silently skip loading parameters not represented in the file.</p></li> |
| <li><p><strong>ignore_extra</strong> (<em>bool</em><em>, </em><em>default False</em>) – Whether to silently ignore parameters from the file that are not |
| present in this dict.</p></li> |
| <li><p><strong>cast_dtype</strong> (<em>bool</em><em>, </em><em>default False</em>) – Cast the data type of the NDArray loaded from the checkpoint to the dtype |
| provided by the Parameter if any</p></li> |
| <li><p><strong>dtype_source</strong> (<em>str</em><em>, </em><em>default 'current'</em>) – must be in {‘current’, ‘saved’} |
| Only valid if cast_dtype=True, specify the source of the dtype for casting |
| the parameters</p></li> |
| </ul> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.ModifierCell.load_parameters"> |
| <code class="sig-name descname">load_parameters</code><span class="sig-paren">(</span><em class="sig-param">filename</em>, <em class="sig-param">device=None</em>, <em class="sig-param">allow_missing=False</em>, <em class="sig-param">ignore_extra=False</em>, <em class="sig-param">cast_dtype=False</em>, <em class="sig-param">dtype_source='current'</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.rnn.ModifierCell.load_parameters" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Load parameters from file previously saved by <cite>save_parameters</cite>.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>filename</strong> (<em>str</em>) – Path to parameter file.</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><em>, </em><em>default cpu</em><em>(</em><em>)</em>) – Device(s) to initialize loaded parameters on.</p></li> |
| <li><p><strong>allow_missing</strong> (<em>bool</em><em>, </em><em>default False</em>) – Whether to silently skip loading parameters not represents in the file.</p></li> |
| <li><p><strong>ignore_extra</strong> (<em>bool</em><em>, </em><em>default False</em>) – Whether to silently ignore parameters from the file that are not |
| present in this Block.</p></li> |
| <li><p><strong>cast_dtype</strong> (<em>bool</em><em>, </em><em>default False</em>) – Cast the data type of the NDArray loaded from the checkpoint to the dtype |
| provided by the Parameter if any.</p></li> |
| <li><p><strong>dtype_source</strong> (<em>str</em><em>, </em><em>default 'current'</em>) – must be in {‘current’, ‘saved’} |
| Only valid if cast_dtype=True, specify the source of the dtype for casting |
| the parameters</p></li> |
| </ul> |
| </dd> |
| </dl> |
| <p class="rubric">References</p> |
| <p><a class="reference external" href="https://mxnet.apache.org/api/python/docs/tutorials/packages/gluon/blocks/save_load_params.html">Saving and Loading Gluon Models</a></p> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.ModifierCell.optimize_for"> |
| <code class="sig-name descname">optimize_for</code><span class="sig-paren">(</span><em class="sig-param">x</em>, <em class="sig-param">*args</em>, <em class="sig-param">backend=None</em>, <em class="sig-param">clear=False</em>, <em class="sig-param">partition_if_dynamic=True</em>, <em class="sig-param">static_alloc=False</em>, <em class="sig-param">static_shape=False</em>, <em class="sig-param">inline_limit=2</em>, <em class="sig-param">forward_bulk_size=None</em>, <em class="sig-param">backward_bulk_size=None</em>, <em class="sig-param">**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.rnn.ModifierCell.optimize_for" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Partitions the current HybridBlock and optimizes it for a given backend |
| without executing a forward pass. Modifies the HybridBlock in-place.</p> |
| <p>Immediately partitions a HybridBlock using the specified backend. Combines |
| the work done in the hybridize API with part of the work done in the forward |
| pass without calling the CachedOp. Can be used in place of hybridize, |
| afterwards <cite>export</cite> can be called or inference can be run. See README.md in |
| example/extensions/lib_subgraph/README.md for more details.</p> |
| <p class="rubric">Examples</p> |
| <p># partition and then export to file |
| block.optimize_for(x, backend=’myPart’) |
| block.export(‘partitioned’)</p> |
| <p># partition and then run inference |
| block.optimize_for(x, backend=’myPart’) |
| block(x)</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>x</strong> (<a class="reference internal" href="../../legacy/ndarray/ndarray.html#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – first input to model</p></li> |
| <li><p><strong>*args</strong> (<a class="reference internal" href="../../legacy/ndarray/ndarray.html#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – other inputs to model</p></li> |
| <li><p><strong>backend</strong> (<em>str</em>) – The name of backend, as registered in <cite>SubgraphBackendRegistry</cite>, default None</p></li> |
| <li><p><strong>backend_opts</strong> (<em>dict of user-specified options to pass to the backend for partitioning</em><em>, </em><em>optional</em>) – Passed on to <cite>PrePartition</cite> and <cite>PostPartition</cite> functions of <cite>SubgraphProperty</cite></p></li> |
| <li><p><strong>clear</strong> (<em>bool</em><em>, </em><em>default False</em>) – clears any previous optimizations</p></li> |
| <li><p><strong>partition_if_dynamic</strong> (<em>bool</em><em>, </em><em>default False</em>) – whether to partition the graph when dynamic shape op exists</p></li> |
| <li><p><strong>static_alloc</strong> (<em>bool</em><em>, </em><em>default False</em>) – Statically allocate memory to improve speed. Memory usage may increase.</p></li> |
| <li><p><strong>static_shape</strong> (<em>bool</em><em>, </em><em>default False</em>) – Optimize for invariant input shapes between iterations. Must also |
| set static_alloc to True. Change of input shapes is still allowed |
| but slower.</p></li> |
| <li><p><strong>inline_limit</strong> (<em>optional int</em><em>, </em><em>default 2</em>) – Maximum number of operators that can be inlined.</p></li> |
| <li><p><strong>forward_bulk_size</strong> (<em>optional int</em><em>, </em><em>default None</em>) – Segment size of bulk execution during forward pass.</p></li> |
| <li><p><strong>backward_bulk_size</strong> (<em>optional int</em><em>, </em><em>default None</em>) – Segment size of bulk execution during backward pass.</p></li> |
| <li><p><strong>**kwargs</strong> (<em>The backend options</em><em>, </em><em>optional</em>) – Passed on to <cite>PrePartition</cite> and <cite>PostPartition</cite> functions of <cite>SubgraphProperty</cite></p></li> |
| </ul> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.ModifierCell.params"> |
| <em class="property">property </em><code class="sig-name descname">params</code><a class="headerlink" href="#mxnet.gluon.rnn.ModifierCell.params" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Return an attribute of instance, which is of type owner.</p> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.ModifierCell.register_child"> |
| <code class="sig-name descname">register_child</code><span class="sig-paren">(</span><em class="sig-param">block</em>, <em class="sig-param">name=None</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.rnn.ModifierCell.register_child" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Registers block as a child of self. <code class="xref py py-class docutils literal notranslate"><span class="pre">Block</span></code> s assigned to self as |
| attributes will be registered automatically.</p> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.ModifierCell.register_forward_hook"> |
| <code class="sig-name descname">register_forward_hook</code><span class="sig-paren">(</span><em class="sig-param">hook</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.rnn.ModifierCell.register_forward_hook" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Registers a forward hook on the block.</p> |
| <p>The hook function is called immediately after <a class="reference internal" href="#mxnet.gluon.rnn.ModifierCell.forward" title="mxnet.gluon.rnn.ModifierCell.forward"><code class="xref py py-func docutils literal notranslate"><span class="pre">forward()</span></code></a>. |
| It should not modify the input or output.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><p><strong>hook</strong> (<em>callable</em>) – The forward hook function of form <cite>hook(block, input, output) -> None</cite>.</p> |
| </dd> |
| <dt class="field-even">Returns</dt> |
| <dd class="field-even"><p></p> |
| </dd> |
| <dt class="field-odd">Return type</dt> |
| <dd class="field-odd"><p><code class="xref py py-class docutils literal notranslate"><span class="pre">mxnet.gluon.utils.HookHandle</span></code></p> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.ModifierCell.register_forward_pre_hook"> |
| <code class="sig-name descname">register_forward_pre_hook</code><span class="sig-paren">(</span><em class="sig-param">hook</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.rnn.ModifierCell.register_forward_pre_hook" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Registers a forward pre-hook on the block.</p> |
| <p>The hook function is called immediately before <a class="reference internal" href="#mxnet.gluon.rnn.ModifierCell.forward" title="mxnet.gluon.rnn.ModifierCell.forward"><code class="xref py py-func docutils literal notranslate"><span class="pre">forward()</span></code></a>. |
| It should not modify the input or output.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><p><strong>hook</strong> (<em>callable</em>) – The forward hook function of form <cite>hook(block, input) -> None</cite>.</p> |
| </dd> |
| <dt class="field-even">Returns</dt> |
| <dd class="field-even"><p></p> |
| </dd> |
| <dt class="field-odd">Return type</dt> |
| <dd class="field-odd"><p><code class="xref py py-class docutils literal notranslate"><span class="pre">mxnet.gluon.utils.HookHandle</span></code></p> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.ModifierCell.register_op_hook"> |
| <code class="sig-name descname">register_op_hook</code><span class="sig-paren">(</span><em class="sig-param">callback</em>, <em class="sig-param">monitor_all=False</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.rnn.ModifierCell.register_op_hook" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Install callback monitor.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>callback</strong> (<em>function</em>) – Function called to inspect the values of the intermediate outputs |
| of blocks after hybridization. It takes 3 parameters: |
| name of the tensor being inspected (str) |
| name of the operator producing or consuming that tensor (str) |
| tensor being inspected (NDArray).</p></li> |
| <li><p><strong>monitor_all</strong> (<em>bool</em><em>, </em><em>default False</em>) – If True, monitor both input and output, otherwise monitor output only.</p></li> |
| </ul> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.ModifierCell.reset"> |
| <code class="sig-name descname">reset</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.rnn.ModifierCell.reset" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Reset before re-using the cell for another graph.</p> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.ModifierCell.reset_ctx"> |
| <code class="sig-name descname">reset_ctx</code><span class="sig-paren">(</span><em class="sig-param">ctx</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.rnn.ModifierCell.reset_ctx" title="Permalink to this definition">¶</a></dt> |
| <dd><p>This function has been deprecated. Please refer to <code class="docutils literal notranslate"><span class="pre">Block.reset_device</span></code>.</p> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.ModifierCell.reset_device"> |
| <code class="sig-name descname">reset_device</code><span class="sig-paren">(</span><em class="sig-param">device</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.rnn.ModifierCell.reset_device" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Re-assign all Parameters to other devices.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><p><strong>device</strong> (Device or list of Device, default <code class="xref py py-meth docutils literal notranslate"><span class="pre">device.current_device()</span></code>.) – Assign Parameter to given device. If device is a list of Device, a |
| copy will be made for each device.</p> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.ModifierCell.save"> |
| <code class="sig-name descname">save</code><span class="sig-paren">(</span><em class="sig-param">prefix</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.rnn.ModifierCell.save" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Save the model architecture and parameters to load again later</p> |
| <p>Saves the model architecture as a nested dictionary where each Block |
| in the model is a dictionary and its children are sub-dictionaries.</p> |
| <p>Each Block is uniquely identified by Block class name and a unique ID. |
| We save each Block’s parameter UUID to restore later in order to match |
| the saved parameters.</p> |
| <p>Recursively traverses a Block’s children in order (since its an |
| OrderedDict) and uses the unique ID to denote that specific Block.</p> |
| <p>Assumes that the model is created in an identical order every time. |
| If the model is not able to be recreated deterministically do not |
| use this set of APIs to save/load your model.</p> |
| <p>For HybridBlocks, the cached_graph is saved (Symbol & inputs) if |
| it has already been hybridized.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><p><strong>prefix</strong> (<em>str</em>) – The prefix to use in filenames for saving this model: |
| <prefix>-model.json and <prefix>-model.params</p> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.ModifierCell.save_parameters"> |
| <code class="sig-name descname">save_parameters</code><span class="sig-paren">(</span><em class="sig-param">filename</em>, <em class="sig-param">deduplicate=False</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.rnn.ModifierCell.save_parameters" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Save parameters to file.</p> |
| <p>Saved parameters can only be loaded with <cite>load_parameters</cite>. Note that this |
| method only saves parameters, not model structure. If you want to save |
| model structures, please use <code class="xref py py-meth docutils literal notranslate"><span class="pre">HybridBlock.export()</span></code>.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>filename</strong> (<em>str</em>) – Path to file.</p></li> |
| <li><p><strong>deduplicate</strong> (<em>bool</em><em>, </em><em>default False</em>) – If True, save shared parameters only once. Otherwise, if a Block |
| contains multiple sub-blocks that share parameters, each of the |
| shared parameters will be separately saved for every sub-block.</p></li> |
| </ul> |
| </dd> |
| </dl> |
| <p class="rubric">References</p> |
| <p><a class="reference external" href="https://mxnet.apache.org/api/python/docs/tutorials/packages/gluon/blocks/save_load_params.html">Saving and Loading Gluon Models</a></p> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.ModifierCell.setattr"> |
| <code class="sig-name descname">setattr</code><span class="sig-paren">(</span><em class="sig-param">name</em>, <em class="sig-param">value</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.rnn.ModifierCell.setattr" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Set an attribute to a new value for all Parameters.</p> |
| <p>For example, set grad_req to null if you don’t need gradient w.r.t a |
| model’s Parameters:</p> |
| <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">model</span><span class="o">.</span><span class="n">setattr</span><span class="p">(</span><span class="s1">'grad_req'</span><span class="p">,</span> <span class="s1">'null'</span><span class="p">)</span> |
| </pre></div> |
| </div> |
| <p>or change the learning rate multiplier:</p> |
| <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">model</span><span class="o">.</span><span class="n">setattr</span><span class="p">(</span><span class="s1">'lr_mult'</span><span class="p">,</span> <span class="mf">0.5</span><span class="p">)</span> |
| </pre></div> |
| </div> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>name</strong> (<em>str</em>) – Name of the attribute.</p></li> |
| <li><p><strong>value</strong> (<em>valid type for attribute name</em>) – The new value for the attribute.</p></li> |
| </ul> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.ModifierCell.share_parameters"> |
| <code class="sig-name descname">share_parameters</code><span class="sig-paren">(</span><em class="sig-param">shared</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.rnn.ModifierCell.share_parameters" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Share parameters recursively inside the model.</p> |
| <p>For example, if you want <code class="docutils literal notranslate"><span class="pre">dense1</span></code> to share <code class="docutils literal notranslate"><span class="pre">dense0</span></code>’s weights, you can do:</p> |
| <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">dense0</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Dense</span><span class="p">(</span><span class="mi">20</span><span class="p">)</span> |
| <span class="n">dense1</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Dense</span><span class="p">(</span><span class="mi">20</span><span class="p">)</span> |
| <span class="n">dense1</span><span class="o">.</span><span class="n">share_parameters</span><span class="p">(</span><span class="n">dense0</span><span class="o">.</span><span class="n">collect_params</span><span class="p">())</span> |
| </pre></div> |
| </div> |
| <dl class="simple"> |
| <dt>which equals to</dt><dd><p>dense1.weight = dense0.weight |
| dense1.bias = dense0.bias</p> |
| </dd> |
| </dl> |
| <p>Note that unlike the <cite>load_parameters</cite> or <cite>load_dict</cite> functions, |
| <cite>share_parameters</cite> results in the <cite>Parameter</cite> object being shared (or |
| tied) between the models, whereas <cite>load_parameters</cite> or <cite>load_dict</cite> only |
| set the value of the data dictionary of a model. If you call |
| <cite>load_parameters</cite> or <cite>load_dict</cite> after <cite>share_parameters</cite>, the loaded |
| value will be reflected in all networks that use the shared (or tied) |
| <cite>Parameter</cite> object.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><p><strong>shared</strong> (<em>Dict</em>) – Dict of the shared parameters.</p> |
| </dd> |
| <dt class="field-even">Returns</dt> |
| <dd class="field-even"><p></p> |
| </dd> |
| <dt class="field-odd">Return type</dt> |
| <dd class="field-odd"><p>this block</p> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.ModifierCell.state_info"> |
| <code class="sig-name descname">state_info</code><span class="sig-paren">(</span><em class="sig-param">batch_size=0</em><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/mxnet/gluon/rnn/rnn_cell.html#ModifierCell.state_info"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.gluon.rnn.ModifierCell.state_info" title="Permalink to this definition">¶</a></dt> |
| <dd><p>shape and layout information of states</p> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.ModifierCell.summary"> |
| <code class="sig-name descname">summary</code><span class="sig-paren">(</span><em class="sig-param">*inputs</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.rnn.ModifierCell.summary" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Print the summary of the model’s output and parameters.</p> |
| <p>The network must have been initialized, and must not have been hybridized.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><p><strong>inputs</strong> (<em>object</em>) – Any input that the model supports. For any tensor in the input, only |
| <a class="reference internal" href="../../legacy/ndarray/ndarray.html#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><code class="xref py py-class docutils literal notranslate"><span class="pre">mxnet.ndarray.NDArray</span></code></a> is supported.</p> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.ModifierCell.unroll"> |
| <code class="sig-name descname">unroll</code><span class="sig-paren">(</span><em class="sig-param">length</em>, <em class="sig-param">inputs</em>, <em class="sig-param">begin_state=None</em>, <em class="sig-param">layout='NTC'</em>, <em class="sig-param">merge_outputs=None</em>, <em class="sig-param">valid_length=None</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.rnn.ModifierCell.unroll" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Unrolls an RNN cell across time steps.</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>) – Number of steps to unroll.</p></li> |
| <li><p><strong>inputs</strong> (<a class="reference internal" href="../../legacy/symbol/symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a><em>, </em><em>list of Symbol</em><em>, or </em><em>None</em>) – <p>If <cite>inputs</cite> is a single Symbol (usually the output |
| of Embedding symbol), it should have shape |
| (batch_size, length, …) if <cite>layout</cite> is ‘NTC’, |
| or (length, batch_size, …) if <cite>layout</cite> is ‘TNC’.</p> |
| <p>If <cite>inputs</cite> is a list of symbols (usually output of |
| previous unroll), they should all have shape |
| (batch_size, …).</p> |
| </p></li> |
| <li><p><strong>begin_state</strong> (<em>nested list of Symbol</em><em>, </em><em>optional</em>) – Input states created by <cite>begin_state()</cite> |
| or output state of another cell. |
| Created from <cite>begin_state()</cite> if <cite>None</cite>.</p></li> |
| <li><p><strong>layout</strong> (<em>str</em><em>, </em><em>optional</em>) – <cite>layout</cite> of input symbol. Only used if inputs |
| is a single Symbol.</p></li> |
| <li><p><strong>merge_outputs</strong> (<em>bool</em><em>, </em><em>optional</em>) – If <cite>False</cite>, returns outputs as a list of Symbols. |
| If <cite>True</cite>, concatenates output across time steps |
| and returns a single symbol with shape |
| (batch_size, length, …) if layout is ‘NTC’, |
| or (length, batch_size, …) if layout is ‘TNC’. |
| If <cite>None</cite>, output whatever is faster.</p></li> |
| <li><p><strong>valid_length</strong> (<a class="reference internal" href="../../legacy/symbol/symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a><em>, </em><a class="reference internal" href="../../legacy/ndarray/ndarray.html#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a><em> or </em><em>None</em>) – <cite>valid_length</cite> specifies the length of the sequences in the batch without padding. |
| This option is especially useful for building sequence-to-sequence models where |
| the input and output sequences would potentially be padded. |
| If <cite>valid_length</cite> is None, all sequences are assumed to have the same length. |
| If <cite>valid_length</cite> is a Symbol or NDArray, it should have shape (batch_size,). |
| The ith element will be the length of the ith sequence in the batch. |
| The last valid state will be return and the padded outputs will be masked with 0. |
| Note that <cite>valid_length</cite> must be smaller or equal to <cite>length</cite>.</p></li> |
| </ul> |
| </dd> |
| <dt class="field-even">Returns</dt> |
| <dd class="field-even"><p><ul class="simple"> |
| <li><p><strong>outputs</strong> (<em>list of Symbol or Symbol</em>) – Symbol (if <cite>merge_outputs</cite> is True) or list of Symbols |
| (if <cite>merge_outputs</cite> is False) corresponding to the output from |
| the RNN from this unrolling.</p></li> |
| <li><p><strong>states</strong> (<em>list of Symbol</em>) – The new state of this RNN after this unrolling. |
| The type of this symbol is same as the output of <cite>begin_state()</cite>.</p></li> |
| </ul> |
| </p> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.ModifierCell.zero_grad"> |
| <code class="sig-name descname">zero_grad</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.rnn.ModifierCell.zero_grad" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Sets all Parameters’ gradient buffer to 0.</p> |
| </dd></dl> |
| |
| </dd></dl> |
| |
| <dl class="class"> |
| <dt id="mxnet.gluon.rnn.RNN"> |
| <em class="property">class </em><code class="sig-name descname">RNN</code><span class="sig-paren">(</span><em class="sig-param">hidden_size</em>, <em class="sig-param">num_layers=1</em>, <em class="sig-param">activation='relu'</em>, <em class="sig-param">layout='TNC'</em>, <em class="sig-param">dropout=0</em>, <em class="sig-param">bidirectional=False</em>, <em class="sig-param">i2h_weight_initializer=None</em>, <em class="sig-param">h2h_weight_initializer=None</em>, <em class="sig-param">i2h_bias_initializer='zeros'</em>, <em class="sig-param">h2h_bias_initializer='zeros'</em>, <em class="sig-param">input_size=0</em>, <em class="sig-param">dtype='float32'</em>, <em class="sig-param">**kwargs</em><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/mxnet/gluon/rnn/rnn_layer.html#RNN"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.gluon.rnn.RNN" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">mxnet.gluon.rnn.rnn_layer._RNNLayer</span></code></p> |
| <p>Applies a multi-layer Elman RNN with <cite>tanh</cite> or <cite>ReLU</cite> non-linearity to an input sequence.</p> |
| <p>For each element in the input sequence, each layer computes the following |
| function:</p> |
| <div class="math notranslate nohighlight"> |
| \[h_t = \tanh(w_{ih} * x_t + b_{ih} + w_{hh} * h_{(t-1)} + b_{hh})\]</div> |
| <p>where <span class="math notranslate nohighlight">\(h_t\)</span> is the hidden state at time <cite>t</cite>, and <span class="math notranslate nohighlight">\(x_t\)</span> is the output |
| of the previous layer at time <cite>t</cite> or <span class="math notranslate nohighlight">\(input_t\)</span> for the first layer. |
| If nonlinearity=’relu’, then <cite>ReLU</cite> is used instead of <cite>tanh</cite>.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>hidden_size</strong> (<em>int</em>) – The number of features in the hidden state h.</p></li> |
| <li><p><strong>num_layers</strong> (<em>int</em><em>, </em><em>default 1</em>) – Number of recurrent layers.</p></li> |
| <li><p><strong>activation</strong> (<em>{'relu'</em><em> or </em><em>'tanh'}</em><em>, </em><em>default 'relu'</em>) – The activation function to use.</p></li> |
| <li><p><strong>layout</strong> (<em>str</em><em>, </em><em>default 'TNC'</em>) – The format of input and output tensors. T, N and C stand for |
| sequence length, batch size, and feature dimensions respectively.</p></li> |
| <li><p><strong>dropout</strong> (<em>float</em><em>, </em><em>default 0</em>) – If non-zero, introduces a dropout layer on the outputs of each |
| RNN layer except the last layer.</p></li> |
| <li><p><strong>bidirectional</strong> (<em>bool</em><em>, </em><em>default False</em>) – If <cite>True</cite>, becomes a bidirectional RNN.</p></li> |
| <li><p><strong>i2h_weight_initializer</strong> (<em>str</em><em> or </em><a class="reference internal" href="../../initializer/index.html#mxnet.initializer.Initializer" title="mxnet.initializer.Initializer"><em>Initializer</em></a>) – Initializer for the input weights matrix, used for the linear |
| transformation of the inputs.</p></li> |
| <li><p><strong>h2h_weight_initializer</strong> (<em>str</em><em> or </em><a class="reference internal" href="../../initializer/index.html#mxnet.initializer.Initializer" title="mxnet.initializer.Initializer"><em>Initializer</em></a>) – Initializer for the recurrent weights matrix, used for the linear |
| transformation of the recurrent state.</p></li> |
| <li><p><strong>i2h_bias_initializer</strong> (<em>str</em><em> or </em><a class="reference internal" href="../../initializer/index.html#mxnet.initializer.Initializer" title="mxnet.initializer.Initializer"><em>Initializer</em></a>) – Initializer for the bias vector.</p></li> |
| <li><p><strong>h2h_bias_initializer</strong> (<em>str</em><em> or </em><a class="reference internal" href="../../initializer/index.html#mxnet.initializer.Initializer" title="mxnet.initializer.Initializer"><em>Initializer</em></a>) – Initializer for the bias vector.</p></li> |
| <li><p><strong>input_size</strong> (<em>int</em><em>, </em><em>default 0</em>) – The number of expected features in the input x. |
| If not specified, it will be inferred from input.</p></li> |
| <li><p><strong>dtype</strong> (<em>str</em><em>, </em><em>default 'float32'</em>) – Type to initialize the parameters and default states to</p></li> |
| </ul> |
| </dd> |
| </dl> |
| <dl class="simple"> |
| <dt>Inputs:</dt><dd><ul class="simple"> |
| <li><p><strong>data</strong>: input tensor with shape <cite>(sequence_length, batch_size, input_size)</cite> |
| when <cite>layout</cite> is “TNC”. For other layouts, dimensions are permuted accordingly |
| using transpose() operator which adds performance overhead. Consider creating |
| batches in TNC layout during data batching step.</p></li> |
| <li><p><strong>states</strong>: initial recurrent state tensor with shape |
| <cite>(num_layers, batch_size, num_hidden)</cite>. If <cite>bidirectional</cite> is True, |
| shape will instead be <cite>(2*num_layers, batch_size, num_hidden)</cite>. If |
| <cite>states</cite> is None, zeros will be used as default begin states.</p></li> |
| </ul> |
| </dd> |
| <dt>Outputs:</dt><dd><ul class="simple"> |
| <li><p><strong>out</strong>: output tensor with shape <cite>(sequence_length, batch_size, num_hidden)</cite> |
| when <cite>layout</cite> is “TNC”. If <cite>bidirectional</cite> is True, output shape will instead |
| be <cite>(sequence_length, batch_size, 2*num_hidden)</cite></p></li> |
| <li><p><strong>out_states</strong>: output recurrent state tensor with the same shape as <cite>states</cite>. |
| If <cite>states</cite> is None <cite>out_states</cite> will not be returned.</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">layer</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">gluon</span><span class="o">.</span><span class="n">rnn</span><span class="o">.</span><span class="n">RNN</span><span class="p">(</span><span class="mi">100</span><span class="p">,</span> <span class="mi">3</span><span class="p">)</span> |
| <span class="gp">>>> </span><span class="n">layer</span><span class="o">.</span><span class="n">initialize</span><span class="p">()</span> |
| <span class="gp">>>> </span><span class="nb">input</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">uniform</span><span class="p">(</span><span class="n">size</span><span class="o">=</span><span class="p">(</span><span class="mi">5</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">10</span><span class="p">))</span> |
| <span class="gp">>>> </span><span class="c1"># by default zeros are used as begin state</span> |
| <span class="gp">>>> </span><span class="n">output</span> <span class="o">=</span> <span class="n">layer</span><span class="p">(</span><span class="nb">input</span><span class="p">)</span> |
| <span class="gp">>>> </span><span class="c1"># manually specify begin state.</span> |
| <span class="gp">>>> </span><span class="n">h0</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">uniform</span><span class="p">(</span><span class="n">size</span><span class="o">=</span><span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">100</span><span class="p">))</span> |
| <span class="gp">>>> </span><span class="n">output</span><span class="p">,</span> <span class="n">hn</span> <span class="o">=</span> <span class="n">layer</span><span class="p">(</span><span class="nb">input</span><span class="p">,</span> <span class="n">h0</span><span class="p">)</span> |
| </pre></div> |
| </div> |
| </dd></dl> |
| |
| <dl class="class"> |
| <dt id="mxnet.gluon.rnn.RNNCell"> |
| <em class="property">class </em><code class="sig-name descname">RNNCell</code><span class="sig-paren">(</span><em class="sig-param">hidden_size</em>, <em class="sig-param">activation='tanh'</em>, <em class="sig-param">i2h_weight_initializer=None</em>, <em class="sig-param">h2h_weight_initializer=None</em>, <em class="sig-param">i2h_bias_initializer='zeros'</em>, <em class="sig-param">h2h_bias_initializer='zeros'</em>, <em class="sig-param">input_size=0</em><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/mxnet/gluon/rnn/rnn_cell.html#RNNCell"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.gluon.rnn.RNNCell" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">mxnet.gluon.rnn.rnn_cell.HybridRecurrentCell</span></code></p> |
| <p>Elman RNN recurrent neural network cell.</p> |
| <p>Each call computes the following function:</p> |
| <div class="math notranslate nohighlight"> |
| \[h_t = \tanh(w_{ih} * x_t + b_{ih} + w_{hh} * h_{(t-1)} + b_{hh})\]</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.rnn.RNNCell.apply" title="mxnet.gluon.rnn.RNNCell.apply"><code class="xref py py-obj docutils literal notranslate"><span class="pre">apply</span></code></a>(fn)</p></td> |
| <td><p>Applies <code class="docutils literal notranslate"><span class="pre">fn</span></code> recursively to every child block as well as self.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.RNNCell.begin_state" title="mxnet.gluon.rnn.RNNCell.begin_state"><code class="xref py py-obj docutils literal notranslate"><span class="pre">begin_state</span></code></a>([batch_size, func])</p></td> |
| <td><p>Initial state for this cell.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.RNNCell.cast" title="mxnet.gluon.rnn.RNNCell.cast"><code class="xref py py-obj docutils literal notranslate"><span class="pre">cast</span></code></a>(dtype)</p></td> |
| <td><p>Cast this Block to use another data type.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.RNNCell.collect_params" title="mxnet.gluon.rnn.RNNCell.collect_params"><code class="xref py py-obj docutils literal notranslate"><span class="pre">collect_params</span></code></a>([select])</p></td> |
| <td><p>Returns a <code class="xref py py-class docutils literal notranslate"><span class="pre">Dict</span></code> containing this <code class="xref py py-class docutils literal notranslate"><span class="pre">Block</span></code> and all of its children’s Parameters(default), also can returns the select <code class="xref py py-class docutils literal notranslate"><span class="pre">Dict</span></code> which match some given regular expressions.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.RNNCell.export" title="mxnet.gluon.rnn.RNNCell.export"><code class="xref py py-obj docutils literal notranslate"><span class="pre">export</span></code></a>(path[, epoch, remove_amp_cast])</p></td> |
| <td><p>Export HybridBlock to json format that can be loaded by <cite>gluon.SymbolBlock.imports</cite> or the C++ interface.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.RNNCell.forward" title="mxnet.gluon.rnn.RNNCell.forward"><code class="xref py py-obj docutils literal notranslate"><span class="pre">forward</span></code></a>(inputs, states)</p></td> |
| <td><p>Unrolls the recurrent cell for one time step.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.RNNCell.hybridize" title="mxnet.gluon.rnn.RNNCell.hybridize"><code class="xref py py-obj docutils literal notranslate"><span class="pre">hybridize</span></code></a>([active])</p></td> |
| <td><p>Please refer description of HybridBlock hybridize().</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.RNNCell.infer_shape" title="mxnet.gluon.rnn.RNNCell.infer_shape"><code class="xref py py-obj docutils literal notranslate"><span class="pre">infer_shape</span></code></a>(i, x, is_bidirect)</p></td> |
| <td><p>Infers shape of Parameters from inputs.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.RNNCell.infer_type" title="mxnet.gluon.rnn.RNNCell.infer_type"><code class="xref py py-obj docutils literal notranslate"><span class="pre">infer_type</span></code></a>(*args)</p></td> |
| <td><p>Infers data type of Parameters from inputs.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.RNNCell.initialize" title="mxnet.gluon.rnn.RNNCell.initialize"><code class="xref py py-obj docutils literal notranslate"><span class="pre">initialize</span></code></a>([init, device, verbose, force_reinit])</p></td> |
| <td><p>Initializes <code class="xref py py-class docutils literal notranslate"><span class="pre">Parameter</span></code> s of this <code class="xref py py-class docutils literal notranslate"><span class="pre">Block</span></code> and its children.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.RNNCell.load" title="mxnet.gluon.rnn.RNNCell.load"><code class="xref py py-obj docutils literal notranslate"><span class="pre">load</span></code></a>(prefix)</p></td> |
| <td><p>Load a model saved using the <cite>save</cite> API</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.RNNCell.load_dict" title="mxnet.gluon.rnn.RNNCell.load_dict"><code class="xref py py-obj docutils literal notranslate"><span class="pre">load_dict</span></code></a>(param_dict[, device, …])</p></td> |
| <td><p>Load parameters from dict</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.RNNCell.load_parameters" title="mxnet.gluon.rnn.RNNCell.load_parameters"><code class="xref py py-obj docutils literal notranslate"><span class="pre">load_parameters</span></code></a>(filename[, device, …])</p></td> |
| <td><p>Load parameters from file previously saved by <cite>save_parameters</cite>.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.RNNCell.optimize_for" title="mxnet.gluon.rnn.RNNCell.optimize_for"><code class="xref py py-obj docutils literal notranslate"><span class="pre">optimize_for</span></code></a>(x, *args[, backend, clear, …])</p></td> |
| <td><p>Partitions the current HybridBlock and optimizes it for a given backend without executing a forward pass.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.RNNCell.register_child" title="mxnet.gluon.rnn.RNNCell.register_child"><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_child</span></code></a>(block[, name])</p></td> |
| <td><p>Registers block as a child of self.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.RNNCell.register_forward_hook" title="mxnet.gluon.rnn.RNNCell.register_forward_hook"><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_forward_hook</span></code></a>(hook)</p></td> |
| <td><p>Registers a forward hook on the block.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.RNNCell.register_forward_pre_hook" title="mxnet.gluon.rnn.RNNCell.register_forward_pre_hook"><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_forward_pre_hook</span></code></a>(hook)</p></td> |
| <td><p>Registers a forward pre-hook on the block.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.RNNCell.register_op_hook" title="mxnet.gluon.rnn.RNNCell.register_op_hook"><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_op_hook</span></code></a>(callback[, monitor_all])</p></td> |
| <td><p>Install callback monitor.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.RNNCell.reset" title="mxnet.gluon.rnn.RNNCell.reset"><code class="xref py py-obj docutils literal notranslate"><span class="pre">reset</span></code></a>()</p></td> |
| <td><p>Reset before re-using the cell for another graph.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.RNNCell.reset_ctx" title="mxnet.gluon.rnn.RNNCell.reset_ctx"><code class="xref py py-obj docutils literal notranslate"><span class="pre">reset_ctx</span></code></a>(ctx)</p></td> |
| <td><p>This function has been deprecated.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.RNNCell.reset_device" title="mxnet.gluon.rnn.RNNCell.reset_device"><code class="xref py py-obj docutils literal notranslate"><span class="pre">reset_device</span></code></a>(device)</p></td> |
| <td><p>Re-assign all Parameters to other devices.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.RNNCell.save" title="mxnet.gluon.rnn.RNNCell.save"><code class="xref py py-obj docutils literal notranslate"><span class="pre">save</span></code></a>(prefix)</p></td> |
| <td><p>Save the model architecture and parameters to load again later</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.RNNCell.save_parameters" title="mxnet.gluon.rnn.RNNCell.save_parameters"><code class="xref py py-obj docutils literal notranslate"><span class="pre">save_parameters</span></code></a>(filename[, deduplicate])</p></td> |
| <td><p>Save parameters to file.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.RNNCell.setattr" title="mxnet.gluon.rnn.RNNCell.setattr"><code class="xref py py-obj docutils literal notranslate"><span class="pre">setattr</span></code></a>(name, value)</p></td> |
| <td><p>Set an attribute to a new value for all Parameters.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.RNNCell.share_parameters" title="mxnet.gluon.rnn.RNNCell.share_parameters"><code class="xref py py-obj docutils literal notranslate"><span class="pre">share_parameters</span></code></a>(shared)</p></td> |
| <td><p>Share parameters recursively inside the model.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.RNNCell.state_info" title="mxnet.gluon.rnn.RNNCell.state_info"><code class="xref py py-obj docutils literal notranslate"><span class="pre">state_info</span></code></a>([batch_size])</p></td> |
| <td><p>shape and layout information of states</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.RNNCell.summary" title="mxnet.gluon.rnn.RNNCell.summary"><code class="xref py py-obj docutils literal notranslate"><span class="pre">summary</span></code></a>(*inputs)</p></td> |
| <td><p>Print the summary of the model’s output and parameters.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.RNNCell.unroll" title="mxnet.gluon.rnn.RNNCell.unroll"><code class="xref py py-obj docutils literal notranslate"><span class="pre">unroll</span></code></a>(length, inputs[, begin_state, …])</p></td> |
| <td><p>Unrolls an RNN cell across time steps.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.RNNCell.zero_grad" title="mxnet.gluon.rnn.RNNCell.zero_grad"><code class="xref py py-obj docutils literal notranslate"><span class="pre">zero_grad</span></code></a>()</p></td> |
| <td><p>Sets all Parameters’ gradient buffer to 0.</p></td> |
| </tr> |
| </tbody> |
| </table> |
| <p><strong>Attributes</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.rnn.RNNCell.params" title="mxnet.gluon.rnn.RNNCell.params"><code class="xref py py-obj docutils literal notranslate"><span class="pre">params</span></code></a></p></td> |
| <td><p>Returns this <code class="xref py py-class docutils literal notranslate"><span class="pre">Block</span></code>’s parameter dictionary (does not include its children’s parameters).</p></td> |
| </tr> |
| </tbody> |
| </table> |
| <p>where <span class="math notranslate nohighlight">\(h_t\)</span> is the hidden state at time <cite>t</cite>, and <span class="math notranslate nohighlight">\(x_t\)</span> is the hidden |
| state of the previous layer at time <cite>t</cite> or <span class="math notranslate nohighlight">\(input_t\)</span> for the first layer. |
| If nonlinearity=’relu’, then <cite>ReLU</cite> is used instead of <cite>tanh</cite>.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>hidden_size</strong> (<em>int</em>) – Number of units in output symbol</p></li> |
| <li><p><strong>activation</strong> (<em>str</em><em> or </em><a class="reference internal" href="../../legacy/symbol/symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a><em>, </em><em>default 'tanh'</em>) – Type of activation function.</p></li> |
| <li><p><strong>i2h_weight_initializer</strong> (<em>str</em><em> or </em><a class="reference internal" href="../../initializer/index.html#mxnet.initializer.Initializer" title="mxnet.initializer.Initializer"><em>Initializer</em></a>) – Initializer for the input weights matrix, used for the linear |
| transformation of the inputs.</p></li> |
| <li><p><strong>h2h_weight_initializer</strong> (<em>str</em><em> or </em><a class="reference internal" href="../../initializer/index.html#mxnet.initializer.Initializer" title="mxnet.initializer.Initializer"><em>Initializer</em></a>) – Initializer for the recurrent weights matrix, used for the linear |
| transformation of the recurrent state.</p></li> |
| <li><p><strong>i2h_bias_initializer</strong> (<em>str</em><em> or </em><a class="reference internal" href="../../initializer/index.html#mxnet.initializer.Initializer" title="mxnet.initializer.Initializer"><em>Initializer</em></a><em>, </em><em>default 'zeros'</em>) – Initializer for the bias vector.</p></li> |
| <li><p><strong>h2h_bias_initializer</strong> (<em>str</em><em> or </em><a class="reference internal" href="../../initializer/index.html#mxnet.initializer.Initializer" title="mxnet.initializer.Initializer"><em>Initializer</em></a><em>, </em><em>default 'zeros'</em>) – Initializer for the bias vector.</p></li> |
| <li><p><strong>input_size</strong> (<em>int</em><em>, </em><em>default 0</em>) – The number of expected features in the input x. |
| If not specified, it will be inferred from input.</p></li> |
| </ul> |
| </dd> |
| </dl> |
| <dl class="simple"> |
| <dt>Inputs:</dt><dd><ul class="simple"> |
| <li><p><strong>data</strong>: input tensor with shape <cite>(batch_size, input_size)</cite>.</p></li> |
| <li><p><strong>states</strong>: a list of one initial recurrent state tensor with shape |
| <cite>(batch_size, num_hidden)</cite>.</p></li> |
| </ul> |
| </dd> |
| <dt>Outputs:</dt><dd><ul class="simple"> |
| <li><p><strong>out</strong>: output tensor with shape <cite>(batch_size, num_hidden)</cite>.</p></li> |
| <li><p><strong>next_states</strong>: a list of one output recurrent state tensor with the |
| same shape as <cite>states</cite>.</p></li> |
| </ul> |
| </dd> |
| </dl> |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.RNNCell.apply"> |
| <code class="sig-name descname">apply</code><span class="sig-paren">(</span><em class="sig-param">fn</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.rnn.RNNCell.apply" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Applies <code class="docutils literal notranslate"><span class="pre">fn</span></code> recursively to every child block as well as self.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><p><strong>fn</strong> (<em>callable</em>) – Function to be applied to each submodule, of form <cite>fn(block)</cite>.</p> |
| </dd> |
| <dt class="field-even">Returns</dt> |
| <dd class="field-even"><p></p> |
| </dd> |
| <dt class="field-odd">Return type</dt> |
| <dd class="field-odd"><p>this block</p> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.RNNCell.begin_state"> |
| <code class="sig-name descname">begin_state</code><span class="sig-paren">(</span><em class="sig-param">batch_size=0</em>, <em class="sig-param">func=<function zeros></em>, <em class="sig-param">**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.rnn.RNNCell.begin_state" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Initial state for this cell.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>func</strong> (<em>callable</em><em>, </em><em>default symbol.zeros</em>) – <p>Function for creating initial state.</p> |
| <p>For Symbol API, func can be <cite>symbol.zeros</cite>, <cite>symbol.uniform</cite>, |
| <cite>symbol.var etc</cite>. Use <cite>symbol.var</cite> if you want to directly |
| feed input as states.</p> |
| <p>For NDArray API, func can be <cite>ndarray.zeros</cite>, <cite>ndarray.ones</cite>, etc.</p> |
| </p></li> |
| <li><p><strong>batch_size</strong> (<em>int</em><em>, </em><em>default 0</em>) – Only required for NDArray API. Size of the batch (‘N’ in layout) |
| dimension of input.</p></li> |
| <li><p><strong>**kwargs</strong> – Additional keyword arguments passed to func. For example |
| <cite>mean</cite>, <cite>std</cite>, <cite>dtype</cite>, etc.</p></li> |
| </ul> |
| </dd> |
| <dt class="field-even">Returns</dt> |
| <dd class="field-even"><p><strong>states</strong> – Starting states for the first RNN step.</p> |
| </dd> |
| <dt class="field-odd">Return type</dt> |
| <dd class="field-odd"><p>nested list of Symbol</p> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.RNNCell.cast"> |
| <code class="sig-name descname">cast</code><span class="sig-paren">(</span><em class="sig-param">dtype</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.rnn.RNNCell.cast" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Cast this Block to use another data type.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><p><strong>dtype</strong> (<em>str</em><em> or </em><em>numpy.dtype</em>) – The new data type.</p> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.RNNCell.collect_params"> |
| <code class="sig-name descname">collect_params</code><span class="sig-paren">(</span><em class="sig-param">select=None</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.rnn.RNNCell.collect_params" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Returns a <code class="xref py py-class docutils literal notranslate"><span class="pre">Dict</span></code> containing this <code class="xref py py-class docutils literal notranslate"><span class="pre">Block</span></code> and all of its |
| children’s Parameters(default), also can returns the select <code class="xref py py-class docutils literal notranslate"><span class="pre">Dict</span></code> |
| which match some given regular expressions.</p> |
| <p>For example, collect the specified parameters in [‘conv1.weight’, ‘conv1.bias’, ‘fc.weight’, |
| ‘fc.bias’]:</p> |
| <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">model</span><span class="o">.</span><span class="n">collect_params</span><span class="p">(</span><span class="s1">'conv1.weight|conv1.bias|fc.weight|fc.bias'</span><span class="p">)</span> |
| </pre></div> |
| </div> |
| <p>or collect all parameters whose names end with ‘weight’ or ‘bias’, this can be done |
| using regular expressions:</p> |
| <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">model</span><span class="o">.</span><span class="n">collect_params</span><span class="p">(</span><span class="s1">'.*weight|.*bias'</span><span class="p">)</span> |
| </pre></div> |
| </div> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><p><strong>select</strong> (<em>str</em>) – regular expressions</p> |
| </dd> |
| <dt class="field-even">Returns</dt> |
| <dd class="field-even"><p></p> |
| </dd> |
| <dt class="field-odd">Return type</dt> |
| <dd class="field-odd"><p>The selected <code class="xref py py-class docutils literal notranslate"><span class="pre">Dict</span></code></p> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.RNNCell.export"> |
| <code class="sig-name descname">export</code><span class="sig-paren">(</span><em class="sig-param">path</em>, <em class="sig-param">epoch=0</em>, <em class="sig-param">remove_amp_cast=True</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.rnn.RNNCell.export" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Export HybridBlock to json format that can be loaded by |
| <cite>gluon.SymbolBlock.imports</cite> or the C++ interface.</p> |
| <div class="admonition note"> |
| <p class="admonition-title">Note</p> |
| <p>When there are only one input, it will have name <cite>data</cite>. When there |
| Are more than one inputs, they will be named as <cite>data0</cite>, <cite>data1</cite>, etc.</p> |
| </div> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>path</strong> (<em>str</em><em> or </em><em>None</em>) – Path to save model. Two files <cite>path-symbol.json</cite> and <cite>path-xxxx.params</cite> |
| will be created, where xxxx is the 4 digits epoch number. |
| If None, do not export to file but return Python Symbol object and |
| corresponding dictionary of parameters.</p></li> |
| <li><p><strong>epoch</strong> (<em>int</em>) – Epoch number of saved model.</p></li> |
| <li><p><strong>remove_amp_cast</strong> (<em>bool</em><em>, </em><em>optional</em>) – Whether to remove the amp_cast and amp_multicast operators, before saving the model.</p></li> |
| </ul> |
| </dd> |
| <dt class="field-even">Returns</dt> |
| <dd class="field-even"><p><ul class="simple"> |
| <li><p><strong>symbol_filename</strong> (<em>str</em>) – Filename to which model symbols were saved, including <cite>path</cite> prefix.</p></li> |
| <li><p><strong>params_filename</strong> (<em>str</em>) – Filename to which model parameters were saved, including <cite>path</cite> prefix.</p></li> |
| </ul> |
| </p> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.RNNCell.forward"> |
| <code class="sig-name descname">forward</code><span class="sig-paren">(</span><em class="sig-param">inputs</em>, <em class="sig-param">states</em><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/mxnet/gluon/rnn/rnn_cell.html#RNNCell.forward"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.gluon.rnn.RNNCell.forward" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Unrolls the recurrent cell for one time step.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>inputs</strong> (<em>sym.Variable</em>) – Input symbol, 2D, of shape (batch_size * num_units).</p></li> |
| <li><p><strong>states</strong> (<em>list of sym.Variable</em>) – RNN state from previous step or the output of begin_state().</p></li> |
| </ul> |
| </dd> |
| <dt class="field-even">Returns</dt> |
| <dd class="field-even"><p><ul class="simple"> |
| <li><p><strong>output</strong> (<em>Symbol</em>) – Symbol corresponding to the output from the RNN when unrolling |
| for a single time step.</p></li> |
| <li><p><strong>states</strong> (<em>list of Symbol</em>) – The new state of this RNN after this unrolling. |
| The type of this symbol is same as the output of <cite>begin_state()</cite>. |
| This can be used as an input state to the next time step |
| of this RNN.</p></li> |
| </ul> |
| </p> |
| </dd> |
| </dl> |
| <div class="admonition seealso"> |
| <p class="admonition-title">See also</p> |
| <dl class="simple"> |
| <dt><a class="reference internal" href="#mxnet.gluon.rnn.RNNCell.begin_state" title="mxnet.gluon.rnn.RNNCell.begin_state"><code class="xref py py-meth docutils literal notranslate"><span class="pre">begin_state()</span></code></a></dt><dd><p>This function can provide the states for the first time step.</p> |
| </dd> |
| <dt><a class="reference internal" href="#mxnet.gluon.rnn.RNNCell.unroll" title="mxnet.gluon.rnn.RNNCell.unroll"><code class="xref py py-meth docutils literal notranslate"><span class="pre">unroll()</span></code></a></dt><dd><p>This function unrolls an RNN for a given number of (>=1) time steps.</p> |
| </dd> |
| </dl> |
| </div> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.RNNCell.hybridize"> |
| <code class="sig-name descname">hybridize</code><span class="sig-paren">(</span><em class="sig-param">active=True</em>, <em class="sig-param">**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.rnn.RNNCell.hybridize" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Please refer description of HybridBlock hybridize().</p> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.RNNCell.infer_shape"> |
| <code class="sig-name descname">infer_shape</code><span class="sig-paren">(</span><em class="sig-param">i</em>, <em class="sig-param">x</em>, <em class="sig-param">is_bidirect</em><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/mxnet/gluon/rnn/rnn_cell.html#RNNCell.infer_shape"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.gluon.rnn.RNNCell.infer_shape" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Infers shape of Parameters from inputs.</p> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.RNNCell.infer_type"> |
| <code class="sig-name descname">infer_type</code><span class="sig-paren">(</span><em class="sig-param">*args</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.rnn.RNNCell.infer_type" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Infers data type of Parameters from inputs.</p> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.RNNCell.initialize"> |
| <code class="sig-name descname">initialize</code><span class="sig-paren">(</span><em class="sig-param">init=<mxnet.initializer.Uniform object></em>, <em class="sig-param">device=None</em>, <em class="sig-param">verbose=False</em>, <em class="sig-param">force_reinit=False</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.rnn.RNNCell.initialize" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Initializes <code class="xref py py-class docutils literal notranslate"><span class="pre">Parameter</span></code> s of this <code class="xref py py-class docutils literal notranslate"><span class="pre">Block</span></code> and its children.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>init</strong> (<a class="reference internal" href="../../initializer/index.html#mxnet.initializer.Initializer" title="mxnet.initializer.Initializer"><em>Initializer</em></a>) – Global default Initializer to be used when <code class="xref py py-meth docutils literal notranslate"><span class="pre">Parameter.init()</span></code> is <code class="docutils literal notranslate"><span class="pre">None</span></code>. |
| Otherwise, <code class="xref py py-meth docutils literal notranslate"><span class="pre">Parameter.init()</span></code> takes precedence.</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>) – Keeps a copy of Parameters on one or many device(s).</p></li> |
| <li><p><strong>verbose</strong> (<em>bool</em><em>, </em><em>default False</em>) – Whether to verbosely print out details on initialization.</p></li> |
| <li><p><strong>force_reinit</strong> (<em>bool</em><em>, </em><em>default False</em>) – Whether to force re-initialization if parameter is already initialized.</p></li> |
| </ul> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.RNNCell.load"> |
| <code class="sig-name descname">load</code><span class="sig-paren">(</span><em class="sig-param">prefix</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.rnn.RNNCell.load" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Load a model saved using the <cite>save</cite> API</p> |
| <p>Reconfigures a model using the saved configuration. This function |
| does not regenerate the model architecture. It resets each Block’s |
| parameter UUIDs as they were when saved in order to match the names of the |
| saved parameters.</p> |
| <p>This function assumes the Blocks in the model were created in the same |
| order they were when the model was saved. This is because each Block is |
| uniquely identified by Block class name and a unique ID in order (since |
| its an OrderedDict) and uses the unique ID to denote that specific Block.</p> |
| <p>Assumes that the model is created in an identical order every time. |
| If the model is not able to be recreated deterministically do not |
| use this set of APIs to save/load your model.</p> |
| <p>For HybridBlocks, the cached_graph (Symbol & inputs) and settings are |
| restored if it had been hybridized before saving.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><p><strong>prefix</strong> (<em>str</em>) – The prefix to use in filenames for loading this model: |
| <prefix>-model.json and <prefix>-model.params</p> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.RNNCell.load_dict"> |
| <code class="sig-name descname">load_dict</code><span class="sig-paren">(</span><em class="sig-param">param_dict</em>, <em class="sig-param">device=None</em>, <em class="sig-param">allow_missing=False</em>, <em class="sig-param">ignore_extra=False</em>, <em class="sig-param">cast_dtype=False</em>, <em class="sig-param">dtype_source='current'</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.rnn.RNNCell.load_dict" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Load parameters from dict</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>param_dict</strong> (<em>dict</em>) – Dictionary containing model 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>, </em><em>optional</em>) – Device context on which the memory is allocated. Default is |
| <cite>mxnet.device.current_device()</cite>.</p></li> |
| <li><p><strong>allow_missing</strong> (<em>bool</em><em>, </em><em>default False</em>) – Whether to silently skip loading parameters not represented in the file.</p></li> |
| <li><p><strong>ignore_extra</strong> (<em>bool</em><em>, </em><em>default False</em>) – Whether to silently ignore parameters from the file that are not |
| present in this dict.</p></li> |
| <li><p><strong>cast_dtype</strong> (<em>bool</em><em>, </em><em>default False</em>) – Cast the data type of the NDArray loaded from the checkpoint to the dtype |
| provided by the Parameter if any</p></li> |
| <li><p><strong>dtype_source</strong> (<em>str</em><em>, </em><em>default 'current'</em>) – must be in {‘current’, ‘saved’} |
| Only valid if cast_dtype=True, specify the source of the dtype for casting |
| the parameters</p></li> |
| </ul> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.RNNCell.load_parameters"> |
| <code class="sig-name descname">load_parameters</code><span class="sig-paren">(</span><em class="sig-param">filename</em>, <em class="sig-param">device=None</em>, <em class="sig-param">allow_missing=False</em>, <em class="sig-param">ignore_extra=False</em>, <em class="sig-param">cast_dtype=False</em>, <em class="sig-param">dtype_source='current'</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.rnn.RNNCell.load_parameters" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Load parameters from file previously saved by <cite>save_parameters</cite>.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>filename</strong> (<em>str</em>) – Path to parameter file.</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><em>, </em><em>default cpu</em><em>(</em><em>)</em>) – Device(s) to initialize loaded parameters on.</p></li> |
| <li><p><strong>allow_missing</strong> (<em>bool</em><em>, </em><em>default False</em>) – Whether to silently skip loading parameters not represents in the file.</p></li> |
| <li><p><strong>ignore_extra</strong> (<em>bool</em><em>, </em><em>default False</em>) – Whether to silently ignore parameters from the file that are not |
| present in this Block.</p></li> |
| <li><p><strong>cast_dtype</strong> (<em>bool</em><em>, </em><em>default False</em>) – Cast the data type of the NDArray loaded from the checkpoint to the dtype |
| provided by the Parameter if any.</p></li> |
| <li><p><strong>dtype_source</strong> (<em>str</em><em>, </em><em>default 'current'</em>) – must be in {‘current’, ‘saved’} |
| Only valid if cast_dtype=True, specify the source of the dtype for casting |
| the parameters</p></li> |
| </ul> |
| </dd> |
| </dl> |
| <p class="rubric">References</p> |
| <p><a class="reference external" href="https://mxnet.apache.org/api/python/docs/tutorials/packages/gluon/blocks/save_load_params.html">Saving and Loading Gluon Models</a></p> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.RNNCell.optimize_for"> |
| <code class="sig-name descname">optimize_for</code><span class="sig-paren">(</span><em class="sig-param">x</em>, <em class="sig-param">*args</em>, <em class="sig-param">backend=None</em>, <em class="sig-param">clear=False</em>, <em class="sig-param">partition_if_dynamic=True</em>, <em class="sig-param">static_alloc=False</em>, <em class="sig-param">static_shape=False</em>, <em class="sig-param">inline_limit=2</em>, <em class="sig-param">forward_bulk_size=None</em>, <em class="sig-param">backward_bulk_size=None</em>, <em class="sig-param">**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.rnn.RNNCell.optimize_for" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Partitions the current HybridBlock and optimizes it for a given backend |
| without executing a forward pass. Modifies the HybridBlock in-place.</p> |
| <p>Immediately partitions a HybridBlock using the specified backend. Combines |
| the work done in the hybridize API with part of the work done in the forward |
| pass without calling the CachedOp. Can be used in place of hybridize, |
| afterwards <cite>export</cite> can be called or inference can be run. See README.md in |
| example/extensions/lib_subgraph/README.md for more details.</p> |
| <p class="rubric">Examples</p> |
| <p># partition and then export to file |
| block.optimize_for(x, backend=’myPart’) |
| block.export(‘partitioned’)</p> |
| <p># partition and then run inference |
| block.optimize_for(x, backend=’myPart’) |
| block(x)</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>x</strong> (<a class="reference internal" href="../../legacy/ndarray/ndarray.html#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – first input to model</p></li> |
| <li><p><strong>*args</strong> (<a class="reference internal" href="../../legacy/ndarray/ndarray.html#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – other inputs to model</p></li> |
| <li><p><strong>backend</strong> (<em>str</em>) – The name of backend, as registered in <cite>SubgraphBackendRegistry</cite>, default None</p></li> |
| <li><p><strong>backend_opts</strong> (<em>dict of user-specified options to pass to the backend for partitioning</em><em>, </em><em>optional</em>) – Passed on to <cite>PrePartition</cite> and <cite>PostPartition</cite> functions of <cite>SubgraphProperty</cite></p></li> |
| <li><p><strong>clear</strong> (<em>bool</em><em>, </em><em>default False</em>) – clears any previous optimizations</p></li> |
| <li><p><strong>partition_if_dynamic</strong> (<em>bool</em><em>, </em><em>default False</em>) – whether to partition the graph when dynamic shape op exists</p></li> |
| <li><p><strong>static_alloc</strong> (<em>bool</em><em>, </em><em>default False</em>) – Statically allocate memory to improve speed. Memory usage may increase.</p></li> |
| <li><p><strong>static_shape</strong> (<em>bool</em><em>, </em><em>default False</em>) – Optimize for invariant input shapes between iterations. Must also |
| set static_alloc to True. Change of input shapes is still allowed |
| but slower.</p></li> |
| <li><p><strong>inline_limit</strong> (<em>optional int</em><em>, </em><em>default 2</em>) – Maximum number of operators that can be inlined.</p></li> |
| <li><p><strong>forward_bulk_size</strong> (<em>optional int</em><em>, </em><em>default None</em>) – Segment size of bulk execution during forward pass.</p></li> |
| <li><p><strong>backward_bulk_size</strong> (<em>optional int</em><em>, </em><em>default None</em>) – Segment size of bulk execution during backward pass.</p></li> |
| <li><p><strong>**kwargs</strong> (<em>The backend options</em><em>, </em><em>optional</em>) – Passed on to <cite>PrePartition</cite> and <cite>PostPartition</cite> functions of <cite>SubgraphProperty</cite></p></li> |
| </ul> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.RNNCell.params"> |
| <em class="property">property </em><code class="sig-name descname">params</code><a class="headerlink" href="#mxnet.gluon.rnn.RNNCell.params" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Returns this <code class="xref py py-class docutils literal notranslate"><span class="pre">Block</span></code>’s parameter dictionary (does not include its |
| children’s parameters).</p> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.RNNCell.register_child"> |
| <code class="sig-name descname">register_child</code><span class="sig-paren">(</span><em class="sig-param">block</em>, <em class="sig-param">name=None</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.rnn.RNNCell.register_child" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Registers block as a child of self. <code class="xref py py-class docutils literal notranslate"><span class="pre">Block</span></code> s assigned to self as |
| attributes will be registered automatically.</p> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.RNNCell.register_forward_hook"> |
| <code class="sig-name descname">register_forward_hook</code><span class="sig-paren">(</span><em class="sig-param">hook</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.rnn.RNNCell.register_forward_hook" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Registers a forward hook on the block.</p> |
| <p>The hook function is called immediately after <a class="reference internal" href="#mxnet.gluon.rnn.RNNCell.forward" title="mxnet.gluon.rnn.RNNCell.forward"><code class="xref py py-func docutils literal notranslate"><span class="pre">forward()</span></code></a>. |
| It should not modify the input or output.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><p><strong>hook</strong> (<em>callable</em>) – The forward hook function of form <cite>hook(block, input, output) -> None</cite>.</p> |
| </dd> |
| <dt class="field-even">Returns</dt> |
| <dd class="field-even"><p></p> |
| </dd> |
| <dt class="field-odd">Return type</dt> |
| <dd class="field-odd"><p><code class="xref py py-class docutils literal notranslate"><span class="pre">mxnet.gluon.utils.HookHandle</span></code></p> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.RNNCell.register_forward_pre_hook"> |
| <code class="sig-name descname">register_forward_pre_hook</code><span class="sig-paren">(</span><em class="sig-param">hook</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.rnn.RNNCell.register_forward_pre_hook" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Registers a forward pre-hook on the block.</p> |
| <p>The hook function is called immediately before <a class="reference internal" href="#mxnet.gluon.rnn.RNNCell.forward" title="mxnet.gluon.rnn.RNNCell.forward"><code class="xref py py-func docutils literal notranslate"><span class="pre">forward()</span></code></a>. |
| It should not modify the input or output.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><p><strong>hook</strong> (<em>callable</em>) – The forward hook function of form <cite>hook(block, input) -> None</cite>.</p> |
| </dd> |
| <dt class="field-even">Returns</dt> |
| <dd class="field-even"><p></p> |
| </dd> |
| <dt class="field-odd">Return type</dt> |
| <dd class="field-odd"><p><code class="xref py py-class docutils literal notranslate"><span class="pre">mxnet.gluon.utils.HookHandle</span></code></p> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.RNNCell.register_op_hook"> |
| <code class="sig-name descname">register_op_hook</code><span class="sig-paren">(</span><em class="sig-param">callback</em>, <em class="sig-param">monitor_all=False</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.rnn.RNNCell.register_op_hook" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Install callback monitor.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>callback</strong> (<em>function</em>) – Function called to inspect the values of the intermediate outputs |
| of blocks after hybridization. It takes 3 parameters: |
| name of the tensor being inspected (str) |
| name of the operator producing or consuming that tensor (str) |
| tensor being inspected (NDArray).</p></li> |
| <li><p><strong>monitor_all</strong> (<em>bool</em><em>, </em><em>default False</em>) – If True, monitor both input and output, otherwise monitor output only.</p></li> |
| </ul> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.RNNCell.reset"> |
| <code class="sig-name descname">reset</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.rnn.RNNCell.reset" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Reset before re-using the cell for another graph.</p> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.RNNCell.reset_ctx"> |
| <code class="sig-name descname">reset_ctx</code><span class="sig-paren">(</span><em class="sig-param">ctx</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.rnn.RNNCell.reset_ctx" title="Permalink to this definition">¶</a></dt> |
| <dd><p>This function has been deprecated. Please refer to <code class="docutils literal notranslate"><span class="pre">Block.reset_device</span></code>.</p> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.RNNCell.reset_device"> |
| <code class="sig-name descname">reset_device</code><span class="sig-paren">(</span><em class="sig-param">device</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.rnn.RNNCell.reset_device" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Re-assign all Parameters to other devices.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><p><strong>device</strong> (Device or list of Device, default <code class="xref py py-meth docutils literal notranslate"><span class="pre">device.current_device()</span></code>.) – Assign Parameter to given device. If device is a list of Device, a |
| copy will be made for each device.</p> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.RNNCell.save"> |
| <code class="sig-name descname">save</code><span class="sig-paren">(</span><em class="sig-param">prefix</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.rnn.RNNCell.save" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Save the model architecture and parameters to load again later</p> |
| <p>Saves the model architecture as a nested dictionary where each Block |
| in the model is a dictionary and its children are sub-dictionaries.</p> |
| <p>Each Block is uniquely identified by Block class name and a unique ID. |
| We save each Block’s parameter UUID to restore later in order to match |
| the saved parameters.</p> |
| <p>Recursively traverses a Block’s children in order (since its an |
| OrderedDict) and uses the unique ID to denote that specific Block.</p> |
| <p>Assumes that the model is created in an identical order every time. |
| If the model is not able to be recreated deterministically do not |
| use this set of APIs to save/load your model.</p> |
| <p>For HybridBlocks, the cached_graph is saved (Symbol & inputs) if |
| it has already been hybridized.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><p><strong>prefix</strong> (<em>str</em>) – The prefix to use in filenames for saving this model: |
| <prefix>-model.json and <prefix>-model.params</p> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.RNNCell.save_parameters"> |
| <code class="sig-name descname">save_parameters</code><span class="sig-paren">(</span><em class="sig-param">filename</em>, <em class="sig-param">deduplicate=False</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.rnn.RNNCell.save_parameters" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Save parameters to file.</p> |
| <p>Saved parameters can only be loaded with <cite>load_parameters</cite>. Note that this |
| method only saves parameters, not model structure. If you want to save |
| model structures, please use <code class="xref py py-meth docutils literal notranslate"><span class="pre">HybridBlock.export()</span></code>.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>filename</strong> (<em>str</em>) – Path to file.</p></li> |
| <li><p><strong>deduplicate</strong> (<em>bool</em><em>, </em><em>default False</em>) – If True, save shared parameters only once. Otherwise, if a Block |
| contains multiple sub-blocks that share parameters, each of the |
| shared parameters will be separately saved for every sub-block.</p></li> |
| </ul> |
| </dd> |
| </dl> |
| <p class="rubric">References</p> |
| <p><a class="reference external" href="https://mxnet.apache.org/api/python/docs/tutorials/packages/gluon/blocks/save_load_params.html">Saving and Loading Gluon Models</a></p> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.RNNCell.setattr"> |
| <code class="sig-name descname">setattr</code><span class="sig-paren">(</span><em class="sig-param">name</em>, <em class="sig-param">value</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.rnn.RNNCell.setattr" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Set an attribute to a new value for all Parameters.</p> |
| <p>For example, set grad_req to null if you don’t need gradient w.r.t a |
| model’s Parameters:</p> |
| <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">model</span><span class="o">.</span><span class="n">setattr</span><span class="p">(</span><span class="s1">'grad_req'</span><span class="p">,</span> <span class="s1">'null'</span><span class="p">)</span> |
| </pre></div> |
| </div> |
| <p>or change the learning rate multiplier:</p> |
| <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">model</span><span class="o">.</span><span class="n">setattr</span><span class="p">(</span><span class="s1">'lr_mult'</span><span class="p">,</span> <span class="mf">0.5</span><span class="p">)</span> |
| </pre></div> |
| </div> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>name</strong> (<em>str</em>) – Name of the attribute.</p></li> |
| <li><p><strong>value</strong> (<em>valid type for attribute name</em>) – The new value for the attribute.</p></li> |
| </ul> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.RNNCell.share_parameters"> |
| <code class="sig-name descname">share_parameters</code><span class="sig-paren">(</span><em class="sig-param">shared</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.rnn.RNNCell.share_parameters" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Share parameters recursively inside the model.</p> |
| <p>For example, if you want <code class="docutils literal notranslate"><span class="pre">dense1</span></code> to share <code class="docutils literal notranslate"><span class="pre">dense0</span></code>’s weights, you can do:</p> |
| <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">dense0</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Dense</span><span class="p">(</span><span class="mi">20</span><span class="p">)</span> |
| <span class="n">dense1</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Dense</span><span class="p">(</span><span class="mi">20</span><span class="p">)</span> |
| <span class="n">dense1</span><span class="o">.</span><span class="n">share_parameters</span><span class="p">(</span><span class="n">dense0</span><span class="o">.</span><span class="n">collect_params</span><span class="p">())</span> |
| </pre></div> |
| </div> |
| <dl class="simple"> |
| <dt>which equals to</dt><dd><p>dense1.weight = dense0.weight |
| dense1.bias = dense0.bias</p> |
| </dd> |
| </dl> |
| <p>Note that unlike the <cite>load_parameters</cite> or <cite>load_dict</cite> functions, |
| <cite>share_parameters</cite> results in the <cite>Parameter</cite> object being shared (or |
| tied) between the models, whereas <cite>load_parameters</cite> or <cite>load_dict</cite> only |
| set the value of the data dictionary of a model. If you call |
| <cite>load_parameters</cite> or <cite>load_dict</cite> after <cite>share_parameters</cite>, the loaded |
| value will be reflected in all networks that use the shared (or tied) |
| <cite>Parameter</cite> object.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><p><strong>shared</strong> (<em>Dict</em>) – Dict of the shared parameters.</p> |
| </dd> |
| <dt class="field-even">Returns</dt> |
| <dd class="field-even"><p></p> |
| </dd> |
| <dt class="field-odd">Return type</dt> |
| <dd class="field-odd"><p>this block</p> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.RNNCell.state_info"> |
| <code class="sig-name descname">state_info</code><span class="sig-paren">(</span><em class="sig-param">batch_size=0</em><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/mxnet/gluon/rnn/rnn_cell.html#RNNCell.state_info"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.gluon.rnn.RNNCell.state_info" title="Permalink to this definition">¶</a></dt> |
| <dd><p>shape and layout information of states</p> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.RNNCell.summary"> |
| <code class="sig-name descname">summary</code><span class="sig-paren">(</span><em class="sig-param">*inputs</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.rnn.RNNCell.summary" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Print the summary of the model’s output and parameters.</p> |
| <p>The network must have been initialized, and must not have been hybridized.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><p><strong>inputs</strong> (<em>object</em>) – Any input that the model supports. For any tensor in the input, only |
| <a class="reference internal" href="../../legacy/ndarray/ndarray.html#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><code class="xref py py-class docutils literal notranslate"><span class="pre">mxnet.ndarray.NDArray</span></code></a> is supported.</p> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.RNNCell.unroll"> |
| <code class="sig-name descname">unroll</code><span class="sig-paren">(</span><em class="sig-param">length</em>, <em class="sig-param">inputs</em>, <em class="sig-param">begin_state=None</em>, <em class="sig-param">layout='NTC'</em>, <em class="sig-param">merge_outputs=None</em>, <em class="sig-param">valid_length=None</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.rnn.RNNCell.unroll" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Unrolls an RNN cell across time steps.</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>) – Number of steps to unroll.</p></li> |
| <li><p><strong>inputs</strong> (<a class="reference internal" href="../../legacy/symbol/symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a><em>, </em><em>list of Symbol</em><em>, or </em><em>None</em>) – <p>If <cite>inputs</cite> is a single Symbol (usually the output |
| of Embedding symbol), it should have shape |
| (batch_size, length, …) if <cite>layout</cite> is ‘NTC’, |
| or (length, batch_size, …) if <cite>layout</cite> is ‘TNC’.</p> |
| <p>If <cite>inputs</cite> is a list of symbols (usually output of |
| previous unroll), they should all have shape |
| (batch_size, …).</p> |
| </p></li> |
| <li><p><strong>begin_state</strong> (<em>nested list of Symbol</em><em>, </em><em>optional</em>) – Input states created by <cite>begin_state()</cite> |
| or output state of another cell. |
| Created from <cite>begin_state()</cite> if <cite>None</cite>.</p></li> |
| <li><p><strong>layout</strong> (<em>str</em><em>, </em><em>optional</em>) – <cite>layout</cite> of input symbol. Only used if inputs |
| is a single Symbol.</p></li> |
| <li><p><strong>merge_outputs</strong> (<em>bool</em><em>, </em><em>optional</em>) – If <cite>False</cite>, returns outputs as a list of Symbols. |
| If <cite>True</cite>, concatenates output across time steps |
| and returns a single symbol with shape |
| (batch_size, length, …) if layout is ‘NTC’, |
| or (length, batch_size, …) if layout is ‘TNC’. |
| If <cite>None</cite>, output whatever is faster.</p></li> |
| <li><p><strong>valid_length</strong> (<a class="reference internal" href="../../legacy/symbol/symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a><em>, </em><a class="reference internal" href="../../legacy/ndarray/ndarray.html#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a><em> or </em><em>None</em>) – <cite>valid_length</cite> specifies the length of the sequences in the batch without padding. |
| This option is especially useful for building sequence-to-sequence models where |
| the input and output sequences would potentially be padded. |
| If <cite>valid_length</cite> is None, all sequences are assumed to have the same length. |
| If <cite>valid_length</cite> is a Symbol or NDArray, it should have shape (batch_size,). |
| The ith element will be the length of the ith sequence in the batch. |
| The last valid state will be return and the padded outputs will be masked with 0. |
| Note that <cite>valid_length</cite> must be smaller or equal to <cite>length</cite>.</p></li> |
| </ul> |
| </dd> |
| <dt class="field-even">Returns</dt> |
| <dd class="field-even"><p><ul class="simple"> |
| <li><p><strong>outputs</strong> (<em>list of Symbol or Symbol</em>) – Symbol (if <cite>merge_outputs</cite> is True) or list of Symbols |
| (if <cite>merge_outputs</cite> is False) corresponding to the output from |
| the RNN from this unrolling.</p></li> |
| <li><p><strong>states</strong> (<em>list of Symbol</em>) – The new state of this RNN after this unrolling. |
| The type of this symbol is same as the output of <cite>begin_state()</cite>.</p></li> |
| </ul> |
| </p> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.RNNCell.zero_grad"> |
| <code class="sig-name descname">zero_grad</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.rnn.RNNCell.zero_grad" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Sets all Parameters’ gradient buffer to 0.</p> |
| </dd></dl> |
| |
| </dd></dl> |
| |
| <dl class="class"> |
| <dt id="mxnet.gluon.rnn.RecurrentCell"> |
| <em class="property">class </em><code class="sig-name descname">RecurrentCell</code><a class="reference internal" href="../../../_modules/mxnet/gluon/rnn/rnn_cell.html#RecurrentCell"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.gluon.rnn.RecurrentCell" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">mxnet.gluon.block.Block</span></code></p> |
| <p>Abstract base class for RNN cells</p> |
| <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.rnn.RecurrentCell.apply" title="mxnet.gluon.rnn.RecurrentCell.apply"><code class="xref py py-obj docutils literal notranslate"><span class="pre">apply</span></code></a>(fn)</p></td> |
| <td><p>Applies <code class="docutils literal notranslate"><span class="pre">fn</span></code> recursively to every child block as well as self.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.RecurrentCell.begin_state" title="mxnet.gluon.rnn.RecurrentCell.begin_state"><code class="xref py py-obj docutils literal notranslate"><span class="pre">begin_state</span></code></a>([batch_size, func])</p></td> |
| <td><p>Initial state for this cell.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.RecurrentCell.cast" title="mxnet.gluon.rnn.RecurrentCell.cast"><code class="xref py py-obj docutils literal notranslate"><span class="pre">cast</span></code></a>(dtype)</p></td> |
| <td><p>Cast this Block to use another data type.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.RecurrentCell.collect_params" title="mxnet.gluon.rnn.RecurrentCell.collect_params"><code class="xref py py-obj docutils literal notranslate"><span class="pre">collect_params</span></code></a>([select])</p></td> |
| <td><p>Returns a <code class="xref py py-class docutils literal notranslate"><span class="pre">Dict</span></code> containing this <code class="xref py py-class docutils literal notranslate"><span class="pre">Block</span></code> and all of its children’s Parameters(default), also can returns the select <code class="xref py py-class docutils literal notranslate"><span class="pre">Dict</span></code> which match some given regular expressions.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.RecurrentCell.forward" title="mxnet.gluon.rnn.RecurrentCell.forward"><code class="xref py py-obj docutils literal notranslate"><span class="pre">forward</span></code></a>(inputs, states)</p></td> |
| <td><p>Unrolls the recurrent cell for one time step.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.RecurrentCell.hybridize" title="mxnet.gluon.rnn.RecurrentCell.hybridize"><code class="xref py py-obj docutils literal notranslate"><span class="pre">hybridize</span></code></a>([active])</p></td> |
| <td><p>Please refer description of HybridBlock hybridize().</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.RecurrentCell.initialize" title="mxnet.gluon.rnn.RecurrentCell.initialize"><code class="xref py py-obj docutils literal notranslate"><span class="pre">initialize</span></code></a>([init, device, verbose, force_reinit])</p></td> |
| <td><p>Initializes <code class="xref py py-class docutils literal notranslate"><span class="pre">Parameter</span></code> s of this <code class="xref py py-class docutils literal notranslate"><span class="pre">Block</span></code> and its children.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.RecurrentCell.load" title="mxnet.gluon.rnn.RecurrentCell.load"><code class="xref py py-obj docutils literal notranslate"><span class="pre">load</span></code></a>(prefix)</p></td> |
| <td><p>Load a model saved using the <cite>save</cite> API</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.RecurrentCell.load_dict" title="mxnet.gluon.rnn.RecurrentCell.load_dict"><code class="xref py py-obj docutils literal notranslate"><span class="pre">load_dict</span></code></a>(param_dict[, device, …])</p></td> |
| <td><p>Load parameters from dict</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.RecurrentCell.load_parameters" title="mxnet.gluon.rnn.RecurrentCell.load_parameters"><code class="xref py py-obj docutils literal notranslate"><span class="pre">load_parameters</span></code></a>(filename[, device, …])</p></td> |
| <td><p>Load parameters from file previously saved by <cite>save_parameters</cite>.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.RecurrentCell.register_child" title="mxnet.gluon.rnn.RecurrentCell.register_child"><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_child</span></code></a>(block[, name])</p></td> |
| <td><p>Registers block as a child of self.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.RecurrentCell.register_forward_hook" title="mxnet.gluon.rnn.RecurrentCell.register_forward_hook"><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_forward_hook</span></code></a>(hook)</p></td> |
| <td><p>Registers a forward hook on the block.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.RecurrentCell.register_forward_pre_hook" title="mxnet.gluon.rnn.RecurrentCell.register_forward_pre_hook"><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_forward_pre_hook</span></code></a>(hook)</p></td> |
| <td><p>Registers a forward pre-hook on the block.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.RecurrentCell.register_op_hook" title="mxnet.gluon.rnn.RecurrentCell.register_op_hook"><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_op_hook</span></code></a>(callback[, monitor_all])</p></td> |
| <td><p>Install callback monitor.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.RecurrentCell.reset" title="mxnet.gluon.rnn.RecurrentCell.reset"><code class="xref py py-obj docutils literal notranslate"><span class="pre">reset</span></code></a>()</p></td> |
| <td><p>Reset before re-using the cell for another graph.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.RecurrentCell.reset_ctx" title="mxnet.gluon.rnn.RecurrentCell.reset_ctx"><code class="xref py py-obj docutils literal notranslate"><span class="pre">reset_ctx</span></code></a>(ctx)</p></td> |
| <td><p>This function has been deprecated.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.RecurrentCell.reset_device" title="mxnet.gluon.rnn.RecurrentCell.reset_device"><code class="xref py py-obj docutils literal notranslate"><span class="pre">reset_device</span></code></a>(device)</p></td> |
| <td><p>Re-assign all Parameters to other devices.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.RecurrentCell.save" title="mxnet.gluon.rnn.RecurrentCell.save"><code class="xref py py-obj docutils literal notranslate"><span class="pre">save</span></code></a>(prefix)</p></td> |
| <td><p>Save the model architecture and parameters to load again later</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.RecurrentCell.save_parameters" title="mxnet.gluon.rnn.RecurrentCell.save_parameters"><code class="xref py py-obj docutils literal notranslate"><span class="pre">save_parameters</span></code></a>(filename[, deduplicate])</p></td> |
| <td><p>Save parameters to file.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.RecurrentCell.setattr" title="mxnet.gluon.rnn.RecurrentCell.setattr"><code class="xref py py-obj docutils literal notranslate"><span class="pre">setattr</span></code></a>(name, value)</p></td> |
| <td><p>Set an attribute to a new value for all Parameters.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.RecurrentCell.share_parameters" title="mxnet.gluon.rnn.RecurrentCell.share_parameters"><code class="xref py py-obj docutils literal notranslate"><span class="pre">share_parameters</span></code></a>(shared)</p></td> |
| <td><p>Share parameters recursively inside the model.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.RecurrentCell.state_info" title="mxnet.gluon.rnn.RecurrentCell.state_info"><code class="xref py py-obj docutils literal notranslate"><span class="pre">state_info</span></code></a>([batch_size])</p></td> |
| <td><p>shape and layout information of states</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.RecurrentCell.summary" title="mxnet.gluon.rnn.RecurrentCell.summary"><code class="xref py py-obj docutils literal notranslate"><span class="pre">summary</span></code></a>(*inputs)</p></td> |
| <td><p>Print the summary of the model’s output and parameters.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.RecurrentCell.unroll" title="mxnet.gluon.rnn.RecurrentCell.unroll"><code class="xref py py-obj docutils literal notranslate"><span class="pre">unroll</span></code></a>(length, inputs[, begin_state, …])</p></td> |
| <td><p>Unrolls an RNN cell across time steps.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.RecurrentCell.zero_grad" title="mxnet.gluon.rnn.RecurrentCell.zero_grad"><code class="xref py py-obj docutils literal notranslate"><span class="pre">zero_grad</span></code></a>()</p></td> |
| <td><p>Sets all Parameters’ gradient buffer to 0.</p></td> |
| </tr> |
| </tbody> |
| </table> |
| <p><strong>Attributes</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.rnn.RecurrentCell.params" title="mxnet.gluon.rnn.RecurrentCell.params"><code class="xref py py-obj docutils literal notranslate"><span class="pre">params</span></code></a></p></td> |
| <td><p>Returns this <code class="xref py py-class docutils literal notranslate"><span class="pre">Block</span></code>’s parameter dictionary (does not include its children’s parameters).</p></td> |
| </tr> |
| </tbody> |
| </table> |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.RecurrentCell.apply"> |
| <code class="sig-name descname">apply</code><span class="sig-paren">(</span><em class="sig-param">fn</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.rnn.RecurrentCell.apply" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Applies <code class="docutils literal notranslate"><span class="pre">fn</span></code> recursively to every child block as well as self.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><p><strong>fn</strong> (<em>callable</em>) – Function to be applied to each submodule, of form <cite>fn(block)</cite>.</p> |
| </dd> |
| <dt class="field-even">Returns</dt> |
| <dd class="field-even"><p></p> |
| </dd> |
| <dt class="field-odd">Return type</dt> |
| <dd class="field-odd"><p>this block</p> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.RecurrentCell.begin_state"> |
| <code class="sig-name descname">begin_state</code><span class="sig-paren">(</span><em class="sig-param">batch_size=0</em>, <em class="sig-param">func=<function zeros></em>, <em class="sig-param">**kwargs</em><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/mxnet/gluon/rnn/rnn_cell.html#RecurrentCell.begin_state"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.gluon.rnn.RecurrentCell.begin_state" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Initial state for this cell.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>func</strong> (<em>callable</em><em>, </em><em>default symbol.zeros</em>) – <p>Function for creating initial state.</p> |
| <p>For Symbol API, func can be <cite>symbol.zeros</cite>, <cite>symbol.uniform</cite>, |
| <cite>symbol.var etc</cite>. Use <cite>symbol.var</cite> if you want to directly |
| feed input as states.</p> |
| <p>For NDArray API, func can be <cite>ndarray.zeros</cite>, <cite>ndarray.ones</cite>, etc.</p> |
| </p></li> |
| <li><p><strong>batch_size</strong> (<em>int</em><em>, </em><em>default 0</em>) – Only required for NDArray API. Size of the batch (‘N’ in layout) |
| dimension of input.</p></li> |
| <li><p><strong>**kwargs</strong> – Additional keyword arguments passed to func. For example |
| <cite>mean</cite>, <cite>std</cite>, <cite>dtype</cite>, etc.</p></li> |
| </ul> |
| </dd> |
| <dt class="field-even">Returns</dt> |
| <dd class="field-even"><p><strong>states</strong> – Starting states for the first RNN step.</p> |
| </dd> |
| <dt class="field-odd">Return type</dt> |
| <dd class="field-odd"><p>nested list of Symbol</p> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.RecurrentCell.cast"> |
| <code class="sig-name descname">cast</code><span class="sig-paren">(</span><em class="sig-param">dtype</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.rnn.RecurrentCell.cast" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Cast this Block to use another data type.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><p><strong>dtype</strong> (<em>str</em><em> or </em><em>numpy.dtype</em>) – The new data type.</p> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.RecurrentCell.collect_params"> |
| <code class="sig-name descname">collect_params</code><span class="sig-paren">(</span><em class="sig-param">select=None</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.rnn.RecurrentCell.collect_params" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Returns a <code class="xref py py-class docutils literal notranslate"><span class="pre">Dict</span></code> containing this <code class="xref py py-class docutils literal notranslate"><span class="pre">Block</span></code> and all of its |
| children’s Parameters(default), also can returns the select <code class="xref py py-class docutils literal notranslate"><span class="pre">Dict</span></code> |
| which match some given regular expressions.</p> |
| <p>For example, collect the specified parameters in [‘conv1.weight’, ‘conv1.bias’, ‘fc.weight’, |
| ‘fc.bias’]:</p> |
| <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">model</span><span class="o">.</span><span class="n">collect_params</span><span class="p">(</span><span class="s1">'conv1.weight|conv1.bias|fc.weight|fc.bias'</span><span class="p">)</span> |
| </pre></div> |
| </div> |
| <p>or collect all parameters whose names end with ‘weight’ or ‘bias’, this can be done |
| using regular expressions:</p> |
| <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">model</span><span class="o">.</span><span class="n">collect_params</span><span class="p">(</span><span class="s1">'.*weight|.*bias'</span><span class="p">)</span> |
| </pre></div> |
| </div> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><p><strong>select</strong> (<em>str</em>) – regular expressions</p> |
| </dd> |
| <dt class="field-even">Returns</dt> |
| <dd class="field-even"><p></p> |
| </dd> |
| <dt class="field-odd">Return type</dt> |
| <dd class="field-odd"><p>The selected <code class="xref py py-class docutils literal notranslate"><span class="pre">Dict</span></code></p> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.RecurrentCell.forward"> |
| <code class="sig-name descname">forward</code><span class="sig-paren">(</span><em class="sig-param">inputs</em>, <em class="sig-param">states</em><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/mxnet/gluon/rnn/rnn_cell.html#RecurrentCell.forward"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.gluon.rnn.RecurrentCell.forward" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Unrolls the recurrent cell for one time step.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>inputs</strong> (<em>sym.Variable</em>) – Input symbol, 2D, of shape (batch_size * num_units).</p></li> |
| <li><p><strong>states</strong> (<em>list of sym.Variable</em>) – RNN state from previous step or the output of begin_state().</p></li> |
| </ul> |
| </dd> |
| <dt class="field-even">Returns</dt> |
| <dd class="field-even"><p><ul class="simple"> |
| <li><p><strong>output</strong> (<em>Symbol</em>) – Symbol corresponding to the output from the RNN when unrolling |
| for a single time step.</p></li> |
| <li><p><strong>states</strong> (<em>list of Symbol</em>) – The new state of this RNN after this unrolling. |
| The type of this symbol is same as the output of <cite>begin_state()</cite>. |
| This can be used as an input state to the next time step |
| of this RNN.</p></li> |
| </ul> |
| </p> |
| </dd> |
| </dl> |
| <div class="admonition seealso"> |
| <p class="admonition-title">See also</p> |
| <dl class="simple"> |
| <dt><a class="reference internal" href="#mxnet.gluon.rnn.RecurrentCell.begin_state" title="mxnet.gluon.rnn.RecurrentCell.begin_state"><code class="xref py py-meth docutils literal notranslate"><span class="pre">begin_state()</span></code></a></dt><dd><p>This function can provide the states for the first time step.</p> |
| </dd> |
| <dt><a class="reference internal" href="#mxnet.gluon.rnn.RecurrentCell.unroll" title="mxnet.gluon.rnn.RecurrentCell.unroll"><code class="xref py py-meth docutils literal notranslate"><span class="pre">unroll()</span></code></a></dt><dd><p>This function unrolls an RNN for a given number of (>=1) time steps.</p> |
| </dd> |
| </dl> |
| </div> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.RecurrentCell.hybridize"> |
| <code class="sig-name descname">hybridize</code><span class="sig-paren">(</span><em class="sig-param">active=True</em>, <em class="sig-param">**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.rnn.RecurrentCell.hybridize" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Please refer description of HybridBlock hybridize().</p> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.RecurrentCell.initialize"> |
| <code class="sig-name descname">initialize</code><span class="sig-paren">(</span><em class="sig-param">init=<mxnet.initializer.Uniform object></em>, <em class="sig-param">device=None</em>, <em class="sig-param">verbose=False</em>, <em class="sig-param">force_reinit=False</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.rnn.RecurrentCell.initialize" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Initializes <code class="xref py py-class docutils literal notranslate"><span class="pre">Parameter</span></code> s of this <code class="xref py py-class docutils literal notranslate"><span class="pre">Block</span></code> and its children.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>init</strong> (<a class="reference internal" href="../../initializer/index.html#mxnet.initializer.Initializer" title="mxnet.initializer.Initializer"><em>Initializer</em></a>) – Global default Initializer to be used when <code class="xref py py-meth docutils literal notranslate"><span class="pre">Parameter.init()</span></code> is <code class="docutils literal notranslate"><span class="pre">None</span></code>. |
| Otherwise, <code class="xref py py-meth docutils literal notranslate"><span class="pre">Parameter.init()</span></code> takes precedence.</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>) – Keeps a copy of Parameters on one or many device(s).</p></li> |
| <li><p><strong>verbose</strong> (<em>bool</em><em>, </em><em>default False</em>) – Whether to verbosely print out details on initialization.</p></li> |
| <li><p><strong>force_reinit</strong> (<em>bool</em><em>, </em><em>default False</em>) – Whether to force re-initialization if parameter is already initialized.</p></li> |
| </ul> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.RecurrentCell.load"> |
| <code class="sig-name descname">load</code><span class="sig-paren">(</span><em class="sig-param">prefix</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.rnn.RecurrentCell.load" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Load a model saved using the <cite>save</cite> API</p> |
| <p>Reconfigures a model using the saved configuration. This function |
| does not regenerate the model architecture. It resets each Block’s |
| parameter UUIDs as they were when saved in order to match the names of the |
| saved parameters.</p> |
| <p>This function assumes the Blocks in the model were created in the same |
| order they were when the model was saved. This is because each Block is |
| uniquely identified by Block class name and a unique ID in order (since |
| its an OrderedDict) and uses the unique ID to denote that specific Block.</p> |
| <p>Assumes that the model is created in an identical order every time. |
| If the model is not able to be recreated deterministically do not |
| use this set of APIs to save/load your model.</p> |
| <p>For HybridBlocks, the cached_graph (Symbol & inputs) and settings are |
| restored if it had been hybridized before saving.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><p><strong>prefix</strong> (<em>str</em>) – The prefix to use in filenames for loading this model: |
| <prefix>-model.json and <prefix>-model.params</p> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.RecurrentCell.load_dict"> |
| <code class="sig-name descname">load_dict</code><span class="sig-paren">(</span><em class="sig-param">param_dict</em>, <em class="sig-param">device=None</em>, <em class="sig-param">allow_missing=False</em>, <em class="sig-param">ignore_extra=False</em>, <em class="sig-param">cast_dtype=False</em>, <em class="sig-param">dtype_source='current'</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.rnn.RecurrentCell.load_dict" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Load parameters from dict</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>param_dict</strong> (<em>dict</em>) – Dictionary containing model 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>, </em><em>optional</em>) – Device context on which the memory is allocated. Default is |
| <cite>mxnet.device.current_device()</cite>.</p></li> |
| <li><p><strong>allow_missing</strong> (<em>bool</em><em>, </em><em>default False</em>) – Whether to silently skip loading parameters not represented in the file.</p></li> |
| <li><p><strong>ignore_extra</strong> (<em>bool</em><em>, </em><em>default False</em>) – Whether to silently ignore parameters from the file that are not |
| present in this dict.</p></li> |
| <li><p><strong>cast_dtype</strong> (<em>bool</em><em>, </em><em>default False</em>) – Cast the data type of the NDArray loaded from the checkpoint to the dtype |
| provided by the Parameter if any</p></li> |
| <li><p><strong>dtype_source</strong> (<em>str</em><em>, </em><em>default 'current'</em>) – must be in {‘current’, ‘saved’} |
| Only valid if cast_dtype=True, specify the source of the dtype for casting |
| the parameters</p></li> |
| </ul> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.RecurrentCell.load_parameters"> |
| <code class="sig-name descname">load_parameters</code><span class="sig-paren">(</span><em class="sig-param">filename</em>, <em class="sig-param">device=None</em>, <em class="sig-param">allow_missing=False</em>, <em class="sig-param">ignore_extra=False</em>, <em class="sig-param">cast_dtype=False</em>, <em class="sig-param">dtype_source='current'</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.rnn.RecurrentCell.load_parameters" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Load parameters from file previously saved by <cite>save_parameters</cite>.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>filename</strong> (<em>str</em>) – Path to parameter file.</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><em>, </em><em>default cpu</em><em>(</em><em>)</em>) – Device(s) to initialize loaded parameters on.</p></li> |
| <li><p><strong>allow_missing</strong> (<em>bool</em><em>, </em><em>default False</em>) – Whether to silently skip loading parameters not represents in the file.</p></li> |
| <li><p><strong>ignore_extra</strong> (<em>bool</em><em>, </em><em>default False</em>) – Whether to silently ignore parameters from the file that are not |
| present in this Block.</p></li> |
| <li><p><strong>cast_dtype</strong> (<em>bool</em><em>, </em><em>default False</em>) – Cast the data type of the NDArray loaded from the checkpoint to the dtype |
| provided by the Parameter if any.</p></li> |
| <li><p><strong>dtype_source</strong> (<em>str</em><em>, </em><em>default 'current'</em>) – must be in {‘current’, ‘saved’} |
| Only valid if cast_dtype=True, specify the source of the dtype for casting |
| the parameters</p></li> |
| </ul> |
| </dd> |
| </dl> |
| <p class="rubric">References</p> |
| <p><a class="reference external" href="https://mxnet.apache.org/api/python/docs/tutorials/packages/gluon/blocks/save_load_params.html">Saving and Loading Gluon Models</a></p> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.RecurrentCell.params"> |
| <em class="property">property </em><code class="sig-name descname">params</code><a class="headerlink" href="#mxnet.gluon.rnn.RecurrentCell.params" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Returns this <code class="xref py py-class docutils literal notranslate"><span class="pre">Block</span></code>’s parameter dictionary (does not include its |
| children’s parameters).</p> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.RecurrentCell.register_child"> |
| <code class="sig-name descname">register_child</code><span class="sig-paren">(</span><em class="sig-param">block</em>, <em class="sig-param">name=None</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.rnn.RecurrentCell.register_child" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Registers block as a child of self. <code class="xref py py-class docutils literal notranslate"><span class="pre">Block</span></code> s assigned to self as |
| attributes will be registered automatically.</p> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.RecurrentCell.register_forward_hook"> |
| <code class="sig-name descname">register_forward_hook</code><span class="sig-paren">(</span><em class="sig-param">hook</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.rnn.RecurrentCell.register_forward_hook" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Registers a forward hook on the block.</p> |
| <p>The hook function is called immediately after <a class="reference internal" href="#mxnet.gluon.rnn.RecurrentCell.forward" title="mxnet.gluon.rnn.RecurrentCell.forward"><code class="xref py py-func docutils literal notranslate"><span class="pre">forward()</span></code></a>. |
| It should not modify the input or output.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><p><strong>hook</strong> (<em>callable</em>) – The forward hook function of form <cite>hook(block, input, output) -> None</cite>.</p> |
| </dd> |
| <dt class="field-even">Returns</dt> |
| <dd class="field-even"><p></p> |
| </dd> |
| <dt class="field-odd">Return type</dt> |
| <dd class="field-odd"><p><code class="xref py py-class docutils literal notranslate"><span class="pre">mxnet.gluon.utils.HookHandle</span></code></p> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.RecurrentCell.register_forward_pre_hook"> |
| <code class="sig-name descname">register_forward_pre_hook</code><span class="sig-paren">(</span><em class="sig-param">hook</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.rnn.RecurrentCell.register_forward_pre_hook" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Registers a forward pre-hook on the block.</p> |
| <p>The hook function is called immediately before <a class="reference internal" href="#mxnet.gluon.rnn.RecurrentCell.forward" title="mxnet.gluon.rnn.RecurrentCell.forward"><code class="xref py py-func docutils literal notranslate"><span class="pre">forward()</span></code></a>. |
| It should not modify the input or output.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><p><strong>hook</strong> (<em>callable</em>) – The forward hook function of form <cite>hook(block, input) -> None</cite>.</p> |
| </dd> |
| <dt class="field-even">Returns</dt> |
| <dd class="field-even"><p></p> |
| </dd> |
| <dt class="field-odd">Return type</dt> |
| <dd class="field-odd"><p><code class="xref py py-class docutils literal notranslate"><span class="pre">mxnet.gluon.utils.HookHandle</span></code></p> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.RecurrentCell.register_op_hook"> |
| <code class="sig-name descname">register_op_hook</code><span class="sig-paren">(</span><em class="sig-param">callback</em>, <em class="sig-param">monitor_all=False</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.rnn.RecurrentCell.register_op_hook" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Install callback monitor.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>callback</strong> (<em>function</em>) – Function called to inspect the values of the intermediate outputs |
| of blocks after hybridization. It takes 3 parameters: |
| name of the tensor being inspected (str) |
| name of the operator producing or consuming that tensor (str) |
| tensor being inspected (NDArray).</p></li> |
| <li><p><strong>monitor_all</strong> (<em>bool</em><em>, </em><em>default False</em>) – If True, monitor both input and output, otherwise monitor output only.</p></li> |
| </ul> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.RecurrentCell.reset"> |
| <code class="sig-name descname">reset</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/mxnet/gluon/rnn/rnn_cell.html#RecurrentCell.reset"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.gluon.rnn.RecurrentCell.reset" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Reset before re-using the cell for another graph.</p> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.RecurrentCell.reset_ctx"> |
| <code class="sig-name descname">reset_ctx</code><span class="sig-paren">(</span><em class="sig-param">ctx</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.rnn.RecurrentCell.reset_ctx" title="Permalink to this definition">¶</a></dt> |
| <dd><p>This function has been deprecated. Please refer to <code class="docutils literal notranslate"><span class="pre">Block.reset_device</span></code>.</p> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.RecurrentCell.reset_device"> |
| <code class="sig-name descname">reset_device</code><span class="sig-paren">(</span><em class="sig-param">device</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.rnn.RecurrentCell.reset_device" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Re-assign all Parameters to other devices.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><p><strong>device</strong> (Device or list of Device, default <code class="xref py py-meth docutils literal notranslate"><span class="pre">device.current_device()</span></code>.) – Assign Parameter to given device. If device is a list of Device, a |
| copy will be made for each device.</p> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.RecurrentCell.save"> |
| <code class="sig-name descname">save</code><span class="sig-paren">(</span><em class="sig-param">prefix</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.rnn.RecurrentCell.save" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Save the model architecture and parameters to load again later</p> |
| <p>Saves the model architecture as a nested dictionary where each Block |
| in the model is a dictionary and its children are sub-dictionaries.</p> |
| <p>Each Block is uniquely identified by Block class name and a unique ID. |
| We save each Block’s parameter UUID to restore later in order to match |
| the saved parameters.</p> |
| <p>Recursively traverses a Block’s children in order (since its an |
| OrderedDict) and uses the unique ID to denote that specific Block.</p> |
| <p>Assumes that the model is created in an identical order every time. |
| If the model is not able to be recreated deterministically do not |
| use this set of APIs to save/load your model.</p> |
| <p>For HybridBlocks, the cached_graph is saved (Symbol & inputs) if |
| it has already been hybridized.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><p><strong>prefix</strong> (<em>str</em>) – The prefix to use in filenames for saving this model: |
| <prefix>-model.json and <prefix>-model.params</p> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.RecurrentCell.save_parameters"> |
| <code class="sig-name descname">save_parameters</code><span class="sig-paren">(</span><em class="sig-param">filename</em>, <em class="sig-param">deduplicate=False</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.rnn.RecurrentCell.save_parameters" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Save parameters to file.</p> |
| <p>Saved parameters can only be loaded with <cite>load_parameters</cite>. Note that this |
| method only saves parameters, not model structure. If you want to save |
| model structures, please use <code class="xref py py-meth docutils literal notranslate"><span class="pre">HybridBlock.export()</span></code>.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>filename</strong> (<em>str</em>) – Path to file.</p></li> |
| <li><p><strong>deduplicate</strong> (<em>bool</em><em>, </em><em>default False</em>) – If True, save shared parameters only once. Otherwise, if a Block |
| contains multiple sub-blocks that share parameters, each of the |
| shared parameters will be separately saved for every sub-block.</p></li> |
| </ul> |
| </dd> |
| </dl> |
| <p class="rubric">References</p> |
| <p><a class="reference external" href="https://mxnet.apache.org/api/python/docs/tutorials/packages/gluon/blocks/save_load_params.html">Saving and Loading Gluon Models</a></p> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.RecurrentCell.setattr"> |
| <code class="sig-name descname">setattr</code><span class="sig-paren">(</span><em class="sig-param">name</em>, <em class="sig-param">value</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.rnn.RecurrentCell.setattr" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Set an attribute to a new value for all Parameters.</p> |
| <p>For example, set grad_req to null if you don’t need gradient w.r.t a |
| model’s Parameters:</p> |
| <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">model</span><span class="o">.</span><span class="n">setattr</span><span class="p">(</span><span class="s1">'grad_req'</span><span class="p">,</span> <span class="s1">'null'</span><span class="p">)</span> |
| </pre></div> |
| </div> |
| <p>or change the learning rate multiplier:</p> |
| <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">model</span><span class="o">.</span><span class="n">setattr</span><span class="p">(</span><span class="s1">'lr_mult'</span><span class="p">,</span> <span class="mf">0.5</span><span class="p">)</span> |
| </pre></div> |
| </div> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>name</strong> (<em>str</em>) – Name of the attribute.</p></li> |
| <li><p><strong>value</strong> (<em>valid type for attribute name</em>) – The new value for the attribute.</p></li> |
| </ul> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.RecurrentCell.share_parameters"> |
| <code class="sig-name descname">share_parameters</code><span class="sig-paren">(</span><em class="sig-param">shared</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.rnn.RecurrentCell.share_parameters" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Share parameters recursively inside the model.</p> |
| <p>For example, if you want <code class="docutils literal notranslate"><span class="pre">dense1</span></code> to share <code class="docutils literal notranslate"><span class="pre">dense0</span></code>’s weights, you can do:</p> |
| <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">dense0</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Dense</span><span class="p">(</span><span class="mi">20</span><span class="p">)</span> |
| <span class="n">dense1</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Dense</span><span class="p">(</span><span class="mi">20</span><span class="p">)</span> |
| <span class="n">dense1</span><span class="o">.</span><span class="n">share_parameters</span><span class="p">(</span><span class="n">dense0</span><span class="o">.</span><span class="n">collect_params</span><span class="p">())</span> |
| </pre></div> |
| </div> |
| <dl class="simple"> |
| <dt>which equals to</dt><dd><p>dense1.weight = dense0.weight |
| dense1.bias = dense0.bias</p> |
| </dd> |
| </dl> |
| <p>Note that unlike the <cite>load_parameters</cite> or <cite>load_dict</cite> functions, |
| <cite>share_parameters</cite> results in the <cite>Parameter</cite> object being shared (or |
| tied) between the models, whereas <cite>load_parameters</cite> or <cite>load_dict</cite> only |
| set the value of the data dictionary of a model. If you call |
| <cite>load_parameters</cite> or <cite>load_dict</cite> after <cite>share_parameters</cite>, the loaded |
| value will be reflected in all networks that use the shared (or tied) |
| <cite>Parameter</cite> object.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><p><strong>shared</strong> (<em>Dict</em>) – Dict of the shared parameters.</p> |
| </dd> |
| <dt class="field-even">Returns</dt> |
| <dd class="field-even"><p></p> |
| </dd> |
| <dt class="field-odd">Return type</dt> |
| <dd class="field-odd"><p>this block</p> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.RecurrentCell.state_info"> |
| <code class="sig-name descname">state_info</code><span class="sig-paren">(</span><em class="sig-param">batch_size=0</em><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/mxnet/gluon/rnn/rnn_cell.html#RecurrentCell.state_info"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.gluon.rnn.RecurrentCell.state_info" title="Permalink to this definition">¶</a></dt> |
| <dd><p>shape and layout information of states</p> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.RecurrentCell.summary"> |
| <code class="sig-name descname">summary</code><span class="sig-paren">(</span><em class="sig-param">*inputs</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.rnn.RecurrentCell.summary" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Print the summary of the model’s output and parameters.</p> |
| <p>The network must have been initialized, and must not have been hybridized.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><p><strong>inputs</strong> (<em>object</em>) – Any input that the model supports. For any tensor in the input, only |
| <a class="reference internal" href="../../legacy/ndarray/ndarray.html#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><code class="xref py py-class docutils literal notranslate"><span class="pre">mxnet.ndarray.NDArray</span></code></a> is supported.</p> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.RecurrentCell.unroll"> |
| <code class="sig-name descname">unroll</code><span class="sig-paren">(</span><em class="sig-param">length</em>, <em class="sig-param">inputs</em>, <em class="sig-param">begin_state=None</em>, <em class="sig-param">layout='NTC'</em>, <em class="sig-param">merge_outputs=None</em>, <em class="sig-param">valid_length=None</em><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/mxnet/gluon/rnn/rnn_cell.html#RecurrentCell.unroll"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.gluon.rnn.RecurrentCell.unroll" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Unrolls an RNN cell across time steps.</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>) – Number of steps to unroll.</p></li> |
| <li><p><strong>inputs</strong> (<a class="reference internal" href="../../legacy/symbol/symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a><em>, </em><em>list of Symbol</em><em>, or </em><em>None</em>) – <p>If <cite>inputs</cite> is a single Symbol (usually the output |
| of Embedding symbol), it should have shape |
| (batch_size, length, …) if <cite>layout</cite> is ‘NTC’, |
| or (length, batch_size, …) if <cite>layout</cite> is ‘TNC’.</p> |
| <p>If <cite>inputs</cite> is a list of symbols (usually output of |
| previous unroll), they should all have shape |
| (batch_size, …).</p> |
| </p></li> |
| <li><p><strong>begin_state</strong> (<em>nested list of Symbol</em><em>, </em><em>optional</em>) – Input states created by <cite>begin_state()</cite> |
| or output state of another cell. |
| Created from <cite>begin_state()</cite> if <cite>None</cite>.</p></li> |
| <li><p><strong>layout</strong> (<em>str</em><em>, </em><em>optional</em>) – <cite>layout</cite> of input symbol. Only used if inputs |
| is a single Symbol.</p></li> |
| <li><p><strong>merge_outputs</strong> (<em>bool</em><em>, </em><em>optional</em>) – If <cite>False</cite>, returns outputs as a list of Symbols. |
| If <cite>True</cite>, concatenates output across time steps |
| and returns a single symbol with shape |
| (batch_size, length, …) if layout is ‘NTC’, |
| or (length, batch_size, …) if layout is ‘TNC’. |
| If <cite>None</cite>, output whatever is faster.</p></li> |
| <li><p><strong>valid_length</strong> (<a class="reference internal" href="../../legacy/symbol/symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a><em>, </em><a class="reference internal" href="../../legacy/ndarray/ndarray.html#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a><em> or </em><em>None</em>) – <cite>valid_length</cite> specifies the length of the sequences in the batch without padding. |
| This option is especially useful for building sequence-to-sequence models where |
| the input and output sequences would potentially be padded. |
| If <cite>valid_length</cite> is None, all sequences are assumed to have the same length. |
| If <cite>valid_length</cite> is a Symbol or NDArray, it should have shape (batch_size,). |
| The ith element will be the length of the ith sequence in the batch. |
| The last valid state will be return and the padded outputs will be masked with 0. |
| Note that <cite>valid_length</cite> must be smaller or equal to <cite>length</cite>.</p></li> |
| </ul> |
| </dd> |
| <dt class="field-even">Returns</dt> |
| <dd class="field-even"><p><ul class="simple"> |
| <li><p><strong>outputs</strong> (<em>list of Symbol or Symbol</em>) – Symbol (if <cite>merge_outputs</cite> is True) or list of Symbols |
| (if <cite>merge_outputs</cite> is False) corresponding to the output from |
| the RNN from this unrolling.</p></li> |
| <li><p><strong>states</strong> (<em>list of Symbol</em>) – The new state of this RNN after this unrolling. |
| The type of this symbol is same as the output of <cite>begin_state()</cite>.</p></li> |
| </ul> |
| </p> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.RecurrentCell.zero_grad"> |
| <code class="sig-name descname">zero_grad</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.rnn.RecurrentCell.zero_grad" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Sets all Parameters’ gradient buffer to 0.</p> |
| </dd></dl> |
| |
| </dd></dl> |
| |
| <dl class="class"> |
| <dt id="mxnet.gluon.rnn.ResidualCell"> |
| <em class="property">class </em><code class="sig-name descname">ResidualCell</code><span class="sig-paren">(</span><em class="sig-param">base_cell</em><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/mxnet/gluon/rnn/rnn_cell.html#ResidualCell"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.gluon.rnn.ResidualCell" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">mxnet.gluon.rnn.rnn_cell.ModifierCell</span></code></p> |
| <p>Adds residual connection as described in Wu et al, 2016 |
| (<a class="reference external" href="https://arxiv.org/abs/1609.08144">https://arxiv.org/abs/1609.08144</a>). |
| Output of the cell is output of the base cell plus input.</p> |
| <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.rnn.ResidualCell.apply" title="mxnet.gluon.rnn.ResidualCell.apply"><code class="xref py py-obj docutils literal notranslate"><span class="pre">apply</span></code></a>(fn)</p></td> |
| <td><p>Applies <code class="docutils literal notranslate"><span class="pre">fn</span></code> recursively to every child block as well as self.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.ResidualCell.cast" title="mxnet.gluon.rnn.ResidualCell.cast"><code class="xref py py-obj docutils literal notranslate"><span class="pre">cast</span></code></a>(dtype)</p></td> |
| <td><p>Cast this Block to use another data type.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.ResidualCell.collect_params" title="mxnet.gluon.rnn.ResidualCell.collect_params"><code class="xref py py-obj docutils literal notranslate"><span class="pre">collect_params</span></code></a>([select])</p></td> |
| <td><p>Returns a <code class="xref py py-class docutils literal notranslate"><span class="pre">Dict</span></code> containing this <code class="xref py py-class docutils literal notranslate"><span class="pre">Block</span></code> and all of its children’s Parameters(default), also can returns the select <code class="xref py py-class docutils literal notranslate"><span class="pre">Dict</span></code> which match some given regular expressions.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.ResidualCell.export" title="mxnet.gluon.rnn.ResidualCell.export"><code class="xref py py-obj docutils literal notranslate"><span class="pre">export</span></code></a>(path[, epoch, remove_amp_cast])</p></td> |
| <td><p>Export HybridBlock to json format that can be loaded by <cite>gluon.SymbolBlock.imports</cite> or the C++ interface.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.ResidualCell.forward" title="mxnet.gluon.rnn.ResidualCell.forward"><code class="xref py py-obj docutils literal notranslate"><span class="pre">forward</span></code></a>(inputs, states)</p></td> |
| <td><p>Unrolls the recurrent cell for one time step.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.ResidualCell.hybridize" title="mxnet.gluon.rnn.ResidualCell.hybridize"><code class="xref py py-obj docutils literal notranslate"><span class="pre">hybridize</span></code></a>([active])</p></td> |
| <td><p>Please refer description of HybridBlock hybridize().</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.ResidualCell.infer_shape" title="mxnet.gluon.rnn.ResidualCell.infer_shape"><code class="xref py py-obj docutils literal notranslate"><span class="pre">infer_shape</span></code></a>(i, x, is_bidirect)</p></td> |
| <td><p>Infers shape of Parameters from inputs.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.ResidualCell.infer_type" title="mxnet.gluon.rnn.ResidualCell.infer_type"><code class="xref py py-obj docutils literal notranslate"><span class="pre">infer_type</span></code></a>(*args)</p></td> |
| <td><p>Infers data type of Parameters from inputs.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.ResidualCell.initialize" title="mxnet.gluon.rnn.ResidualCell.initialize"><code class="xref py py-obj docutils literal notranslate"><span class="pre">initialize</span></code></a>([init, device, verbose, force_reinit])</p></td> |
| <td><p>Initializes <code class="xref py py-class docutils literal notranslate"><span class="pre">Parameter</span></code> s of this <code class="xref py py-class docutils literal notranslate"><span class="pre">Block</span></code> and its children.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.ResidualCell.load" title="mxnet.gluon.rnn.ResidualCell.load"><code class="xref py py-obj docutils literal notranslate"><span class="pre">load</span></code></a>(prefix)</p></td> |
| <td><p>Load a model saved using the <cite>save</cite> API</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.ResidualCell.load_dict" title="mxnet.gluon.rnn.ResidualCell.load_dict"><code class="xref py py-obj docutils literal notranslate"><span class="pre">load_dict</span></code></a>(param_dict[, device, …])</p></td> |
| <td><p>Load parameters from dict</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.ResidualCell.load_parameters" title="mxnet.gluon.rnn.ResidualCell.load_parameters"><code class="xref py py-obj docutils literal notranslate"><span class="pre">load_parameters</span></code></a>(filename[, device, …])</p></td> |
| <td><p>Load parameters from file previously saved by <cite>save_parameters</cite>.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.ResidualCell.optimize_for" title="mxnet.gluon.rnn.ResidualCell.optimize_for"><code class="xref py py-obj docutils literal notranslate"><span class="pre">optimize_for</span></code></a>(x, *args[, backend, clear, …])</p></td> |
| <td><p>Partitions the current HybridBlock and optimizes it for a given backend without executing a forward pass.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.ResidualCell.register_child" title="mxnet.gluon.rnn.ResidualCell.register_child"><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_child</span></code></a>(block[, name])</p></td> |
| <td><p>Registers block as a child of self.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.ResidualCell.register_forward_hook" title="mxnet.gluon.rnn.ResidualCell.register_forward_hook"><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_forward_hook</span></code></a>(hook)</p></td> |
| <td><p>Registers a forward hook on the block.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.ResidualCell.register_forward_pre_hook" title="mxnet.gluon.rnn.ResidualCell.register_forward_pre_hook"><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_forward_pre_hook</span></code></a>(hook)</p></td> |
| <td><p>Registers a forward pre-hook on the block.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.ResidualCell.register_op_hook" title="mxnet.gluon.rnn.ResidualCell.register_op_hook"><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_op_hook</span></code></a>(callback[, monitor_all])</p></td> |
| <td><p>Install callback monitor.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.ResidualCell.reset" title="mxnet.gluon.rnn.ResidualCell.reset"><code class="xref py py-obj docutils literal notranslate"><span class="pre">reset</span></code></a>()</p></td> |
| <td><p>Reset before re-using the cell for another graph.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.ResidualCell.reset_ctx" title="mxnet.gluon.rnn.ResidualCell.reset_ctx"><code class="xref py py-obj docutils literal notranslate"><span class="pre">reset_ctx</span></code></a>(ctx)</p></td> |
| <td><p>This function has been deprecated.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.ResidualCell.reset_device" title="mxnet.gluon.rnn.ResidualCell.reset_device"><code class="xref py py-obj docutils literal notranslate"><span class="pre">reset_device</span></code></a>(device)</p></td> |
| <td><p>Re-assign all Parameters to other devices.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.ResidualCell.save" title="mxnet.gluon.rnn.ResidualCell.save"><code class="xref py py-obj docutils literal notranslate"><span class="pre">save</span></code></a>(prefix)</p></td> |
| <td><p>Save the model architecture and parameters to load again later</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.ResidualCell.save_parameters" title="mxnet.gluon.rnn.ResidualCell.save_parameters"><code class="xref py py-obj docutils literal notranslate"><span class="pre">save_parameters</span></code></a>(filename[, deduplicate])</p></td> |
| <td><p>Save parameters to file.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.ResidualCell.setattr" title="mxnet.gluon.rnn.ResidualCell.setattr"><code class="xref py py-obj docutils literal notranslate"><span class="pre">setattr</span></code></a>(name, value)</p></td> |
| <td><p>Set an attribute to a new value for all Parameters.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.ResidualCell.share_parameters" title="mxnet.gluon.rnn.ResidualCell.share_parameters"><code class="xref py py-obj docutils literal notranslate"><span class="pre">share_parameters</span></code></a>(shared)</p></td> |
| <td><p>Share parameters recursively inside the model.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.ResidualCell.summary" title="mxnet.gluon.rnn.ResidualCell.summary"><code class="xref py py-obj docutils literal notranslate"><span class="pre">summary</span></code></a>(*inputs)</p></td> |
| <td><p>Print the summary of the model’s output and parameters.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.ResidualCell.unroll" title="mxnet.gluon.rnn.ResidualCell.unroll"><code class="xref py py-obj docutils literal notranslate"><span class="pre">unroll</span></code></a>(length, inputs[, begin_state, …])</p></td> |
| <td><p>Unrolls an RNN cell across time steps.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.ResidualCell.zero_grad" title="mxnet.gluon.rnn.ResidualCell.zero_grad"><code class="xref py py-obj docutils literal notranslate"><span class="pre">zero_grad</span></code></a>()</p></td> |
| <td><p>Sets all Parameters’ gradient buffer to 0.</p></td> |
| </tr> |
| </tbody> |
| </table> |
| <p><strong>Attributes</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.rnn.ResidualCell.params" title="mxnet.gluon.rnn.ResidualCell.params"><code class="xref py py-obj docutils literal notranslate"><span class="pre">params</span></code></a></p></td> |
| <td><p>Return an attribute of instance, which is of type owner.</p></td> |
| </tr> |
| </tbody> |
| </table> |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.ResidualCell.apply"> |
| <code class="sig-name descname">apply</code><span class="sig-paren">(</span><em class="sig-param">fn</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.rnn.ResidualCell.apply" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Applies <code class="docutils literal notranslate"><span class="pre">fn</span></code> recursively to every child block as well as self.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><p><strong>fn</strong> (<em>callable</em>) – Function to be applied to each submodule, of form <cite>fn(block)</cite>.</p> |
| </dd> |
| <dt class="field-even">Returns</dt> |
| <dd class="field-even"><p></p> |
| </dd> |
| <dt class="field-odd">Return type</dt> |
| <dd class="field-odd"><p>this block</p> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.ResidualCell.cast"> |
| <code class="sig-name descname">cast</code><span class="sig-paren">(</span><em class="sig-param">dtype</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.rnn.ResidualCell.cast" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Cast this Block to use another data type.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><p><strong>dtype</strong> (<em>str</em><em> or </em><em>numpy.dtype</em>) – The new data type.</p> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.ResidualCell.collect_params"> |
| <code class="sig-name descname">collect_params</code><span class="sig-paren">(</span><em class="sig-param">select=None</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.rnn.ResidualCell.collect_params" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Returns a <code class="xref py py-class docutils literal notranslate"><span class="pre">Dict</span></code> containing this <code class="xref py py-class docutils literal notranslate"><span class="pre">Block</span></code> and all of its |
| children’s Parameters(default), also can returns the select <code class="xref py py-class docutils literal notranslate"><span class="pre">Dict</span></code> |
| which match some given regular expressions.</p> |
| <p>For example, collect the specified parameters in [‘conv1.weight’, ‘conv1.bias’, ‘fc.weight’, |
| ‘fc.bias’]:</p> |
| <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">model</span><span class="o">.</span><span class="n">collect_params</span><span class="p">(</span><span class="s1">'conv1.weight|conv1.bias|fc.weight|fc.bias'</span><span class="p">)</span> |
| </pre></div> |
| </div> |
| <p>or collect all parameters whose names end with ‘weight’ or ‘bias’, this can be done |
| using regular expressions:</p> |
| <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">model</span><span class="o">.</span><span class="n">collect_params</span><span class="p">(</span><span class="s1">'.*weight|.*bias'</span><span class="p">)</span> |
| </pre></div> |
| </div> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><p><strong>select</strong> (<em>str</em>) – regular expressions</p> |
| </dd> |
| <dt class="field-even">Returns</dt> |
| <dd class="field-even"><p></p> |
| </dd> |
| <dt class="field-odd">Return type</dt> |
| <dd class="field-odd"><p>The selected <code class="xref py py-class docutils literal notranslate"><span class="pre">Dict</span></code></p> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.ResidualCell.export"> |
| <code class="sig-name descname">export</code><span class="sig-paren">(</span><em class="sig-param">path</em>, <em class="sig-param">epoch=0</em>, <em class="sig-param">remove_amp_cast=True</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.rnn.ResidualCell.export" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Export HybridBlock to json format that can be loaded by |
| <cite>gluon.SymbolBlock.imports</cite> or the C++ interface.</p> |
| <div class="admonition note"> |
| <p class="admonition-title">Note</p> |
| <p>When there are only one input, it will have name <cite>data</cite>. When there |
| Are more than one inputs, they will be named as <cite>data0</cite>, <cite>data1</cite>, etc.</p> |
| </div> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>path</strong> (<em>str</em><em> or </em><em>None</em>) – Path to save model. Two files <cite>path-symbol.json</cite> and <cite>path-xxxx.params</cite> |
| will be created, where xxxx is the 4 digits epoch number. |
| If None, do not export to file but return Python Symbol object and |
| corresponding dictionary of parameters.</p></li> |
| <li><p><strong>epoch</strong> (<em>int</em>) – Epoch number of saved model.</p></li> |
| <li><p><strong>remove_amp_cast</strong> (<em>bool</em><em>, </em><em>optional</em>) – Whether to remove the amp_cast and amp_multicast operators, before saving the model.</p></li> |
| </ul> |
| </dd> |
| <dt class="field-even">Returns</dt> |
| <dd class="field-even"><p><ul class="simple"> |
| <li><p><strong>symbol_filename</strong> (<em>str</em>) – Filename to which model symbols were saved, including <cite>path</cite> prefix.</p></li> |
| <li><p><strong>params_filename</strong> (<em>str</em>) – Filename to which model parameters were saved, including <cite>path</cite> prefix.</p></li> |
| </ul> |
| </p> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.ResidualCell.forward"> |
| <code class="sig-name descname">forward</code><span class="sig-paren">(</span><em class="sig-param">inputs</em>, <em class="sig-param">states</em><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/mxnet/gluon/rnn/rnn_cell.html#ResidualCell.forward"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.gluon.rnn.ResidualCell.forward" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Unrolls the recurrent cell for one time step.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>inputs</strong> (<em>sym.Variable</em>) – Input symbol, 2D, of shape (batch_size * num_units).</p></li> |
| <li><p><strong>states</strong> (<em>list of sym.Variable</em>) – RNN state from previous step or the output of begin_state().</p></li> |
| </ul> |
| </dd> |
| <dt class="field-even">Returns</dt> |
| <dd class="field-even"><p><ul class="simple"> |
| <li><p><strong>output</strong> (<em>Symbol</em>) – Symbol corresponding to the output from the RNN when unrolling |
| for a single time step.</p></li> |
| <li><p><strong>states</strong> (<em>list of Symbol</em>) – The new state of this RNN after this unrolling. |
| The type of this symbol is same as the output of <cite>begin_state()</cite>. |
| This can be used as an input state to the next time step |
| of this RNN.</p></li> |
| </ul> |
| </p> |
| </dd> |
| </dl> |
| <div class="admonition seealso"> |
| <p class="admonition-title">See also</p> |
| <dl class="simple"> |
| <dt><code class="xref py py-meth docutils literal notranslate"><span class="pre">begin_state()</span></code></dt><dd><p>This function can provide the states for the first time step.</p> |
| </dd> |
| <dt><a class="reference internal" href="#mxnet.gluon.rnn.ResidualCell.unroll" title="mxnet.gluon.rnn.ResidualCell.unroll"><code class="xref py py-meth docutils literal notranslate"><span class="pre">unroll()</span></code></a></dt><dd><p>This function unrolls an RNN for a given number of (>=1) time steps.</p> |
| </dd> |
| </dl> |
| </div> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.ResidualCell.hybridize"> |
| <code class="sig-name descname">hybridize</code><span class="sig-paren">(</span><em class="sig-param">active=True</em>, <em class="sig-param">**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.rnn.ResidualCell.hybridize" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Please refer description of HybridBlock hybridize().</p> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.ResidualCell.infer_shape"> |
| <code class="sig-name descname">infer_shape</code><span class="sig-paren">(</span><em class="sig-param">i</em>, <em class="sig-param">x</em>, <em class="sig-param">is_bidirect</em><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/mxnet/gluon/rnn/rnn_cell.html#ResidualCell.infer_shape"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.gluon.rnn.ResidualCell.infer_shape" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Infers shape of Parameters from inputs.</p> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.ResidualCell.infer_type"> |
| <code class="sig-name descname">infer_type</code><span class="sig-paren">(</span><em class="sig-param">*args</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.rnn.ResidualCell.infer_type" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Infers data type of Parameters from inputs.</p> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.ResidualCell.initialize"> |
| <code class="sig-name descname">initialize</code><span class="sig-paren">(</span><em class="sig-param">init=<mxnet.initializer.Uniform object></em>, <em class="sig-param">device=None</em>, <em class="sig-param">verbose=False</em>, <em class="sig-param">force_reinit=False</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.rnn.ResidualCell.initialize" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Initializes <code class="xref py py-class docutils literal notranslate"><span class="pre">Parameter</span></code> s of this <code class="xref py py-class docutils literal notranslate"><span class="pre">Block</span></code> and its children.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>init</strong> (<a class="reference internal" href="../../initializer/index.html#mxnet.initializer.Initializer" title="mxnet.initializer.Initializer"><em>Initializer</em></a>) – Global default Initializer to be used when <code class="xref py py-meth docutils literal notranslate"><span class="pre">Parameter.init()</span></code> is <code class="docutils literal notranslate"><span class="pre">None</span></code>. |
| Otherwise, <code class="xref py py-meth docutils literal notranslate"><span class="pre">Parameter.init()</span></code> takes precedence.</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>) – Keeps a copy of Parameters on one or many device(s).</p></li> |
| <li><p><strong>verbose</strong> (<em>bool</em><em>, </em><em>default False</em>) – Whether to verbosely print out details on initialization.</p></li> |
| <li><p><strong>force_reinit</strong> (<em>bool</em><em>, </em><em>default False</em>) – Whether to force re-initialization if parameter is already initialized.</p></li> |
| </ul> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.ResidualCell.load"> |
| <code class="sig-name descname">load</code><span class="sig-paren">(</span><em class="sig-param">prefix</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.rnn.ResidualCell.load" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Load a model saved using the <cite>save</cite> API</p> |
| <p>Reconfigures a model using the saved configuration. This function |
| does not regenerate the model architecture. It resets each Block’s |
| parameter UUIDs as they were when saved in order to match the names of the |
| saved parameters.</p> |
| <p>This function assumes the Blocks in the model were created in the same |
| order they were when the model was saved. This is because each Block is |
| uniquely identified by Block class name and a unique ID in order (since |
| its an OrderedDict) and uses the unique ID to denote that specific Block.</p> |
| <p>Assumes that the model is created in an identical order every time. |
| If the model is not able to be recreated deterministically do not |
| use this set of APIs to save/load your model.</p> |
| <p>For HybridBlocks, the cached_graph (Symbol & inputs) and settings are |
| restored if it had been hybridized before saving.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><p><strong>prefix</strong> (<em>str</em>) – The prefix to use in filenames for loading this model: |
| <prefix>-model.json and <prefix>-model.params</p> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.ResidualCell.load_dict"> |
| <code class="sig-name descname">load_dict</code><span class="sig-paren">(</span><em class="sig-param">param_dict</em>, <em class="sig-param">device=None</em>, <em class="sig-param">allow_missing=False</em>, <em class="sig-param">ignore_extra=False</em>, <em class="sig-param">cast_dtype=False</em>, <em class="sig-param">dtype_source='current'</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.rnn.ResidualCell.load_dict" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Load parameters from dict</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>param_dict</strong> (<em>dict</em>) – Dictionary containing model 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>, </em><em>optional</em>) – Device context on which the memory is allocated. Default is |
| <cite>mxnet.device.current_device()</cite>.</p></li> |
| <li><p><strong>allow_missing</strong> (<em>bool</em><em>, </em><em>default False</em>) – Whether to silently skip loading parameters not represented in the file.</p></li> |
| <li><p><strong>ignore_extra</strong> (<em>bool</em><em>, </em><em>default False</em>) – Whether to silently ignore parameters from the file that are not |
| present in this dict.</p></li> |
| <li><p><strong>cast_dtype</strong> (<em>bool</em><em>, </em><em>default False</em>) – Cast the data type of the NDArray loaded from the checkpoint to the dtype |
| provided by the Parameter if any</p></li> |
| <li><p><strong>dtype_source</strong> (<em>str</em><em>, </em><em>default 'current'</em>) – must be in {‘current’, ‘saved’} |
| Only valid if cast_dtype=True, specify the source of the dtype for casting |
| the parameters</p></li> |
| </ul> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.ResidualCell.load_parameters"> |
| <code class="sig-name descname">load_parameters</code><span class="sig-paren">(</span><em class="sig-param">filename</em>, <em class="sig-param">device=None</em>, <em class="sig-param">allow_missing=False</em>, <em class="sig-param">ignore_extra=False</em>, <em class="sig-param">cast_dtype=False</em>, <em class="sig-param">dtype_source='current'</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.rnn.ResidualCell.load_parameters" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Load parameters from file previously saved by <cite>save_parameters</cite>.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>filename</strong> (<em>str</em>) – Path to parameter file.</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><em>, </em><em>default cpu</em><em>(</em><em>)</em>) – Device(s) to initialize loaded parameters on.</p></li> |
| <li><p><strong>allow_missing</strong> (<em>bool</em><em>, </em><em>default False</em>) – Whether to silently skip loading parameters not represents in the file.</p></li> |
| <li><p><strong>ignore_extra</strong> (<em>bool</em><em>, </em><em>default False</em>) – Whether to silently ignore parameters from the file that are not |
| present in this Block.</p></li> |
| <li><p><strong>cast_dtype</strong> (<em>bool</em><em>, </em><em>default False</em>) – Cast the data type of the NDArray loaded from the checkpoint to the dtype |
| provided by the Parameter if any.</p></li> |
| <li><p><strong>dtype_source</strong> (<em>str</em><em>, </em><em>default 'current'</em>) – must be in {‘current’, ‘saved’} |
| Only valid if cast_dtype=True, specify the source of the dtype for casting |
| the parameters</p></li> |
| </ul> |
| </dd> |
| </dl> |
| <p class="rubric">References</p> |
| <p><a class="reference external" href="https://mxnet.apache.org/api/python/docs/tutorials/packages/gluon/blocks/save_load_params.html">Saving and Loading Gluon Models</a></p> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.ResidualCell.optimize_for"> |
| <code class="sig-name descname">optimize_for</code><span class="sig-paren">(</span><em class="sig-param">x</em>, <em class="sig-param">*args</em>, <em class="sig-param">backend=None</em>, <em class="sig-param">clear=False</em>, <em class="sig-param">partition_if_dynamic=True</em>, <em class="sig-param">static_alloc=False</em>, <em class="sig-param">static_shape=False</em>, <em class="sig-param">inline_limit=2</em>, <em class="sig-param">forward_bulk_size=None</em>, <em class="sig-param">backward_bulk_size=None</em>, <em class="sig-param">**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.rnn.ResidualCell.optimize_for" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Partitions the current HybridBlock and optimizes it for a given backend |
| without executing a forward pass. Modifies the HybridBlock in-place.</p> |
| <p>Immediately partitions a HybridBlock using the specified backend. Combines |
| the work done in the hybridize API with part of the work done in the forward |
| pass without calling the CachedOp. Can be used in place of hybridize, |
| afterwards <cite>export</cite> can be called or inference can be run. See README.md in |
| example/extensions/lib_subgraph/README.md for more details.</p> |
| <p class="rubric">Examples</p> |
| <p># partition and then export to file |
| block.optimize_for(x, backend=’myPart’) |
| block.export(‘partitioned’)</p> |
| <p># partition and then run inference |
| block.optimize_for(x, backend=’myPart’) |
| block(x)</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>x</strong> (<a class="reference internal" href="../../legacy/ndarray/ndarray.html#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – first input to model</p></li> |
| <li><p><strong>*args</strong> (<a class="reference internal" href="../../legacy/ndarray/ndarray.html#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – other inputs to model</p></li> |
| <li><p><strong>backend</strong> (<em>str</em>) – The name of backend, as registered in <cite>SubgraphBackendRegistry</cite>, default None</p></li> |
| <li><p><strong>backend_opts</strong> (<em>dict of user-specified options to pass to the backend for partitioning</em><em>, </em><em>optional</em>) – Passed on to <cite>PrePartition</cite> and <cite>PostPartition</cite> functions of <cite>SubgraphProperty</cite></p></li> |
| <li><p><strong>clear</strong> (<em>bool</em><em>, </em><em>default False</em>) – clears any previous optimizations</p></li> |
| <li><p><strong>partition_if_dynamic</strong> (<em>bool</em><em>, </em><em>default False</em>) – whether to partition the graph when dynamic shape op exists</p></li> |
| <li><p><strong>static_alloc</strong> (<em>bool</em><em>, </em><em>default False</em>) – Statically allocate memory to improve speed. Memory usage may increase.</p></li> |
| <li><p><strong>static_shape</strong> (<em>bool</em><em>, </em><em>default False</em>) – Optimize for invariant input shapes between iterations. Must also |
| set static_alloc to True. Change of input shapes is still allowed |
| but slower.</p></li> |
| <li><p><strong>inline_limit</strong> (<em>optional int</em><em>, </em><em>default 2</em>) – Maximum number of operators that can be inlined.</p></li> |
| <li><p><strong>forward_bulk_size</strong> (<em>optional int</em><em>, </em><em>default None</em>) – Segment size of bulk execution during forward pass.</p></li> |
| <li><p><strong>backward_bulk_size</strong> (<em>optional int</em><em>, </em><em>default None</em>) – Segment size of bulk execution during backward pass.</p></li> |
| <li><p><strong>**kwargs</strong> (<em>The backend options</em><em>, </em><em>optional</em>) – Passed on to <cite>PrePartition</cite> and <cite>PostPartition</cite> functions of <cite>SubgraphProperty</cite></p></li> |
| </ul> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.ResidualCell.params"> |
| <em class="property">property </em><code class="sig-name descname">params</code><a class="headerlink" href="#mxnet.gluon.rnn.ResidualCell.params" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Return an attribute of instance, which is of type owner.</p> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.ResidualCell.register_child"> |
| <code class="sig-name descname">register_child</code><span class="sig-paren">(</span><em class="sig-param">block</em>, <em class="sig-param">name=None</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.rnn.ResidualCell.register_child" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Registers block as a child of self. <code class="xref py py-class docutils literal notranslate"><span class="pre">Block</span></code> s assigned to self as |
| attributes will be registered automatically.</p> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.ResidualCell.register_forward_hook"> |
| <code class="sig-name descname">register_forward_hook</code><span class="sig-paren">(</span><em class="sig-param">hook</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.rnn.ResidualCell.register_forward_hook" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Registers a forward hook on the block.</p> |
| <p>The hook function is called immediately after <a class="reference internal" href="#mxnet.gluon.rnn.ResidualCell.forward" title="mxnet.gluon.rnn.ResidualCell.forward"><code class="xref py py-func docutils literal notranslate"><span class="pre">forward()</span></code></a>. |
| It should not modify the input or output.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><p><strong>hook</strong> (<em>callable</em>) – The forward hook function of form <cite>hook(block, input, output) -> None</cite>.</p> |
| </dd> |
| <dt class="field-even">Returns</dt> |
| <dd class="field-even"><p></p> |
| </dd> |
| <dt class="field-odd">Return type</dt> |
| <dd class="field-odd"><p><code class="xref py py-class docutils literal notranslate"><span class="pre">mxnet.gluon.utils.HookHandle</span></code></p> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.ResidualCell.register_forward_pre_hook"> |
| <code class="sig-name descname">register_forward_pre_hook</code><span class="sig-paren">(</span><em class="sig-param">hook</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.rnn.ResidualCell.register_forward_pre_hook" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Registers a forward pre-hook on the block.</p> |
| <p>The hook function is called immediately before <a class="reference internal" href="#mxnet.gluon.rnn.ResidualCell.forward" title="mxnet.gluon.rnn.ResidualCell.forward"><code class="xref py py-func docutils literal notranslate"><span class="pre">forward()</span></code></a>. |
| It should not modify the input or output.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><p><strong>hook</strong> (<em>callable</em>) – The forward hook function of form <cite>hook(block, input) -> None</cite>.</p> |
| </dd> |
| <dt class="field-even">Returns</dt> |
| <dd class="field-even"><p></p> |
| </dd> |
| <dt class="field-odd">Return type</dt> |
| <dd class="field-odd"><p><code class="xref py py-class docutils literal notranslate"><span class="pre">mxnet.gluon.utils.HookHandle</span></code></p> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.ResidualCell.register_op_hook"> |
| <code class="sig-name descname">register_op_hook</code><span class="sig-paren">(</span><em class="sig-param">callback</em>, <em class="sig-param">monitor_all=False</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.rnn.ResidualCell.register_op_hook" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Install callback monitor.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>callback</strong> (<em>function</em>) – Function called to inspect the values of the intermediate outputs |
| of blocks after hybridization. It takes 3 parameters: |
| name of the tensor being inspected (str) |
| name of the operator producing or consuming that tensor (str) |
| tensor being inspected (NDArray).</p></li> |
| <li><p><strong>monitor_all</strong> (<em>bool</em><em>, </em><em>default False</em>) – If True, monitor both input and output, otherwise monitor output only.</p></li> |
| </ul> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.ResidualCell.reset"> |
| <code class="sig-name descname">reset</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.rnn.ResidualCell.reset" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Reset before re-using the cell for another graph.</p> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.ResidualCell.reset_ctx"> |
| <code class="sig-name descname">reset_ctx</code><span class="sig-paren">(</span><em class="sig-param">ctx</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.rnn.ResidualCell.reset_ctx" title="Permalink to this definition">¶</a></dt> |
| <dd><p>This function has been deprecated. Please refer to <code class="docutils literal notranslate"><span class="pre">Block.reset_device</span></code>.</p> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.ResidualCell.reset_device"> |
| <code class="sig-name descname">reset_device</code><span class="sig-paren">(</span><em class="sig-param">device</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.rnn.ResidualCell.reset_device" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Re-assign all Parameters to other devices.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><p><strong>device</strong> (Device or list of Device, default <code class="xref py py-meth docutils literal notranslate"><span class="pre">device.current_device()</span></code>.) – Assign Parameter to given device. If device is a list of Device, a |
| copy will be made for each device.</p> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.ResidualCell.save"> |
| <code class="sig-name descname">save</code><span class="sig-paren">(</span><em class="sig-param">prefix</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.rnn.ResidualCell.save" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Save the model architecture and parameters to load again later</p> |
| <p>Saves the model architecture as a nested dictionary where each Block |
| in the model is a dictionary and its children are sub-dictionaries.</p> |
| <p>Each Block is uniquely identified by Block class name and a unique ID. |
| We save each Block’s parameter UUID to restore later in order to match |
| the saved parameters.</p> |
| <p>Recursively traverses a Block’s children in order (since its an |
| OrderedDict) and uses the unique ID to denote that specific Block.</p> |
| <p>Assumes that the model is created in an identical order every time. |
| If the model is not able to be recreated deterministically do not |
| use this set of APIs to save/load your model.</p> |
| <p>For HybridBlocks, the cached_graph is saved (Symbol & inputs) if |
| it has already been hybridized.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><p><strong>prefix</strong> (<em>str</em>) – The prefix to use in filenames for saving this model: |
| <prefix>-model.json and <prefix>-model.params</p> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.ResidualCell.save_parameters"> |
| <code class="sig-name descname">save_parameters</code><span class="sig-paren">(</span><em class="sig-param">filename</em>, <em class="sig-param">deduplicate=False</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.rnn.ResidualCell.save_parameters" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Save parameters to file.</p> |
| <p>Saved parameters can only be loaded with <cite>load_parameters</cite>. Note that this |
| method only saves parameters, not model structure. If you want to save |
| model structures, please use <code class="xref py py-meth docutils literal notranslate"><span class="pre">HybridBlock.export()</span></code>.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>filename</strong> (<em>str</em>) – Path to file.</p></li> |
| <li><p><strong>deduplicate</strong> (<em>bool</em><em>, </em><em>default False</em>) – If True, save shared parameters only once. Otherwise, if a Block |
| contains multiple sub-blocks that share parameters, each of the |
| shared parameters will be separately saved for every sub-block.</p></li> |
| </ul> |
| </dd> |
| </dl> |
| <p class="rubric">References</p> |
| <p><a class="reference external" href="https://mxnet.apache.org/api/python/docs/tutorials/packages/gluon/blocks/save_load_params.html">Saving and Loading Gluon Models</a></p> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.ResidualCell.setattr"> |
| <code class="sig-name descname">setattr</code><span class="sig-paren">(</span><em class="sig-param">name</em>, <em class="sig-param">value</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.rnn.ResidualCell.setattr" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Set an attribute to a new value for all Parameters.</p> |
| <p>For example, set grad_req to null if you don’t need gradient w.r.t a |
| model’s Parameters:</p> |
| <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">model</span><span class="o">.</span><span class="n">setattr</span><span class="p">(</span><span class="s1">'grad_req'</span><span class="p">,</span> <span class="s1">'null'</span><span class="p">)</span> |
| </pre></div> |
| </div> |
| <p>or change the learning rate multiplier:</p> |
| <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">model</span><span class="o">.</span><span class="n">setattr</span><span class="p">(</span><span class="s1">'lr_mult'</span><span class="p">,</span> <span class="mf">0.5</span><span class="p">)</span> |
| </pre></div> |
| </div> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>name</strong> (<em>str</em>) – Name of the attribute.</p></li> |
| <li><p><strong>value</strong> (<em>valid type for attribute name</em>) – The new value for the attribute.</p></li> |
| </ul> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.ResidualCell.share_parameters"> |
| <code class="sig-name descname">share_parameters</code><span class="sig-paren">(</span><em class="sig-param">shared</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.rnn.ResidualCell.share_parameters" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Share parameters recursively inside the model.</p> |
| <p>For example, if you want <code class="docutils literal notranslate"><span class="pre">dense1</span></code> to share <code class="docutils literal notranslate"><span class="pre">dense0</span></code>’s weights, you can do:</p> |
| <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">dense0</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Dense</span><span class="p">(</span><span class="mi">20</span><span class="p">)</span> |
| <span class="n">dense1</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Dense</span><span class="p">(</span><span class="mi">20</span><span class="p">)</span> |
| <span class="n">dense1</span><span class="o">.</span><span class="n">share_parameters</span><span class="p">(</span><span class="n">dense0</span><span class="o">.</span><span class="n">collect_params</span><span class="p">())</span> |
| </pre></div> |
| </div> |
| <dl class="simple"> |
| <dt>which equals to</dt><dd><p>dense1.weight = dense0.weight |
| dense1.bias = dense0.bias</p> |
| </dd> |
| </dl> |
| <p>Note that unlike the <cite>load_parameters</cite> or <cite>load_dict</cite> functions, |
| <cite>share_parameters</cite> results in the <cite>Parameter</cite> object being shared (or |
| tied) between the models, whereas <cite>load_parameters</cite> or <cite>load_dict</cite> only |
| set the value of the data dictionary of a model. If you call |
| <cite>load_parameters</cite> or <cite>load_dict</cite> after <cite>share_parameters</cite>, the loaded |
| value will be reflected in all networks that use the shared (or tied) |
| <cite>Parameter</cite> object.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><p><strong>shared</strong> (<em>Dict</em>) – Dict of the shared parameters.</p> |
| </dd> |
| <dt class="field-even">Returns</dt> |
| <dd class="field-even"><p></p> |
| </dd> |
| <dt class="field-odd">Return type</dt> |
| <dd class="field-odd"><p>this block</p> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.ResidualCell.summary"> |
| <code class="sig-name descname">summary</code><span class="sig-paren">(</span><em class="sig-param">*inputs</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.rnn.ResidualCell.summary" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Print the summary of the model’s output and parameters.</p> |
| <p>The network must have been initialized, and must not have been hybridized.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><p><strong>inputs</strong> (<em>object</em>) – Any input that the model supports. For any tensor in the input, only |
| <a class="reference internal" href="../../legacy/ndarray/ndarray.html#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><code class="xref py py-class docutils literal notranslate"><span class="pre">mxnet.ndarray.NDArray</span></code></a> is supported.</p> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.ResidualCell.unroll"> |
| <code class="sig-name descname">unroll</code><span class="sig-paren">(</span><em class="sig-param">length</em>, <em class="sig-param">inputs</em>, <em class="sig-param">begin_state=None</em>, <em class="sig-param">layout='NTC'</em>, <em class="sig-param">merge_outputs=None</em>, <em class="sig-param">valid_length=None</em><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/mxnet/gluon/rnn/rnn_cell.html#ResidualCell.unroll"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.gluon.rnn.ResidualCell.unroll" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Unrolls an RNN cell across time steps.</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>) – Number of steps to unroll.</p></li> |
| <li><p><strong>inputs</strong> (<a class="reference internal" href="../../legacy/symbol/symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a><em>, </em><em>list of Symbol</em><em>, or </em><em>None</em>) – <p>If <cite>inputs</cite> is a single Symbol (usually the output |
| of Embedding symbol), it should have shape |
| (batch_size, length, …) if <cite>layout</cite> is ‘NTC’, |
| or (length, batch_size, …) if <cite>layout</cite> is ‘TNC’.</p> |
| <p>If <cite>inputs</cite> is a list of symbols (usually output of |
| previous unroll), they should all have shape |
| (batch_size, …).</p> |
| </p></li> |
| <li><p><strong>begin_state</strong> (<em>nested list of Symbol</em><em>, </em><em>optional</em>) – Input states created by <cite>begin_state()</cite> |
| or output state of another cell. |
| Created from <cite>begin_state()</cite> if <cite>None</cite>.</p></li> |
| <li><p><strong>layout</strong> (<em>str</em><em>, </em><em>optional</em>) – <cite>layout</cite> of input symbol. Only used if inputs |
| is a single Symbol.</p></li> |
| <li><p><strong>merge_outputs</strong> (<em>bool</em><em>, </em><em>optional</em>) – If <cite>False</cite>, returns outputs as a list of Symbols. |
| If <cite>True</cite>, concatenates output across time steps |
| and returns a single symbol with shape |
| (batch_size, length, …) if layout is ‘NTC’, |
| or (length, batch_size, …) if layout is ‘TNC’. |
| If <cite>None</cite>, output whatever is faster.</p></li> |
| <li><p><strong>valid_length</strong> (<a class="reference internal" href="../../legacy/symbol/symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a><em>, </em><a class="reference internal" href="../../legacy/ndarray/ndarray.html#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a><em> or </em><em>None</em>) – <cite>valid_length</cite> specifies the length of the sequences in the batch without padding. |
| This option is especially useful for building sequence-to-sequence models where |
| the input and output sequences would potentially be padded. |
| If <cite>valid_length</cite> is None, all sequences are assumed to have the same length. |
| If <cite>valid_length</cite> is a Symbol or NDArray, it should have shape (batch_size,). |
| The ith element will be the length of the ith sequence in the batch. |
| The last valid state will be return and the padded outputs will be masked with 0. |
| Note that <cite>valid_length</cite> must be smaller or equal to <cite>length</cite>.</p></li> |
| </ul> |
| </dd> |
| <dt class="field-even">Returns</dt> |
| <dd class="field-even"><p><ul class="simple"> |
| <li><p><strong>outputs</strong> (<em>list of Symbol or Symbol</em>) – Symbol (if <cite>merge_outputs</cite> is True) or list of Symbols |
| (if <cite>merge_outputs</cite> is False) corresponding to the output from |
| the RNN from this unrolling.</p></li> |
| <li><p><strong>states</strong> (<em>list of Symbol</em>) – The new state of this RNN after this unrolling. |
| The type of this symbol is same as the output of <cite>begin_state()</cite>.</p></li> |
| </ul> |
| </p> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.ResidualCell.zero_grad"> |
| <code class="sig-name descname">zero_grad</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.rnn.ResidualCell.zero_grad" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Sets all Parameters’ gradient buffer to 0.</p> |
| </dd></dl> |
| |
| </dd></dl> |
| |
| <dl class="class"> |
| <dt id="mxnet.gluon.rnn.SequentialRNNCell"> |
| <em class="property">class </em><code class="sig-name descname">SequentialRNNCell</code><a class="reference internal" href="../../../_modules/mxnet/gluon/rnn/rnn_cell.html#SequentialRNNCell"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.gluon.rnn.SequentialRNNCell" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">mxnet.gluon.rnn.rnn_cell.RecurrentCell</span></code></p> |
| <p>Sequentially stacking multiple RNN cells.</p> |
| <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.rnn.SequentialRNNCell.add" title="mxnet.gluon.rnn.SequentialRNNCell.add"><code class="xref py py-obj docutils literal notranslate"><span class="pre">add</span></code></a>(cell)</p></td> |
| <td><p>Appends a cell into the stack.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.SequentialRNNCell.apply" title="mxnet.gluon.rnn.SequentialRNNCell.apply"><code class="xref py py-obj docutils literal notranslate"><span class="pre">apply</span></code></a>(fn)</p></td> |
| <td><p>Applies <code class="docutils literal notranslate"><span class="pre">fn</span></code> recursively to every child block as well as self.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.SequentialRNNCell.begin_state" title="mxnet.gluon.rnn.SequentialRNNCell.begin_state"><code class="xref py py-obj docutils literal notranslate"><span class="pre">begin_state</span></code></a>(**kwargs)</p></td> |
| <td><p>Initial state for this cell.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.SequentialRNNCell.cast" title="mxnet.gluon.rnn.SequentialRNNCell.cast"><code class="xref py py-obj docutils literal notranslate"><span class="pre">cast</span></code></a>(dtype)</p></td> |
| <td><p>Cast this Block to use another data type.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.SequentialRNNCell.collect_params" title="mxnet.gluon.rnn.SequentialRNNCell.collect_params"><code class="xref py py-obj docutils literal notranslate"><span class="pre">collect_params</span></code></a>([select])</p></td> |
| <td><p>Returns a <code class="xref py py-class docutils literal notranslate"><span class="pre">Dict</span></code> containing this <code class="xref py py-class docutils literal notranslate"><span class="pre">Block</span></code> and all of its children’s Parameters(default), also can returns the select <code class="xref py py-class docutils literal notranslate"><span class="pre">Dict</span></code> which match some given regular expressions.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.SequentialRNNCell.forward" title="mxnet.gluon.rnn.SequentialRNNCell.forward"><code class="xref py py-obj docutils literal notranslate"><span class="pre">forward</span></code></a>(*args, **kwargs)</p></td> |
| <td><p>Unrolls the recurrent cell for one time step.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.SequentialRNNCell.hybridize" title="mxnet.gluon.rnn.SequentialRNNCell.hybridize"><code class="xref py py-obj docutils literal notranslate"><span class="pre">hybridize</span></code></a>([active])</p></td> |
| <td><p>Please refer description of HybridBlock hybridize().</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.SequentialRNNCell.initialize" title="mxnet.gluon.rnn.SequentialRNNCell.initialize"><code class="xref py py-obj docutils literal notranslate"><span class="pre">initialize</span></code></a>([init, device, verbose, force_reinit])</p></td> |
| <td><p>Initializes <code class="xref py py-class docutils literal notranslate"><span class="pre">Parameter</span></code> s of this <code class="xref py py-class docutils literal notranslate"><span class="pre">Block</span></code> and its children.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.SequentialRNNCell.load" title="mxnet.gluon.rnn.SequentialRNNCell.load"><code class="xref py py-obj docutils literal notranslate"><span class="pre">load</span></code></a>(prefix)</p></td> |
| <td><p>Load a model saved using the <cite>save</cite> API</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.SequentialRNNCell.load_dict" title="mxnet.gluon.rnn.SequentialRNNCell.load_dict"><code class="xref py py-obj docutils literal notranslate"><span class="pre">load_dict</span></code></a>(param_dict[, device, …])</p></td> |
| <td><p>Load parameters from dict</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.SequentialRNNCell.load_parameters" title="mxnet.gluon.rnn.SequentialRNNCell.load_parameters"><code class="xref py py-obj docutils literal notranslate"><span class="pre">load_parameters</span></code></a>(filename[, device, …])</p></td> |
| <td><p>Load parameters from file previously saved by <cite>save_parameters</cite>.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.SequentialRNNCell.register_child" title="mxnet.gluon.rnn.SequentialRNNCell.register_child"><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_child</span></code></a>(block[, name])</p></td> |
| <td><p>Registers block as a child of self.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.SequentialRNNCell.register_forward_hook" title="mxnet.gluon.rnn.SequentialRNNCell.register_forward_hook"><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_forward_hook</span></code></a>(hook)</p></td> |
| <td><p>Registers a forward hook on the block.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.SequentialRNNCell.register_forward_pre_hook" title="mxnet.gluon.rnn.SequentialRNNCell.register_forward_pre_hook"><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_forward_pre_hook</span></code></a>(hook)</p></td> |
| <td><p>Registers a forward pre-hook on the block.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.SequentialRNNCell.register_op_hook" title="mxnet.gluon.rnn.SequentialRNNCell.register_op_hook"><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_op_hook</span></code></a>(callback[, monitor_all])</p></td> |
| <td><p>Install callback monitor.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.SequentialRNNCell.reset" title="mxnet.gluon.rnn.SequentialRNNCell.reset"><code class="xref py py-obj docutils literal notranslate"><span class="pre">reset</span></code></a>()</p></td> |
| <td><p>Reset before re-using the cell for another graph.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.SequentialRNNCell.reset_ctx" title="mxnet.gluon.rnn.SequentialRNNCell.reset_ctx"><code class="xref py py-obj docutils literal notranslate"><span class="pre">reset_ctx</span></code></a>(ctx)</p></td> |
| <td><p>This function has been deprecated.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.SequentialRNNCell.reset_device" title="mxnet.gluon.rnn.SequentialRNNCell.reset_device"><code class="xref py py-obj docutils literal notranslate"><span class="pre">reset_device</span></code></a>(device)</p></td> |
| <td><p>Re-assign all Parameters to other devices.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.SequentialRNNCell.save" title="mxnet.gluon.rnn.SequentialRNNCell.save"><code class="xref py py-obj docutils literal notranslate"><span class="pre">save</span></code></a>(prefix)</p></td> |
| <td><p>Save the model architecture and parameters to load again later</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.SequentialRNNCell.save_parameters" title="mxnet.gluon.rnn.SequentialRNNCell.save_parameters"><code class="xref py py-obj docutils literal notranslate"><span class="pre">save_parameters</span></code></a>(filename[, deduplicate])</p></td> |
| <td><p>Save parameters to file.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.SequentialRNNCell.setattr" title="mxnet.gluon.rnn.SequentialRNNCell.setattr"><code class="xref py py-obj docutils literal notranslate"><span class="pre">setattr</span></code></a>(name, value)</p></td> |
| <td><p>Set an attribute to a new value for all Parameters.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.SequentialRNNCell.share_parameters" title="mxnet.gluon.rnn.SequentialRNNCell.share_parameters"><code class="xref py py-obj docutils literal notranslate"><span class="pre">share_parameters</span></code></a>(shared)</p></td> |
| <td><p>Share parameters recursively inside the model.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.SequentialRNNCell.state_info" title="mxnet.gluon.rnn.SequentialRNNCell.state_info"><code class="xref py py-obj docutils literal notranslate"><span class="pre">state_info</span></code></a>([batch_size])</p></td> |
| <td><p>shape and layout information of states</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.SequentialRNNCell.summary" title="mxnet.gluon.rnn.SequentialRNNCell.summary"><code class="xref py py-obj docutils literal notranslate"><span class="pre">summary</span></code></a>(*inputs)</p></td> |
| <td><p>Print the summary of the model’s output and parameters.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.SequentialRNNCell.unroll" title="mxnet.gluon.rnn.SequentialRNNCell.unroll"><code class="xref py py-obj docutils literal notranslate"><span class="pre">unroll</span></code></a>(length, inputs[, begin_state, …])</p></td> |
| <td><p>Unrolls an RNN cell across time steps.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.SequentialRNNCell.zero_grad" title="mxnet.gluon.rnn.SequentialRNNCell.zero_grad"><code class="xref py py-obj docutils literal notranslate"><span class="pre">zero_grad</span></code></a>()</p></td> |
| <td><p>Sets all Parameters’ gradient buffer to 0.</p></td> |
| </tr> |
| </tbody> |
| </table> |
| <p><strong>Attributes</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.rnn.SequentialRNNCell.params" title="mxnet.gluon.rnn.SequentialRNNCell.params"><code class="xref py py-obj docutils literal notranslate"><span class="pre">params</span></code></a></p></td> |
| <td><p>Returns this <code class="xref py py-class docutils literal notranslate"><span class="pre">Block</span></code>’s parameter dictionary (does not include its children’s parameters).</p></td> |
| </tr> |
| </tbody> |
| </table> |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.SequentialRNNCell.add"> |
| <code class="sig-name descname">add</code><span class="sig-paren">(</span><em class="sig-param">cell</em><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/mxnet/gluon/rnn/rnn_cell.html#SequentialRNNCell.add"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.gluon.rnn.SequentialRNNCell.add" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Appends a cell into the stack.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><p><strong>cell</strong> (<a class="reference internal" href="#mxnet.gluon.rnn.RecurrentCell" title="mxnet.gluon.rnn.RecurrentCell"><em>RecurrentCell</em></a>) – The cell to add.</p> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.SequentialRNNCell.apply"> |
| <code class="sig-name descname">apply</code><span class="sig-paren">(</span><em class="sig-param">fn</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.rnn.SequentialRNNCell.apply" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Applies <code class="docutils literal notranslate"><span class="pre">fn</span></code> recursively to every child block as well as self.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><p><strong>fn</strong> (<em>callable</em>) – Function to be applied to each submodule, of form <cite>fn(block)</cite>.</p> |
| </dd> |
| <dt class="field-even">Returns</dt> |
| <dd class="field-even"><p></p> |
| </dd> |
| <dt class="field-odd">Return type</dt> |
| <dd class="field-odd"><p>this block</p> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.SequentialRNNCell.begin_state"> |
| <code class="sig-name descname">begin_state</code><span class="sig-paren">(</span><em class="sig-param">**kwargs</em><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/mxnet/gluon/rnn/rnn_cell.html#SequentialRNNCell.begin_state"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.gluon.rnn.SequentialRNNCell.begin_state" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Initial state for this cell.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>func</strong> (<em>callable</em><em>, </em><em>default symbol.zeros</em>) – <p>Function for creating initial state.</p> |
| <p>For Symbol API, func can be <cite>symbol.zeros</cite>, <cite>symbol.uniform</cite>, |
| <cite>symbol.var etc</cite>. Use <cite>symbol.var</cite> if you want to directly |
| feed input as states.</p> |
| <p>For NDArray API, func can be <cite>ndarray.zeros</cite>, <cite>ndarray.ones</cite>, etc.</p> |
| </p></li> |
| <li><p><strong>batch_size</strong> (<em>int</em><em>, </em><em>default 0</em>) – Only required for NDArray API. Size of the batch (‘N’ in layout) |
| dimension of input.</p></li> |
| <li><p><strong>**kwargs</strong> – Additional keyword arguments passed to func. For example |
| <cite>mean</cite>, <cite>std</cite>, <cite>dtype</cite>, etc.</p></li> |
| </ul> |
| </dd> |
| <dt class="field-even">Returns</dt> |
| <dd class="field-even"><p><strong>states</strong> – Starting states for the first RNN step.</p> |
| </dd> |
| <dt class="field-odd">Return type</dt> |
| <dd class="field-odd"><p>nested list of Symbol</p> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.SequentialRNNCell.cast"> |
| <code class="sig-name descname">cast</code><span class="sig-paren">(</span><em class="sig-param">dtype</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.rnn.SequentialRNNCell.cast" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Cast this Block to use another data type.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><p><strong>dtype</strong> (<em>str</em><em> or </em><em>numpy.dtype</em>) – The new data type.</p> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.SequentialRNNCell.collect_params"> |
| <code class="sig-name descname">collect_params</code><span class="sig-paren">(</span><em class="sig-param">select=None</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.rnn.SequentialRNNCell.collect_params" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Returns a <code class="xref py py-class docutils literal notranslate"><span class="pre">Dict</span></code> containing this <code class="xref py py-class docutils literal notranslate"><span class="pre">Block</span></code> and all of its |
| children’s Parameters(default), also can returns the select <code class="xref py py-class docutils literal notranslate"><span class="pre">Dict</span></code> |
| which match some given regular expressions.</p> |
| <p>For example, collect the specified parameters in [‘conv1.weight’, ‘conv1.bias’, ‘fc.weight’, |
| ‘fc.bias’]:</p> |
| <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">model</span><span class="o">.</span><span class="n">collect_params</span><span class="p">(</span><span class="s1">'conv1.weight|conv1.bias|fc.weight|fc.bias'</span><span class="p">)</span> |
| </pre></div> |
| </div> |
| <p>or collect all parameters whose names end with ‘weight’ or ‘bias’, this can be done |
| using regular expressions:</p> |
| <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">model</span><span class="o">.</span><span class="n">collect_params</span><span class="p">(</span><span class="s1">'.*weight|.*bias'</span><span class="p">)</span> |
| </pre></div> |
| </div> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><p><strong>select</strong> (<em>str</em>) – regular expressions</p> |
| </dd> |
| <dt class="field-even">Returns</dt> |
| <dd class="field-even"><p></p> |
| </dd> |
| <dt class="field-odd">Return type</dt> |
| <dd class="field-odd"><p>The selected <code class="xref py py-class docutils literal notranslate"><span class="pre">Dict</span></code></p> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.SequentialRNNCell.forward"> |
| <code class="sig-name descname">forward</code><span class="sig-paren">(</span><em class="sig-param">*args</em>, <em class="sig-param">**kwargs</em><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/mxnet/gluon/rnn/rnn_cell.html#SequentialRNNCell.forward"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.gluon.rnn.SequentialRNNCell.forward" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Unrolls the recurrent cell for one time step.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>inputs</strong> (<em>sym.Variable</em>) – Input symbol, 2D, of shape (batch_size * num_units).</p></li> |
| <li><p><strong>states</strong> (<em>list of sym.Variable</em>) – RNN state from previous step or the output of begin_state().</p></li> |
| </ul> |
| </dd> |
| <dt class="field-even">Returns</dt> |
| <dd class="field-even"><p><ul class="simple"> |
| <li><p><strong>output</strong> (<em>Symbol</em>) – Symbol corresponding to the output from the RNN when unrolling |
| for a single time step.</p></li> |
| <li><p><strong>states</strong> (<em>list of Symbol</em>) – The new state of this RNN after this unrolling. |
| The type of this symbol is same as the output of <cite>begin_state()</cite>. |
| This can be used as an input state to the next time step |
| of this RNN.</p></li> |
| </ul> |
| </p> |
| </dd> |
| </dl> |
| <div class="admonition seealso"> |
| <p class="admonition-title">See also</p> |
| <dl class="simple"> |
| <dt><a class="reference internal" href="#mxnet.gluon.rnn.SequentialRNNCell.begin_state" title="mxnet.gluon.rnn.SequentialRNNCell.begin_state"><code class="xref py py-meth docutils literal notranslate"><span class="pre">begin_state()</span></code></a></dt><dd><p>This function can provide the states for the first time step.</p> |
| </dd> |
| <dt><a class="reference internal" href="#mxnet.gluon.rnn.SequentialRNNCell.unroll" title="mxnet.gluon.rnn.SequentialRNNCell.unroll"><code class="xref py py-meth docutils literal notranslate"><span class="pre">unroll()</span></code></a></dt><dd><p>This function unrolls an RNN for a given number of (>=1) time steps.</p> |
| </dd> |
| </dl> |
| </div> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.SequentialRNNCell.hybridize"> |
| <code class="sig-name descname">hybridize</code><span class="sig-paren">(</span><em class="sig-param">active=True</em>, <em class="sig-param">**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.rnn.SequentialRNNCell.hybridize" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Please refer description of HybridBlock hybridize().</p> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.SequentialRNNCell.initialize"> |
| <code class="sig-name descname">initialize</code><span class="sig-paren">(</span><em class="sig-param">init=<mxnet.initializer.Uniform object></em>, <em class="sig-param">device=None</em>, <em class="sig-param">verbose=False</em>, <em class="sig-param">force_reinit=False</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.rnn.SequentialRNNCell.initialize" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Initializes <code class="xref py py-class docutils literal notranslate"><span class="pre">Parameter</span></code> s of this <code class="xref py py-class docutils literal notranslate"><span class="pre">Block</span></code> and its children.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>init</strong> (<a class="reference internal" href="../../initializer/index.html#mxnet.initializer.Initializer" title="mxnet.initializer.Initializer"><em>Initializer</em></a>) – Global default Initializer to be used when <code class="xref py py-meth docutils literal notranslate"><span class="pre">Parameter.init()</span></code> is <code class="docutils literal notranslate"><span class="pre">None</span></code>. |
| Otherwise, <code class="xref py py-meth docutils literal notranslate"><span class="pre">Parameter.init()</span></code> takes precedence.</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>) – Keeps a copy of Parameters on one or many device(s).</p></li> |
| <li><p><strong>verbose</strong> (<em>bool</em><em>, </em><em>default False</em>) – Whether to verbosely print out details on initialization.</p></li> |
| <li><p><strong>force_reinit</strong> (<em>bool</em><em>, </em><em>default False</em>) – Whether to force re-initialization if parameter is already initialized.</p></li> |
| </ul> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.SequentialRNNCell.load"> |
| <code class="sig-name descname">load</code><span class="sig-paren">(</span><em class="sig-param">prefix</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.rnn.SequentialRNNCell.load" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Load a model saved using the <cite>save</cite> API</p> |
| <p>Reconfigures a model using the saved configuration. This function |
| does not regenerate the model architecture. It resets each Block’s |
| parameter UUIDs as they were when saved in order to match the names of the |
| saved parameters.</p> |
| <p>This function assumes the Blocks in the model were created in the same |
| order they were when the model was saved. This is because each Block is |
| uniquely identified by Block class name and a unique ID in order (since |
| its an OrderedDict) and uses the unique ID to denote that specific Block.</p> |
| <p>Assumes that the model is created in an identical order every time. |
| If the model is not able to be recreated deterministically do not |
| use this set of APIs to save/load your model.</p> |
| <p>For HybridBlocks, the cached_graph (Symbol & inputs) and settings are |
| restored if it had been hybridized before saving.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><p><strong>prefix</strong> (<em>str</em>) – The prefix to use in filenames for loading this model: |
| <prefix>-model.json and <prefix>-model.params</p> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.SequentialRNNCell.load_dict"> |
| <code class="sig-name descname">load_dict</code><span class="sig-paren">(</span><em class="sig-param">param_dict</em>, <em class="sig-param">device=None</em>, <em class="sig-param">allow_missing=False</em>, <em class="sig-param">ignore_extra=False</em>, <em class="sig-param">cast_dtype=False</em>, <em class="sig-param">dtype_source='current'</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.rnn.SequentialRNNCell.load_dict" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Load parameters from dict</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>param_dict</strong> (<em>dict</em>) – Dictionary containing model 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>, </em><em>optional</em>) – Device context on which the memory is allocated. Default is |
| <cite>mxnet.device.current_device()</cite>.</p></li> |
| <li><p><strong>allow_missing</strong> (<em>bool</em><em>, </em><em>default False</em>) – Whether to silently skip loading parameters not represented in the file.</p></li> |
| <li><p><strong>ignore_extra</strong> (<em>bool</em><em>, </em><em>default False</em>) – Whether to silently ignore parameters from the file that are not |
| present in this dict.</p></li> |
| <li><p><strong>cast_dtype</strong> (<em>bool</em><em>, </em><em>default False</em>) – Cast the data type of the NDArray loaded from the checkpoint to the dtype |
| provided by the Parameter if any</p></li> |
| <li><p><strong>dtype_source</strong> (<em>str</em><em>, </em><em>default 'current'</em>) – must be in {‘current’, ‘saved’} |
| Only valid if cast_dtype=True, specify the source of the dtype for casting |
| the parameters</p></li> |
| </ul> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.SequentialRNNCell.load_parameters"> |
| <code class="sig-name descname">load_parameters</code><span class="sig-paren">(</span><em class="sig-param">filename</em>, <em class="sig-param">device=None</em>, <em class="sig-param">allow_missing=False</em>, <em class="sig-param">ignore_extra=False</em>, <em class="sig-param">cast_dtype=False</em>, <em class="sig-param">dtype_source='current'</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.rnn.SequentialRNNCell.load_parameters" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Load parameters from file previously saved by <cite>save_parameters</cite>.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>filename</strong> (<em>str</em>) – Path to parameter file.</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><em>, </em><em>default cpu</em><em>(</em><em>)</em>) – Device(s) to initialize loaded parameters on.</p></li> |
| <li><p><strong>allow_missing</strong> (<em>bool</em><em>, </em><em>default False</em>) – Whether to silently skip loading parameters not represents in the file.</p></li> |
| <li><p><strong>ignore_extra</strong> (<em>bool</em><em>, </em><em>default False</em>) – Whether to silently ignore parameters from the file that are not |
| present in this Block.</p></li> |
| <li><p><strong>cast_dtype</strong> (<em>bool</em><em>, </em><em>default False</em>) – Cast the data type of the NDArray loaded from the checkpoint to the dtype |
| provided by the Parameter if any.</p></li> |
| <li><p><strong>dtype_source</strong> (<em>str</em><em>, </em><em>default 'current'</em>) – must be in {‘current’, ‘saved’} |
| Only valid if cast_dtype=True, specify the source of the dtype for casting |
| the parameters</p></li> |
| </ul> |
| </dd> |
| </dl> |
| <p class="rubric">References</p> |
| <p><a class="reference external" href="https://mxnet.apache.org/api/python/docs/tutorials/packages/gluon/blocks/save_load_params.html">Saving and Loading Gluon Models</a></p> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.SequentialRNNCell.params"> |
| <em class="property">property </em><code class="sig-name descname">params</code><a class="headerlink" href="#mxnet.gluon.rnn.SequentialRNNCell.params" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Returns this <code class="xref py py-class docutils literal notranslate"><span class="pre">Block</span></code>’s parameter dictionary (does not include its |
| children’s parameters).</p> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.SequentialRNNCell.register_child"> |
| <code class="sig-name descname">register_child</code><span class="sig-paren">(</span><em class="sig-param">block</em>, <em class="sig-param">name=None</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.rnn.SequentialRNNCell.register_child" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Registers block as a child of self. <code class="xref py py-class docutils literal notranslate"><span class="pre">Block</span></code> s assigned to self as |
| attributes will be registered automatically.</p> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.SequentialRNNCell.register_forward_hook"> |
| <code class="sig-name descname">register_forward_hook</code><span class="sig-paren">(</span><em class="sig-param">hook</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.rnn.SequentialRNNCell.register_forward_hook" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Registers a forward hook on the block.</p> |
| <p>The hook function is called immediately after <a class="reference internal" href="#mxnet.gluon.rnn.SequentialRNNCell.forward" title="mxnet.gluon.rnn.SequentialRNNCell.forward"><code class="xref py py-func docutils literal notranslate"><span class="pre">forward()</span></code></a>. |
| It should not modify the input or output.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><p><strong>hook</strong> (<em>callable</em>) – The forward hook function of form <cite>hook(block, input, output) -> None</cite>.</p> |
| </dd> |
| <dt class="field-even">Returns</dt> |
| <dd class="field-even"><p></p> |
| </dd> |
| <dt class="field-odd">Return type</dt> |
| <dd class="field-odd"><p><code class="xref py py-class docutils literal notranslate"><span class="pre">mxnet.gluon.utils.HookHandle</span></code></p> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.SequentialRNNCell.register_forward_pre_hook"> |
| <code class="sig-name descname">register_forward_pre_hook</code><span class="sig-paren">(</span><em class="sig-param">hook</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.rnn.SequentialRNNCell.register_forward_pre_hook" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Registers a forward pre-hook on the block.</p> |
| <p>The hook function is called immediately before <a class="reference internal" href="#mxnet.gluon.rnn.SequentialRNNCell.forward" title="mxnet.gluon.rnn.SequentialRNNCell.forward"><code class="xref py py-func docutils literal notranslate"><span class="pre">forward()</span></code></a>. |
| It should not modify the input or output.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><p><strong>hook</strong> (<em>callable</em>) – The forward hook function of form <cite>hook(block, input) -> None</cite>.</p> |
| </dd> |
| <dt class="field-even">Returns</dt> |
| <dd class="field-even"><p></p> |
| </dd> |
| <dt class="field-odd">Return type</dt> |
| <dd class="field-odd"><p><code class="xref py py-class docutils literal notranslate"><span class="pre">mxnet.gluon.utils.HookHandle</span></code></p> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.SequentialRNNCell.register_op_hook"> |
| <code class="sig-name descname">register_op_hook</code><span class="sig-paren">(</span><em class="sig-param">callback</em>, <em class="sig-param">monitor_all=False</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.rnn.SequentialRNNCell.register_op_hook" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Install callback monitor.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>callback</strong> (<em>function</em>) – Function called to inspect the values of the intermediate outputs |
| of blocks after hybridization. It takes 3 parameters: |
| name of the tensor being inspected (str) |
| name of the operator producing or consuming that tensor (str) |
| tensor being inspected (NDArray).</p></li> |
| <li><p><strong>monitor_all</strong> (<em>bool</em><em>, </em><em>default False</em>) – If True, monitor both input and output, otherwise monitor output only.</p></li> |
| </ul> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.SequentialRNNCell.reset"> |
| <code class="sig-name descname">reset</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.rnn.SequentialRNNCell.reset" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Reset before re-using the cell for another graph.</p> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.SequentialRNNCell.reset_ctx"> |
| <code class="sig-name descname">reset_ctx</code><span class="sig-paren">(</span><em class="sig-param">ctx</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.rnn.SequentialRNNCell.reset_ctx" title="Permalink to this definition">¶</a></dt> |
| <dd><p>This function has been deprecated. Please refer to <code class="docutils literal notranslate"><span class="pre">Block.reset_device</span></code>.</p> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.SequentialRNNCell.reset_device"> |
| <code class="sig-name descname">reset_device</code><span class="sig-paren">(</span><em class="sig-param">device</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.rnn.SequentialRNNCell.reset_device" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Re-assign all Parameters to other devices.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><p><strong>device</strong> (Device or list of Device, default <code class="xref py py-meth docutils literal notranslate"><span class="pre">device.current_device()</span></code>.) – Assign Parameter to given device. If device is a list of Device, a |
| copy will be made for each device.</p> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.SequentialRNNCell.save"> |
| <code class="sig-name descname">save</code><span class="sig-paren">(</span><em class="sig-param">prefix</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.rnn.SequentialRNNCell.save" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Save the model architecture and parameters to load again later</p> |
| <p>Saves the model architecture as a nested dictionary where each Block |
| in the model is a dictionary and its children are sub-dictionaries.</p> |
| <p>Each Block is uniquely identified by Block class name and a unique ID. |
| We save each Block’s parameter UUID to restore later in order to match |
| the saved parameters.</p> |
| <p>Recursively traverses a Block’s children in order (since its an |
| OrderedDict) and uses the unique ID to denote that specific Block.</p> |
| <p>Assumes that the model is created in an identical order every time. |
| If the model is not able to be recreated deterministically do not |
| use this set of APIs to save/load your model.</p> |
| <p>For HybridBlocks, the cached_graph is saved (Symbol & inputs) if |
| it has already been hybridized.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><p><strong>prefix</strong> (<em>str</em>) – The prefix to use in filenames for saving this model: |
| <prefix>-model.json and <prefix>-model.params</p> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.SequentialRNNCell.save_parameters"> |
| <code class="sig-name descname">save_parameters</code><span class="sig-paren">(</span><em class="sig-param">filename</em>, <em class="sig-param">deduplicate=False</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.rnn.SequentialRNNCell.save_parameters" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Save parameters to file.</p> |
| <p>Saved parameters can only be loaded with <cite>load_parameters</cite>. Note that this |
| method only saves parameters, not model structure. If you want to save |
| model structures, please use <code class="xref py py-meth docutils literal notranslate"><span class="pre">HybridBlock.export()</span></code>.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>filename</strong> (<em>str</em>) – Path to file.</p></li> |
| <li><p><strong>deduplicate</strong> (<em>bool</em><em>, </em><em>default False</em>) – If True, save shared parameters only once. Otherwise, if a Block |
| contains multiple sub-blocks that share parameters, each of the |
| shared parameters will be separately saved for every sub-block.</p></li> |
| </ul> |
| </dd> |
| </dl> |
| <p class="rubric">References</p> |
| <p><a class="reference external" href="https://mxnet.apache.org/api/python/docs/tutorials/packages/gluon/blocks/save_load_params.html">Saving and Loading Gluon Models</a></p> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.SequentialRNNCell.setattr"> |
| <code class="sig-name descname">setattr</code><span class="sig-paren">(</span><em class="sig-param">name</em>, <em class="sig-param">value</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.rnn.SequentialRNNCell.setattr" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Set an attribute to a new value for all Parameters.</p> |
| <p>For example, set grad_req to null if you don’t need gradient w.r.t a |
| model’s Parameters:</p> |
| <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">model</span><span class="o">.</span><span class="n">setattr</span><span class="p">(</span><span class="s1">'grad_req'</span><span class="p">,</span> <span class="s1">'null'</span><span class="p">)</span> |
| </pre></div> |
| </div> |
| <p>or change the learning rate multiplier:</p> |
| <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">model</span><span class="o">.</span><span class="n">setattr</span><span class="p">(</span><span class="s1">'lr_mult'</span><span class="p">,</span> <span class="mf">0.5</span><span class="p">)</span> |
| </pre></div> |
| </div> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>name</strong> (<em>str</em>) – Name of the attribute.</p></li> |
| <li><p><strong>value</strong> (<em>valid type for attribute name</em>) – The new value for the attribute.</p></li> |
| </ul> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.SequentialRNNCell.share_parameters"> |
| <code class="sig-name descname">share_parameters</code><span class="sig-paren">(</span><em class="sig-param">shared</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.rnn.SequentialRNNCell.share_parameters" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Share parameters recursively inside the model.</p> |
| <p>For example, if you want <code class="docutils literal notranslate"><span class="pre">dense1</span></code> to share <code class="docutils literal notranslate"><span class="pre">dense0</span></code>’s weights, you can do:</p> |
| <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">dense0</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Dense</span><span class="p">(</span><span class="mi">20</span><span class="p">)</span> |
| <span class="n">dense1</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Dense</span><span class="p">(</span><span class="mi">20</span><span class="p">)</span> |
| <span class="n">dense1</span><span class="o">.</span><span class="n">share_parameters</span><span class="p">(</span><span class="n">dense0</span><span class="o">.</span><span class="n">collect_params</span><span class="p">())</span> |
| </pre></div> |
| </div> |
| <dl class="simple"> |
| <dt>which equals to</dt><dd><p>dense1.weight = dense0.weight |
| dense1.bias = dense0.bias</p> |
| </dd> |
| </dl> |
| <p>Note that unlike the <cite>load_parameters</cite> or <cite>load_dict</cite> functions, |
| <cite>share_parameters</cite> results in the <cite>Parameter</cite> object being shared (or |
| tied) between the models, whereas <cite>load_parameters</cite> or <cite>load_dict</cite> only |
| set the value of the data dictionary of a model. If you call |
| <cite>load_parameters</cite> or <cite>load_dict</cite> after <cite>share_parameters</cite>, the loaded |
| value will be reflected in all networks that use the shared (or tied) |
| <cite>Parameter</cite> object.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><p><strong>shared</strong> (<em>Dict</em>) – Dict of the shared parameters.</p> |
| </dd> |
| <dt class="field-even">Returns</dt> |
| <dd class="field-even"><p></p> |
| </dd> |
| <dt class="field-odd">Return type</dt> |
| <dd class="field-odd"><p>this block</p> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.SequentialRNNCell.state_info"> |
| <code class="sig-name descname">state_info</code><span class="sig-paren">(</span><em class="sig-param">batch_size=0</em><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/mxnet/gluon/rnn/rnn_cell.html#SequentialRNNCell.state_info"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.gluon.rnn.SequentialRNNCell.state_info" title="Permalink to this definition">¶</a></dt> |
| <dd><p>shape and layout information of states</p> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.SequentialRNNCell.summary"> |
| <code class="sig-name descname">summary</code><span class="sig-paren">(</span><em class="sig-param">*inputs</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.rnn.SequentialRNNCell.summary" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Print the summary of the model’s output and parameters.</p> |
| <p>The network must have been initialized, and must not have been hybridized.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><p><strong>inputs</strong> (<em>object</em>) – Any input that the model supports. For any tensor in the input, only |
| <a class="reference internal" href="../../legacy/ndarray/ndarray.html#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><code class="xref py py-class docutils literal notranslate"><span class="pre">mxnet.ndarray.NDArray</span></code></a> is supported.</p> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.SequentialRNNCell.unroll"> |
| <code class="sig-name descname">unroll</code><span class="sig-paren">(</span><em class="sig-param">length</em>, <em class="sig-param">inputs</em>, <em class="sig-param">begin_state=None</em>, <em class="sig-param">layout='NTC'</em>, <em class="sig-param">merge_outputs=None</em>, <em class="sig-param">valid_length=None</em><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/mxnet/gluon/rnn/rnn_cell.html#SequentialRNNCell.unroll"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.gluon.rnn.SequentialRNNCell.unroll" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Unrolls an RNN cell across time steps.</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>) – Number of steps to unroll.</p></li> |
| <li><p><strong>inputs</strong> (<a class="reference internal" href="../../legacy/symbol/symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a><em>, </em><em>list of Symbol</em><em>, or </em><em>None</em>) – <p>If <cite>inputs</cite> is a single Symbol (usually the output |
| of Embedding symbol), it should have shape |
| (batch_size, length, …) if <cite>layout</cite> is ‘NTC’, |
| or (length, batch_size, …) if <cite>layout</cite> is ‘TNC’.</p> |
| <p>If <cite>inputs</cite> is a list of symbols (usually output of |
| previous unroll), they should all have shape |
| (batch_size, …).</p> |
| </p></li> |
| <li><p><strong>begin_state</strong> (<em>nested list of Symbol</em><em>, </em><em>optional</em>) – Input states created by <cite>begin_state()</cite> |
| or output state of another cell. |
| Created from <cite>begin_state()</cite> if <cite>None</cite>.</p></li> |
| <li><p><strong>layout</strong> (<em>str</em><em>, </em><em>optional</em>) – <cite>layout</cite> of input symbol. Only used if inputs |
| is a single Symbol.</p></li> |
| <li><p><strong>merge_outputs</strong> (<em>bool</em><em>, </em><em>optional</em>) – If <cite>False</cite>, returns outputs as a list of Symbols. |
| If <cite>True</cite>, concatenates output across time steps |
| and returns a single symbol with shape |
| (batch_size, length, …) if layout is ‘NTC’, |
| or (length, batch_size, …) if layout is ‘TNC’. |
| If <cite>None</cite>, output whatever is faster.</p></li> |
| <li><p><strong>valid_length</strong> (<a class="reference internal" href="../../legacy/symbol/symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a><em>, </em><a class="reference internal" href="../../legacy/ndarray/ndarray.html#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a><em> or </em><em>None</em>) – <cite>valid_length</cite> specifies the length of the sequences in the batch without padding. |
| This option is especially useful for building sequence-to-sequence models where |
| the input and output sequences would potentially be padded. |
| If <cite>valid_length</cite> is None, all sequences are assumed to have the same length. |
| If <cite>valid_length</cite> is a Symbol or NDArray, it should have shape (batch_size,). |
| The ith element will be the length of the ith sequence in the batch. |
| The last valid state will be return and the padded outputs will be masked with 0. |
| Note that <cite>valid_length</cite> must be smaller or equal to <cite>length</cite>.</p></li> |
| </ul> |
| </dd> |
| <dt class="field-even">Returns</dt> |
| <dd class="field-even"><p><ul class="simple"> |
| <li><p><strong>outputs</strong> (<em>list of Symbol or Symbol</em>) – Symbol (if <cite>merge_outputs</cite> is True) or list of Symbols |
| (if <cite>merge_outputs</cite> is False) corresponding to the output from |
| the RNN from this unrolling.</p></li> |
| <li><p><strong>states</strong> (<em>list of Symbol</em>) – The new state of this RNN after this unrolling. |
| The type of this symbol is same as the output of <cite>begin_state()</cite>.</p></li> |
| </ul> |
| </p> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.SequentialRNNCell.zero_grad"> |
| <code class="sig-name descname">zero_grad</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.rnn.SequentialRNNCell.zero_grad" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Sets all Parameters’ gradient buffer to 0.</p> |
| </dd></dl> |
| |
| </dd></dl> |
| |
| <dl class="class"> |
| <dt id="mxnet.gluon.rnn.VariationalDropoutCell"> |
| <em class="property">class </em><code class="sig-name descname">VariationalDropoutCell</code><span class="sig-paren">(</span><em class="sig-param">base_cell</em>, <em class="sig-param">drop_inputs=0.0</em>, <em class="sig-param">drop_states=0.0</em>, <em class="sig-param">drop_outputs=0.0</em><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/mxnet/gluon/rnn/rnn_cell.html#VariationalDropoutCell"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.gluon.rnn.VariationalDropoutCell" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">mxnet.gluon.rnn.rnn_cell.ModifierCell</span></code></p> |
| <p>Applies Variational Dropout on base cell. |
| <a class="reference external" href="https://arxiv.org/pdf/1512.05287.pdf">https://arxiv.org/pdf/1512.05287.pdf</a></p> |
| <p>Variational dropout uses the same dropout mask across time-steps. It can be applied to RNN |
| inputs, outputs, and states. The masks for them are not shared.</p> |
| <p>The dropout mask is initialized when stepping forward for the first time and will remain |
| the same until .reset() is called. Thus, if using the cell and stepping manually without calling |
| .unroll(), the .reset() should be called after each sequence.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>base_cell</strong> (<a class="reference internal" href="#mxnet.gluon.rnn.RecurrentCell" title="mxnet.gluon.rnn.RecurrentCell"><em>RecurrentCell</em></a>) – The cell on which to perform variational dropout.</p></li> |
| <li><p><strong>drop_inputs</strong> (<em>float</em><em>, </em><em>default 0.</em>) – The dropout rate for inputs. Won’t apply dropout if it equals 0.</p></li> |
| <li><p><strong>drop_states</strong> (<em>float</em><em>, </em><em>default 0.</em>) – The dropout rate for state inputs on the first state channel. |
| Won’t apply dropout if it equals 0.</p></li> |
| <li><p><strong>drop_outputs</strong> (<em>float</em><em>, </em><em>default 0.</em>) – The dropout rate for outputs. Won’t apply dropout if it equals 0.</p></li> |
| </ul> |
| </dd> |
| </dl> |
| <p><strong>Methods</strong></p> |
| <table class="longtable docutils align-default"> |
| <colgroup> |
| <col style="width: 10%" /> |
| <col style="width: 90%" /> |
| </colgroup> |
| <tbody> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.VariationalDropoutCell.apply" title="mxnet.gluon.rnn.VariationalDropoutCell.apply"><code class="xref py py-obj docutils literal notranslate"><span class="pre">apply</span></code></a>(fn)</p></td> |
| <td><p>Applies <code class="docutils literal notranslate"><span class="pre">fn</span></code> recursively to every child block as well as self.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.VariationalDropoutCell.cast" title="mxnet.gluon.rnn.VariationalDropoutCell.cast"><code class="xref py py-obj docutils literal notranslate"><span class="pre">cast</span></code></a>(dtype)</p></td> |
| <td><p>Cast this Block to use another data type.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.VariationalDropoutCell.collect_params" title="mxnet.gluon.rnn.VariationalDropoutCell.collect_params"><code class="xref py py-obj docutils literal notranslate"><span class="pre">collect_params</span></code></a>([select])</p></td> |
| <td><p>Returns a <code class="xref py py-class docutils literal notranslate"><span class="pre">Dict</span></code> containing this <code class="xref py py-class docutils literal notranslate"><span class="pre">Block</span></code> and all of its children’s Parameters(default), also can returns the select <code class="xref py py-class docutils literal notranslate"><span class="pre">Dict</span></code> which match some given regular expressions.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.VariationalDropoutCell.export" title="mxnet.gluon.rnn.VariationalDropoutCell.export"><code class="xref py py-obj docutils literal notranslate"><span class="pre">export</span></code></a>(path[, epoch, remove_amp_cast])</p></td> |
| <td><p>Export HybridBlock to json format that can be loaded by <cite>gluon.SymbolBlock.imports</cite> or the C++ interface.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.VariationalDropoutCell.forward" title="mxnet.gluon.rnn.VariationalDropoutCell.forward"><code class="xref py py-obj docutils literal notranslate"><span class="pre">forward</span></code></a>(inputs, states)</p></td> |
| <td><p>Unrolls the recurrent cell for one time step.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.VariationalDropoutCell.hybridize" title="mxnet.gluon.rnn.VariationalDropoutCell.hybridize"><code class="xref py py-obj docutils literal notranslate"><span class="pre">hybridize</span></code></a>([active])</p></td> |
| <td><p>Please refer description of HybridBlock hybridize().</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.VariationalDropoutCell.infer_shape" title="mxnet.gluon.rnn.VariationalDropoutCell.infer_shape"><code class="xref py py-obj docutils literal notranslate"><span class="pre">infer_shape</span></code></a>(i, x, is_bidirect)</p></td> |
| <td><p>Infers shape of Parameters from inputs.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.VariationalDropoutCell.infer_type" title="mxnet.gluon.rnn.VariationalDropoutCell.infer_type"><code class="xref py py-obj docutils literal notranslate"><span class="pre">infer_type</span></code></a>(*args)</p></td> |
| <td><p>Infers data type of Parameters from inputs.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.VariationalDropoutCell.initialize" title="mxnet.gluon.rnn.VariationalDropoutCell.initialize"><code class="xref py py-obj docutils literal notranslate"><span class="pre">initialize</span></code></a>([init, device, verbose, force_reinit])</p></td> |
| <td><p>Initializes <code class="xref py py-class docutils literal notranslate"><span class="pre">Parameter</span></code> s of this <code class="xref py py-class docutils literal notranslate"><span class="pre">Block</span></code> and its children.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.VariationalDropoutCell.load" title="mxnet.gluon.rnn.VariationalDropoutCell.load"><code class="xref py py-obj docutils literal notranslate"><span class="pre">load</span></code></a>(prefix)</p></td> |
| <td><p>Load a model saved using the <cite>save</cite> API</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.VariationalDropoutCell.load_dict" title="mxnet.gluon.rnn.VariationalDropoutCell.load_dict"><code class="xref py py-obj docutils literal notranslate"><span class="pre">load_dict</span></code></a>(param_dict[, device, …])</p></td> |
| <td><p>Load parameters from dict</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.VariationalDropoutCell.load_parameters" title="mxnet.gluon.rnn.VariationalDropoutCell.load_parameters"><code class="xref py py-obj docutils literal notranslate"><span class="pre">load_parameters</span></code></a>(filename[, device, …])</p></td> |
| <td><p>Load parameters from file previously saved by <cite>save_parameters</cite>.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.VariationalDropoutCell.optimize_for" title="mxnet.gluon.rnn.VariationalDropoutCell.optimize_for"><code class="xref py py-obj docutils literal notranslate"><span class="pre">optimize_for</span></code></a>(x, *args[, backend, clear, …])</p></td> |
| <td><p>Partitions the current HybridBlock and optimizes it for a given backend without executing a forward pass.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.VariationalDropoutCell.register_child" title="mxnet.gluon.rnn.VariationalDropoutCell.register_child"><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_child</span></code></a>(block[, name])</p></td> |
| <td><p>Registers block as a child of self.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.VariationalDropoutCell.register_forward_hook" title="mxnet.gluon.rnn.VariationalDropoutCell.register_forward_hook"><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_forward_hook</span></code></a>(hook)</p></td> |
| <td><p>Registers a forward hook on the block.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.VariationalDropoutCell.register_forward_pre_hook" title="mxnet.gluon.rnn.VariationalDropoutCell.register_forward_pre_hook"><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_forward_pre_hook</span></code></a>(hook)</p></td> |
| <td><p>Registers a forward pre-hook on the block.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.VariationalDropoutCell.register_op_hook" title="mxnet.gluon.rnn.VariationalDropoutCell.register_op_hook"><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_op_hook</span></code></a>(callback[, monitor_all])</p></td> |
| <td><p>Install callback monitor.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.VariationalDropoutCell.reset" title="mxnet.gluon.rnn.VariationalDropoutCell.reset"><code class="xref py py-obj docutils literal notranslate"><span class="pre">reset</span></code></a>()</p></td> |
| <td><p>Reset before re-using the cell for another graph.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.VariationalDropoutCell.reset_ctx" title="mxnet.gluon.rnn.VariationalDropoutCell.reset_ctx"><code class="xref py py-obj docutils literal notranslate"><span class="pre">reset_ctx</span></code></a>(ctx)</p></td> |
| <td><p>This function has been deprecated.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.VariationalDropoutCell.reset_device" title="mxnet.gluon.rnn.VariationalDropoutCell.reset_device"><code class="xref py py-obj docutils literal notranslate"><span class="pre">reset_device</span></code></a>(device)</p></td> |
| <td><p>Re-assign all Parameters to other devices.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.VariationalDropoutCell.save" title="mxnet.gluon.rnn.VariationalDropoutCell.save"><code class="xref py py-obj docutils literal notranslate"><span class="pre">save</span></code></a>(prefix)</p></td> |
| <td><p>Save the model architecture and parameters to load again later</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.VariationalDropoutCell.save_parameters" title="mxnet.gluon.rnn.VariationalDropoutCell.save_parameters"><code class="xref py py-obj docutils literal notranslate"><span class="pre">save_parameters</span></code></a>(filename[, deduplicate])</p></td> |
| <td><p>Save parameters to file.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.VariationalDropoutCell.setattr" title="mxnet.gluon.rnn.VariationalDropoutCell.setattr"><code class="xref py py-obj docutils literal notranslate"><span class="pre">setattr</span></code></a>(name, value)</p></td> |
| <td><p>Set an attribute to a new value for all Parameters.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.VariationalDropoutCell.share_parameters" title="mxnet.gluon.rnn.VariationalDropoutCell.share_parameters"><code class="xref py py-obj docutils literal notranslate"><span class="pre">share_parameters</span></code></a>(shared)</p></td> |
| <td><p>Share parameters recursively inside the model.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.VariationalDropoutCell.summary" title="mxnet.gluon.rnn.VariationalDropoutCell.summary"><code class="xref py py-obj docutils literal notranslate"><span class="pre">summary</span></code></a>(*inputs)</p></td> |
| <td><p>Print the summary of the model’s output and parameters.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.VariationalDropoutCell.unroll" title="mxnet.gluon.rnn.VariationalDropoutCell.unroll"><code class="xref py py-obj docutils literal notranslate"><span class="pre">unroll</span></code></a>(length, inputs[, begin_state, …])</p></td> |
| <td><p>Unrolls an RNN cell across time steps.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.VariationalDropoutCell.zero_grad" title="mxnet.gluon.rnn.VariationalDropoutCell.zero_grad"><code class="xref py py-obj docutils literal notranslate"><span class="pre">zero_grad</span></code></a>()</p></td> |
| <td><p>Sets all Parameters’ gradient buffer to 0.</p></td> |
| </tr> |
| </tbody> |
| </table> |
| <p><strong>Attributes</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.rnn.VariationalDropoutCell.params" title="mxnet.gluon.rnn.VariationalDropoutCell.params"><code class="xref py py-obj docutils literal notranslate"><span class="pre">params</span></code></a></p></td> |
| <td><p>Return an attribute of instance, which is of type owner.</p></td> |
| </tr> |
| </tbody> |
| </table> |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.VariationalDropoutCell.apply"> |
| <code class="sig-name descname">apply</code><span class="sig-paren">(</span><em class="sig-param">fn</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.rnn.VariationalDropoutCell.apply" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Applies <code class="docutils literal notranslate"><span class="pre">fn</span></code> recursively to every child block as well as self.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><p><strong>fn</strong> (<em>callable</em>) – Function to be applied to each submodule, of form <cite>fn(block)</cite>.</p> |
| </dd> |
| <dt class="field-even">Returns</dt> |
| <dd class="field-even"><p></p> |
| </dd> |
| <dt class="field-odd">Return type</dt> |
| <dd class="field-odd"><p>this block</p> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.VariationalDropoutCell.cast"> |
| <code class="sig-name descname">cast</code><span class="sig-paren">(</span><em class="sig-param">dtype</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.rnn.VariationalDropoutCell.cast" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Cast this Block to use another data type.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><p><strong>dtype</strong> (<em>str</em><em> or </em><em>numpy.dtype</em>) – The new data type.</p> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.VariationalDropoutCell.collect_params"> |
| <code class="sig-name descname">collect_params</code><span class="sig-paren">(</span><em class="sig-param">select=None</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.rnn.VariationalDropoutCell.collect_params" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Returns a <code class="xref py py-class docutils literal notranslate"><span class="pre">Dict</span></code> containing this <code class="xref py py-class docutils literal notranslate"><span class="pre">Block</span></code> and all of its |
| children’s Parameters(default), also can returns the select <code class="xref py py-class docutils literal notranslate"><span class="pre">Dict</span></code> |
| which match some given regular expressions.</p> |
| <p>For example, collect the specified parameters in [‘conv1.weight’, ‘conv1.bias’, ‘fc.weight’, |
| ‘fc.bias’]:</p> |
| <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">model</span><span class="o">.</span><span class="n">collect_params</span><span class="p">(</span><span class="s1">'conv1.weight|conv1.bias|fc.weight|fc.bias'</span><span class="p">)</span> |
| </pre></div> |
| </div> |
| <p>or collect all parameters whose names end with ‘weight’ or ‘bias’, this can be done |
| using regular expressions:</p> |
| <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">model</span><span class="o">.</span><span class="n">collect_params</span><span class="p">(</span><span class="s1">'.*weight|.*bias'</span><span class="p">)</span> |
| </pre></div> |
| </div> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><p><strong>select</strong> (<em>str</em>) – regular expressions</p> |
| </dd> |
| <dt class="field-even">Returns</dt> |
| <dd class="field-even"><p></p> |
| </dd> |
| <dt class="field-odd">Return type</dt> |
| <dd class="field-odd"><p>The selected <code class="xref py py-class docutils literal notranslate"><span class="pre">Dict</span></code></p> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.VariationalDropoutCell.export"> |
| <code class="sig-name descname">export</code><span class="sig-paren">(</span><em class="sig-param">path</em>, <em class="sig-param">epoch=0</em>, <em class="sig-param">remove_amp_cast=True</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.rnn.VariationalDropoutCell.export" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Export HybridBlock to json format that can be loaded by |
| <cite>gluon.SymbolBlock.imports</cite> or the C++ interface.</p> |
| <div class="admonition note"> |
| <p class="admonition-title">Note</p> |
| <p>When there are only one input, it will have name <cite>data</cite>. When there |
| Are more than one inputs, they will be named as <cite>data0</cite>, <cite>data1</cite>, etc.</p> |
| </div> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>path</strong> (<em>str</em><em> or </em><em>None</em>) – Path to save model. Two files <cite>path-symbol.json</cite> and <cite>path-xxxx.params</cite> |
| will be created, where xxxx is the 4 digits epoch number. |
| If None, do not export to file but return Python Symbol object and |
| corresponding dictionary of parameters.</p></li> |
| <li><p><strong>epoch</strong> (<em>int</em>) – Epoch number of saved model.</p></li> |
| <li><p><strong>remove_amp_cast</strong> (<em>bool</em><em>, </em><em>optional</em>) – Whether to remove the amp_cast and amp_multicast operators, before saving the model.</p></li> |
| </ul> |
| </dd> |
| <dt class="field-even">Returns</dt> |
| <dd class="field-even"><p><ul class="simple"> |
| <li><p><strong>symbol_filename</strong> (<em>str</em>) – Filename to which model symbols were saved, including <cite>path</cite> prefix.</p></li> |
| <li><p><strong>params_filename</strong> (<em>str</em>) – Filename to which model parameters were saved, including <cite>path</cite> prefix.</p></li> |
| </ul> |
| </p> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.VariationalDropoutCell.forward"> |
| <code class="sig-name descname">forward</code><span class="sig-paren">(</span><em class="sig-param">inputs</em>, <em class="sig-param">states</em><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/mxnet/gluon/rnn/rnn_cell.html#VariationalDropoutCell.forward"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.gluon.rnn.VariationalDropoutCell.forward" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Unrolls the recurrent cell for one time step.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>inputs</strong> (<em>sym.Variable</em>) – Input symbol, 2D, of shape (batch_size * num_units).</p></li> |
| <li><p><strong>states</strong> (<em>list of sym.Variable</em>) – RNN state from previous step or the output of begin_state().</p></li> |
| </ul> |
| </dd> |
| <dt class="field-even">Returns</dt> |
| <dd class="field-even"><p><ul class="simple"> |
| <li><p><strong>output</strong> (<em>Symbol</em>) – Symbol corresponding to the output from the RNN when unrolling |
| for a single time step.</p></li> |
| <li><p><strong>states</strong> (<em>list of Symbol</em>) – The new state of this RNN after this unrolling. |
| The type of this symbol is same as the output of <cite>begin_state()</cite>. |
| This can be used as an input state to the next time step |
| of this RNN.</p></li> |
| </ul> |
| </p> |
| </dd> |
| </dl> |
| <div class="admonition seealso"> |
| <p class="admonition-title">See also</p> |
| <dl class="simple"> |
| <dt><code class="xref py py-meth docutils literal notranslate"><span class="pre">begin_state()</span></code></dt><dd><p>This function can provide the states for the first time step.</p> |
| </dd> |
| <dt><a class="reference internal" href="#mxnet.gluon.rnn.VariationalDropoutCell.unroll" title="mxnet.gluon.rnn.VariationalDropoutCell.unroll"><code class="xref py py-meth docutils literal notranslate"><span class="pre">unroll()</span></code></a></dt><dd><p>This function unrolls an RNN for a given number of (>=1) time steps.</p> |
| </dd> |
| </dl> |
| </div> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.VariationalDropoutCell.hybridize"> |
| <code class="sig-name descname">hybridize</code><span class="sig-paren">(</span><em class="sig-param">active=True</em>, <em class="sig-param">**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.rnn.VariationalDropoutCell.hybridize" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Please refer description of HybridBlock hybridize().</p> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.VariationalDropoutCell.infer_shape"> |
| <code class="sig-name descname">infer_shape</code><span class="sig-paren">(</span><em class="sig-param">i</em>, <em class="sig-param">x</em>, <em class="sig-param">is_bidirect</em><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/mxnet/gluon/rnn/rnn_cell.html#VariationalDropoutCell.infer_shape"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.gluon.rnn.VariationalDropoutCell.infer_shape" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Infers shape of Parameters from inputs.</p> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.VariationalDropoutCell.infer_type"> |
| <code class="sig-name descname">infer_type</code><span class="sig-paren">(</span><em class="sig-param">*args</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.rnn.VariationalDropoutCell.infer_type" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Infers data type of Parameters from inputs.</p> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.VariationalDropoutCell.initialize"> |
| <code class="sig-name descname">initialize</code><span class="sig-paren">(</span><em class="sig-param">init=<mxnet.initializer.Uniform object></em>, <em class="sig-param">device=None</em>, <em class="sig-param">verbose=False</em>, <em class="sig-param">force_reinit=False</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.rnn.VariationalDropoutCell.initialize" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Initializes <code class="xref py py-class docutils literal notranslate"><span class="pre">Parameter</span></code> s of this <code class="xref py py-class docutils literal notranslate"><span class="pre">Block</span></code> and its children.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>init</strong> (<a class="reference internal" href="../../initializer/index.html#mxnet.initializer.Initializer" title="mxnet.initializer.Initializer"><em>Initializer</em></a>) – Global default Initializer to be used when <code class="xref py py-meth docutils literal notranslate"><span class="pre">Parameter.init()</span></code> is <code class="docutils literal notranslate"><span class="pre">None</span></code>. |
| Otherwise, <code class="xref py py-meth docutils literal notranslate"><span class="pre">Parameter.init()</span></code> takes precedence.</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>) – Keeps a copy of Parameters on one or many device(s).</p></li> |
| <li><p><strong>verbose</strong> (<em>bool</em><em>, </em><em>default False</em>) – Whether to verbosely print out details on initialization.</p></li> |
| <li><p><strong>force_reinit</strong> (<em>bool</em><em>, </em><em>default False</em>) – Whether to force re-initialization if parameter is already initialized.</p></li> |
| </ul> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.VariationalDropoutCell.load"> |
| <code class="sig-name descname">load</code><span class="sig-paren">(</span><em class="sig-param">prefix</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.rnn.VariationalDropoutCell.load" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Load a model saved using the <cite>save</cite> API</p> |
| <p>Reconfigures a model using the saved configuration. This function |
| does not regenerate the model architecture. It resets each Block’s |
| parameter UUIDs as they were when saved in order to match the names of the |
| saved parameters.</p> |
| <p>This function assumes the Blocks in the model were created in the same |
| order they were when the model was saved. This is because each Block is |
| uniquely identified by Block class name and a unique ID in order (since |
| its an OrderedDict) and uses the unique ID to denote that specific Block.</p> |
| <p>Assumes that the model is created in an identical order every time. |
| If the model is not able to be recreated deterministically do not |
| use this set of APIs to save/load your model.</p> |
| <p>For HybridBlocks, the cached_graph (Symbol & inputs) and settings are |
| restored if it had been hybridized before saving.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><p><strong>prefix</strong> (<em>str</em>) – The prefix to use in filenames for loading this model: |
| <prefix>-model.json and <prefix>-model.params</p> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.VariationalDropoutCell.load_dict"> |
| <code class="sig-name descname">load_dict</code><span class="sig-paren">(</span><em class="sig-param">param_dict</em>, <em class="sig-param">device=None</em>, <em class="sig-param">allow_missing=False</em>, <em class="sig-param">ignore_extra=False</em>, <em class="sig-param">cast_dtype=False</em>, <em class="sig-param">dtype_source='current'</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.rnn.VariationalDropoutCell.load_dict" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Load parameters from dict</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>param_dict</strong> (<em>dict</em>) – Dictionary containing model 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>, </em><em>optional</em>) – Device context on which the memory is allocated. Default is |
| <cite>mxnet.device.current_device()</cite>.</p></li> |
| <li><p><strong>allow_missing</strong> (<em>bool</em><em>, </em><em>default False</em>) – Whether to silently skip loading parameters not represented in the file.</p></li> |
| <li><p><strong>ignore_extra</strong> (<em>bool</em><em>, </em><em>default False</em>) – Whether to silently ignore parameters from the file that are not |
| present in this dict.</p></li> |
| <li><p><strong>cast_dtype</strong> (<em>bool</em><em>, </em><em>default False</em>) – Cast the data type of the NDArray loaded from the checkpoint to the dtype |
| provided by the Parameter if any</p></li> |
| <li><p><strong>dtype_source</strong> (<em>str</em><em>, </em><em>default 'current'</em>) – must be in {‘current’, ‘saved’} |
| Only valid if cast_dtype=True, specify the source of the dtype for casting |
| the parameters</p></li> |
| </ul> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.VariationalDropoutCell.load_parameters"> |
| <code class="sig-name descname">load_parameters</code><span class="sig-paren">(</span><em class="sig-param">filename</em>, <em class="sig-param">device=None</em>, <em class="sig-param">allow_missing=False</em>, <em class="sig-param">ignore_extra=False</em>, <em class="sig-param">cast_dtype=False</em>, <em class="sig-param">dtype_source='current'</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.rnn.VariationalDropoutCell.load_parameters" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Load parameters from file previously saved by <cite>save_parameters</cite>.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>filename</strong> (<em>str</em>) – Path to parameter file.</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><em>, </em><em>default cpu</em><em>(</em><em>)</em>) – Device(s) to initialize loaded parameters on.</p></li> |
| <li><p><strong>allow_missing</strong> (<em>bool</em><em>, </em><em>default False</em>) – Whether to silently skip loading parameters not represents in the file.</p></li> |
| <li><p><strong>ignore_extra</strong> (<em>bool</em><em>, </em><em>default False</em>) – Whether to silently ignore parameters from the file that are not |
| present in this Block.</p></li> |
| <li><p><strong>cast_dtype</strong> (<em>bool</em><em>, </em><em>default False</em>) – Cast the data type of the NDArray loaded from the checkpoint to the dtype |
| provided by the Parameter if any.</p></li> |
| <li><p><strong>dtype_source</strong> (<em>str</em><em>, </em><em>default 'current'</em>) – must be in {‘current’, ‘saved’} |
| Only valid if cast_dtype=True, specify the source of the dtype for casting |
| the parameters</p></li> |
| </ul> |
| </dd> |
| </dl> |
| <p class="rubric">References</p> |
| <p><a class="reference external" href="https://mxnet.apache.org/api/python/docs/tutorials/packages/gluon/blocks/save_load_params.html">Saving and Loading Gluon Models</a></p> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.VariationalDropoutCell.optimize_for"> |
| <code class="sig-name descname">optimize_for</code><span class="sig-paren">(</span><em class="sig-param">x</em>, <em class="sig-param">*args</em>, <em class="sig-param">backend=None</em>, <em class="sig-param">clear=False</em>, <em class="sig-param">partition_if_dynamic=True</em>, <em class="sig-param">static_alloc=False</em>, <em class="sig-param">static_shape=False</em>, <em class="sig-param">inline_limit=2</em>, <em class="sig-param">forward_bulk_size=None</em>, <em class="sig-param">backward_bulk_size=None</em>, <em class="sig-param">**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.rnn.VariationalDropoutCell.optimize_for" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Partitions the current HybridBlock and optimizes it for a given backend |
| without executing a forward pass. Modifies the HybridBlock in-place.</p> |
| <p>Immediately partitions a HybridBlock using the specified backend. Combines |
| the work done in the hybridize API with part of the work done in the forward |
| pass without calling the CachedOp. Can be used in place of hybridize, |
| afterwards <cite>export</cite> can be called or inference can be run. See README.md in |
| example/extensions/lib_subgraph/README.md for more details.</p> |
| <p class="rubric">Examples</p> |
| <p># partition and then export to file |
| block.optimize_for(x, backend=’myPart’) |
| block.export(‘partitioned’)</p> |
| <p># partition and then run inference |
| block.optimize_for(x, backend=’myPart’) |
| block(x)</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>x</strong> (<a class="reference internal" href="../../legacy/ndarray/ndarray.html#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – first input to model</p></li> |
| <li><p><strong>*args</strong> (<a class="reference internal" href="../../legacy/ndarray/ndarray.html#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – other inputs to model</p></li> |
| <li><p><strong>backend</strong> (<em>str</em>) – The name of backend, as registered in <cite>SubgraphBackendRegistry</cite>, default None</p></li> |
| <li><p><strong>backend_opts</strong> (<em>dict of user-specified options to pass to the backend for partitioning</em><em>, </em><em>optional</em>) – Passed on to <cite>PrePartition</cite> and <cite>PostPartition</cite> functions of <cite>SubgraphProperty</cite></p></li> |
| <li><p><strong>clear</strong> (<em>bool</em><em>, </em><em>default False</em>) – clears any previous optimizations</p></li> |
| <li><p><strong>partition_if_dynamic</strong> (<em>bool</em><em>, </em><em>default False</em>) – whether to partition the graph when dynamic shape op exists</p></li> |
| <li><p><strong>static_alloc</strong> (<em>bool</em><em>, </em><em>default False</em>) – Statically allocate memory to improve speed. Memory usage may increase.</p></li> |
| <li><p><strong>static_shape</strong> (<em>bool</em><em>, </em><em>default False</em>) – Optimize for invariant input shapes between iterations. Must also |
| set static_alloc to True. Change of input shapes is still allowed |
| but slower.</p></li> |
| <li><p><strong>inline_limit</strong> (<em>optional int</em><em>, </em><em>default 2</em>) – Maximum number of operators that can be inlined.</p></li> |
| <li><p><strong>forward_bulk_size</strong> (<em>optional int</em><em>, </em><em>default None</em>) – Segment size of bulk execution during forward pass.</p></li> |
| <li><p><strong>backward_bulk_size</strong> (<em>optional int</em><em>, </em><em>default None</em>) – Segment size of bulk execution during backward pass.</p></li> |
| <li><p><strong>**kwargs</strong> (<em>The backend options</em><em>, </em><em>optional</em>) – Passed on to <cite>PrePartition</cite> and <cite>PostPartition</cite> functions of <cite>SubgraphProperty</cite></p></li> |
| </ul> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.VariationalDropoutCell.params"> |
| <em class="property">property </em><code class="sig-name descname">params</code><a class="headerlink" href="#mxnet.gluon.rnn.VariationalDropoutCell.params" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Return an attribute of instance, which is of type owner.</p> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.VariationalDropoutCell.register_child"> |
| <code class="sig-name descname">register_child</code><span class="sig-paren">(</span><em class="sig-param">block</em>, <em class="sig-param">name=None</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.rnn.VariationalDropoutCell.register_child" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Registers block as a child of self. <code class="xref py py-class docutils literal notranslate"><span class="pre">Block</span></code> s assigned to self as |
| attributes will be registered automatically.</p> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.VariationalDropoutCell.register_forward_hook"> |
| <code class="sig-name descname">register_forward_hook</code><span class="sig-paren">(</span><em class="sig-param">hook</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.rnn.VariationalDropoutCell.register_forward_hook" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Registers a forward hook on the block.</p> |
| <p>The hook function is called immediately after <a class="reference internal" href="#mxnet.gluon.rnn.VariationalDropoutCell.forward" title="mxnet.gluon.rnn.VariationalDropoutCell.forward"><code class="xref py py-func docutils literal notranslate"><span class="pre">forward()</span></code></a>. |
| It should not modify the input or output.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><p><strong>hook</strong> (<em>callable</em>) – The forward hook function of form <cite>hook(block, input, output) -> None</cite>.</p> |
| </dd> |
| <dt class="field-even">Returns</dt> |
| <dd class="field-even"><p></p> |
| </dd> |
| <dt class="field-odd">Return type</dt> |
| <dd class="field-odd"><p><code class="xref py py-class docutils literal notranslate"><span class="pre">mxnet.gluon.utils.HookHandle</span></code></p> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.VariationalDropoutCell.register_forward_pre_hook"> |
| <code class="sig-name descname">register_forward_pre_hook</code><span class="sig-paren">(</span><em class="sig-param">hook</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.rnn.VariationalDropoutCell.register_forward_pre_hook" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Registers a forward pre-hook on the block.</p> |
| <p>The hook function is called immediately before <a class="reference internal" href="#mxnet.gluon.rnn.VariationalDropoutCell.forward" title="mxnet.gluon.rnn.VariationalDropoutCell.forward"><code class="xref py py-func docutils literal notranslate"><span class="pre">forward()</span></code></a>. |
| It should not modify the input or output.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><p><strong>hook</strong> (<em>callable</em>) – The forward hook function of form <cite>hook(block, input) -> None</cite>.</p> |
| </dd> |
| <dt class="field-even">Returns</dt> |
| <dd class="field-even"><p></p> |
| </dd> |
| <dt class="field-odd">Return type</dt> |
| <dd class="field-odd"><p><code class="xref py py-class docutils literal notranslate"><span class="pre">mxnet.gluon.utils.HookHandle</span></code></p> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.VariationalDropoutCell.register_op_hook"> |
| <code class="sig-name descname">register_op_hook</code><span class="sig-paren">(</span><em class="sig-param">callback</em>, <em class="sig-param">monitor_all=False</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.rnn.VariationalDropoutCell.register_op_hook" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Install callback monitor.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>callback</strong> (<em>function</em>) – Function called to inspect the values of the intermediate outputs |
| of blocks after hybridization. It takes 3 parameters: |
| name of the tensor being inspected (str) |
| name of the operator producing or consuming that tensor (str) |
| tensor being inspected (NDArray).</p></li> |
| <li><p><strong>monitor_all</strong> (<em>bool</em><em>, </em><em>default False</em>) – If True, monitor both input and output, otherwise monitor output only.</p></li> |
| </ul> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.VariationalDropoutCell.reset"> |
| <code class="sig-name descname">reset</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/mxnet/gluon/rnn/rnn_cell.html#VariationalDropoutCell.reset"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.gluon.rnn.VariationalDropoutCell.reset" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Reset before re-using the cell for another graph.</p> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.VariationalDropoutCell.reset_ctx"> |
| <code class="sig-name descname">reset_ctx</code><span class="sig-paren">(</span><em class="sig-param">ctx</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.rnn.VariationalDropoutCell.reset_ctx" title="Permalink to this definition">¶</a></dt> |
| <dd><p>This function has been deprecated. Please refer to <code class="docutils literal notranslate"><span class="pre">Block.reset_device</span></code>.</p> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.VariationalDropoutCell.reset_device"> |
| <code class="sig-name descname">reset_device</code><span class="sig-paren">(</span><em class="sig-param">device</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.rnn.VariationalDropoutCell.reset_device" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Re-assign all Parameters to other devices.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><p><strong>device</strong> (Device or list of Device, default <code class="xref py py-meth docutils literal notranslate"><span class="pre">device.current_device()</span></code>.) – Assign Parameter to given device. If device is a list of Device, a |
| copy will be made for each device.</p> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.VariationalDropoutCell.save"> |
| <code class="sig-name descname">save</code><span class="sig-paren">(</span><em class="sig-param">prefix</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.rnn.VariationalDropoutCell.save" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Save the model architecture and parameters to load again later</p> |
| <p>Saves the model architecture as a nested dictionary where each Block |
| in the model is a dictionary and its children are sub-dictionaries.</p> |
| <p>Each Block is uniquely identified by Block class name and a unique ID. |
| We save each Block’s parameter UUID to restore later in order to match |
| the saved parameters.</p> |
| <p>Recursively traverses a Block’s children in order (since its an |
| OrderedDict) and uses the unique ID to denote that specific Block.</p> |
| <p>Assumes that the model is created in an identical order every time. |
| If the model is not able to be recreated deterministically do not |
| use this set of APIs to save/load your model.</p> |
| <p>For HybridBlocks, the cached_graph is saved (Symbol & inputs) if |
| it has already been hybridized.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><p><strong>prefix</strong> (<em>str</em>) – The prefix to use in filenames for saving this model: |
| <prefix>-model.json and <prefix>-model.params</p> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.VariationalDropoutCell.save_parameters"> |
| <code class="sig-name descname">save_parameters</code><span class="sig-paren">(</span><em class="sig-param">filename</em>, <em class="sig-param">deduplicate=False</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.rnn.VariationalDropoutCell.save_parameters" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Save parameters to file.</p> |
| <p>Saved parameters can only be loaded with <cite>load_parameters</cite>. Note that this |
| method only saves parameters, not model structure. If you want to save |
| model structures, please use <code class="xref py py-meth docutils literal notranslate"><span class="pre">HybridBlock.export()</span></code>.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>filename</strong> (<em>str</em>) – Path to file.</p></li> |
| <li><p><strong>deduplicate</strong> (<em>bool</em><em>, </em><em>default False</em>) – If True, save shared parameters only once. Otherwise, if a Block |
| contains multiple sub-blocks that share parameters, each of the |
| shared parameters will be separately saved for every sub-block.</p></li> |
| </ul> |
| </dd> |
| </dl> |
| <p class="rubric">References</p> |
| <p><a class="reference external" href="https://mxnet.apache.org/api/python/docs/tutorials/packages/gluon/blocks/save_load_params.html">Saving and Loading Gluon Models</a></p> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.VariationalDropoutCell.setattr"> |
| <code class="sig-name descname">setattr</code><span class="sig-paren">(</span><em class="sig-param">name</em>, <em class="sig-param">value</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.rnn.VariationalDropoutCell.setattr" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Set an attribute to a new value for all Parameters.</p> |
| <p>For example, set grad_req to null if you don’t need gradient w.r.t a |
| model’s Parameters:</p> |
| <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">model</span><span class="o">.</span><span class="n">setattr</span><span class="p">(</span><span class="s1">'grad_req'</span><span class="p">,</span> <span class="s1">'null'</span><span class="p">)</span> |
| </pre></div> |
| </div> |
| <p>or change the learning rate multiplier:</p> |
| <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">model</span><span class="o">.</span><span class="n">setattr</span><span class="p">(</span><span class="s1">'lr_mult'</span><span class="p">,</span> <span class="mf">0.5</span><span class="p">)</span> |
| </pre></div> |
| </div> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>name</strong> (<em>str</em>) – Name of the attribute.</p></li> |
| <li><p><strong>value</strong> (<em>valid type for attribute name</em>) – The new value for the attribute.</p></li> |
| </ul> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.VariationalDropoutCell.share_parameters"> |
| <code class="sig-name descname">share_parameters</code><span class="sig-paren">(</span><em class="sig-param">shared</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.rnn.VariationalDropoutCell.share_parameters" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Share parameters recursively inside the model.</p> |
| <p>For example, if you want <code class="docutils literal notranslate"><span class="pre">dense1</span></code> to share <code class="docutils literal notranslate"><span class="pre">dense0</span></code>’s weights, you can do:</p> |
| <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">dense0</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Dense</span><span class="p">(</span><span class="mi">20</span><span class="p">)</span> |
| <span class="n">dense1</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Dense</span><span class="p">(</span><span class="mi">20</span><span class="p">)</span> |
| <span class="n">dense1</span><span class="o">.</span><span class="n">share_parameters</span><span class="p">(</span><span class="n">dense0</span><span class="o">.</span><span class="n">collect_params</span><span class="p">())</span> |
| </pre></div> |
| </div> |
| <dl class="simple"> |
| <dt>which equals to</dt><dd><p>dense1.weight = dense0.weight |
| dense1.bias = dense0.bias</p> |
| </dd> |
| </dl> |
| <p>Note that unlike the <cite>load_parameters</cite> or <cite>load_dict</cite> functions, |
| <cite>share_parameters</cite> results in the <cite>Parameter</cite> object being shared (or |
| tied) between the models, whereas <cite>load_parameters</cite> or <cite>load_dict</cite> only |
| set the value of the data dictionary of a model. If you call |
| <cite>load_parameters</cite> or <cite>load_dict</cite> after <cite>share_parameters</cite>, the loaded |
| value will be reflected in all networks that use the shared (or tied) |
| <cite>Parameter</cite> object.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><p><strong>shared</strong> (<em>Dict</em>) – Dict of the shared parameters.</p> |
| </dd> |
| <dt class="field-even">Returns</dt> |
| <dd class="field-even"><p></p> |
| </dd> |
| <dt class="field-odd">Return type</dt> |
| <dd class="field-odd"><p>this block</p> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.VariationalDropoutCell.summary"> |
| <code class="sig-name descname">summary</code><span class="sig-paren">(</span><em class="sig-param">*inputs</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.rnn.VariationalDropoutCell.summary" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Print the summary of the model’s output and parameters.</p> |
| <p>The network must have been initialized, and must not have been hybridized.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><p><strong>inputs</strong> (<em>object</em>) – Any input that the model supports. For any tensor in the input, only |
| <a class="reference internal" href="../../legacy/ndarray/ndarray.html#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><code class="xref py py-class docutils literal notranslate"><span class="pre">mxnet.ndarray.NDArray</span></code></a> is supported.</p> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.VariationalDropoutCell.unroll"> |
| <code class="sig-name descname">unroll</code><span class="sig-paren">(</span><em class="sig-param">length</em>, <em class="sig-param">inputs</em>, <em class="sig-param">begin_state=None</em>, <em class="sig-param">layout='NTC'</em>, <em class="sig-param">merge_outputs=None</em>, <em class="sig-param">valid_length=None</em><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/mxnet/gluon/rnn/rnn_cell.html#VariationalDropoutCell.unroll"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.gluon.rnn.VariationalDropoutCell.unroll" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Unrolls an RNN cell across time steps.</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>) – Number of steps to unroll.</p></li> |
| <li><p><strong>inputs</strong> (<a class="reference internal" href="../../legacy/symbol/symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a><em>, </em><em>list of Symbol</em><em>, or </em><em>None</em>) – <p>If <cite>inputs</cite> is a single Symbol (usually the output |
| of Embedding symbol), it should have shape |
| (batch_size, length, …) if <cite>layout</cite> is ‘NTC’, |
| or (length, batch_size, …) if <cite>layout</cite> is ‘TNC’.</p> |
| <p>If <cite>inputs</cite> is a list of symbols (usually output of |
| previous unroll), they should all have shape |
| (batch_size, …).</p> |
| </p></li> |
| <li><p><strong>begin_state</strong> (<em>nested list of Symbol</em><em>, </em><em>optional</em>) – Input states created by <cite>begin_state()</cite> |
| or output state of another cell. |
| Created from <cite>begin_state()</cite> if <cite>None</cite>.</p></li> |
| <li><p><strong>layout</strong> (<em>str</em><em>, </em><em>optional</em>) – <cite>layout</cite> of input symbol. Only used if inputs |
| is a single Symbol.</p></li> |
| <li><p><strong>merge_outputs</strong> (<em>bool</em><em>, </em><em>optional</em>) – If <cite>False</cite>, returns outputs as a list of Symbols. |
| If <cite>True</cite>, concatenates output across time steps |
| and returns a single symbol with shape |
| (batch_size, length, …) if layout is ‘NTC’, |
| or (length, batch_size, …) if layout is ‘TNC’. |
| If <cite>None</cite>, output whatever is faster.</p></li> |
| <li><p><strong>valid_length</strong> (<a class="reference internal" href="../../legacy/symbol/symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a><em>, </em><a class="reference internal" href="../../legacy/ndarray/ndarray.html#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a><em> or </em><em>None</em>) – <cite>valid_length</cite> specifies the length of the sequences in the batch without padding. |
| This option is especially useful for building sequence-to-sequence models where |
| the input and output sequences would potentially be padded. |
| If <cite>valid_length</cite> is None, all sequences are assumed to have the same length. |
| If <cite>valid_length</cite> is a Symbol or NDArray, it should have shape (batch_size,). |
| The ith element will be the length of the ith sequence in the batch. |
| The last valid state will be return and the padded outputs will be masked with 0. |
| Note that <cite>valid_length</cite> must be smaller or equal to <cite>length</cite>.</p></li> |
| </ul> |
| </dd> |
| <dt class="field-even">Returns</dt> |
| <dd class="field-even"><p><ul class="simple"> |
| <li><p><strong>outputs</strong> (<em>list of Symbol or Symbol</em>) – Symbol (if <cite>merge_outputs</cite> is True) or list of Symbols |
| (if <cite>merge_outputs</cite> is False) corresponding to the output from |
| the RNN from this unrolling.</p></li> |
| <li><p><strong>states</strong> (<em>list of Symbol</em>) – The new state of this RNN after this unrolling. |
| The type of this symbol is same as the output of <cite>begin_state()</cite>.</p></li> |
| </ul> |
| </p> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.VariationalDropoutCell.zero_grad"> |
| <code class="sig-name descname">zero_grad</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.rnn.VariationalDropoutCell.zero_grad" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Sets all Parameters’ gradient buffer to 0.</p> |
| </dd></dl> |
| |
| </dd></dl> |
| |
| <dl class="class"> |
| <dt id="mxnet.gluon.rnn.ZoneoutCell"> |
| <em class="property">class </em><code class="sig-name descname">ZoneoutCell</code><span class="sig-paren">(</span><em class="sig-param">base_cell</em>, <em class="sig-param">zoneout_outputs=0.0</em>, <em class="sig-param">zoneout_states=0.0</em><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/mxnet/gluon/rnn/rnn_cell.html#ZoneoutCell"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.gluon.rnn.ZoneoutCell" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">mxnet.gluon.rnn.rnn_cell.ModifierCell</span></code></p> |
| <p>Applies Zoneout on base cell.</p> |
| <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.rnn.ZoneoutCell.apply" title="mxnet.gluon.rnn.ZoneoutCell.apply"><code class="xref py py-obj docutils literal notranslate"><span class="pre">apply</span></code></a>(fn)</p></td> |
| <td><p>Applies <code class="docutils literal notranslate"><span class="pre">fn</span></code> recursively to every child block as well as self.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.ZoneoutCell.cast" title="mxnet.gluon.rnn.ZoneoutCell.cast"><code class="xref py py-obj docutils literal notranslate"><span class="pre">cast</span></code></a>(dtype)</p></td> |
| <td><p>Cast this Block to use another data type.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.ZoneoutCell.collect_params" title="mxnet.gluon.rnn.ZoneoutCell.collect_params"><code class="xref py py-obj docutils literal notranslate"><span class="pre">collect_params</span></code></a>([select])</p></td> |
| <td><p>Returns a <code class="xref py py-class docutils literal notranslate"><span class="pre">Dict</span></code> containing this <code class="xref py py-class docutils literal notranslate"><span class="pre">Block</span></code> and all of its children’s Parameters(default), also can returns the select <code class="xref py py-class docutils literal notranslate"><span class="pre">Dict</span></code> which match some given regular expressions.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.ZoneoutCell.export" title="mxnet.gluon.rnn.ZoneoutCell.export"><code class="xref py py-obj docutils literal notranslate"><span class="pre">export</span></code></a>(path[, epoch, remove_amp_cast])</p></td> |
| <td><p>Export HybridBlock to json format that can be loaded by <cite>gluon.SymbolBlock.imports</cite> or the C++ interface.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.ZoneoutCell.forward" title="mxnet.gluon.rnn.ZoneoutCell.forward"><code class="xref py py-obj docutils literal notranslate"><span class="pre">forward</span></code></a>(inputs, states)</p></td> |
| <td><p>Unrolls the recurrent cell for one time step.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.ZoneoutCell.hybridize" title="mxnet.gluon.rnn.ZoneoutCell.hybridize"><code class="xref py py-obj docutils literal notranslate"><span class="pre">hybridize</span></code></a>([active])</p></td> |
| <td><p>Please refer description of HybridBlock hybridize().</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.ZoneoutCell.infer_shape" title="mxnet.gluon.rnn.ZoneoutCell.infer_shape"><code class="xref py py-obj docutils literal notranslate"><span class="pre">infer_shape</span></code></a>(i, x, is_bidirect)</p></td> |
| <td><p>Infers shape of Parameters from inputs.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.ZoneoutCell.infer_type" title="mxnet.gluon.rnn.ZoneoutCell.infer_type"><code class="xref py py-obj docutils literal notranslate"><span class="pre">infer_type</span></code></a>(*args)</p></td> |
| <td><p>Infers data type of Parameters from inputs.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.ZoneoutCell.initialize" title="mxnet.gluon.rnn.ZoneoutCell.initialize"><code class="xref py py-obj docutils literal notranslate"><span class="pre">initialize</span></code></a>([init, device, verbose, force_reinit])</p></td> |
| <td><p>Initializes <code class="xref py py-class docutils literal notranslate"><span class="pre">Parameter</span></code> s of this <code class="xref py py-class docutils literal notranslate"><span class="pre">Block</span></code> and its children.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.ZoneoutCell.load" title="mxnet.gluon.rnn.ZoneoutCell.load"><code class="xref py py-obj docutils literal notranslate"><span class="pre">load</span></code></a>(prefix)</p></td> |
| <td><p>Load a model saved using the <cite>save</cite> API</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.ZoneoutCell.load_dict" title="mxnet.gluon.rnn.ZoneoutCell.load_dict"><code class="xref py py-obj docutils literal notranslate"><span class="pre">load_dict</span></code></a>(param_dict[, device, …])</p></td> |
| <td><p>Load parameters from dict</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.ZoneoutCell.load_parameters" title="mxnet.gluon.rnn.ZoneoutCell.load_parameters"><code class="xref py py-obj docutils literal notranslate"><span class="pre">load_parameters</span></code></a>(filename[, device, …])</p></td> |
| <td><p>Load parameters from file previously saved by <cite>save_parameters</cite>.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.ZoneoutCell.optimize_for" title="mxnet.gluon.rnn.ZoneoutCell.optimize_for"><code class="xref py py-obj docutils literal notranslate"><span class="pre">optimize_for</span></code></a>(x, *args[, backend, clear, …])</p></td> |
| <td><p>Partitions the current HybridBlock and optimizes it for a given backend without executing a forward pass.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.ZoneoutCell.register_child" title="mxnet.gluon.rnn.ZoneoutCell.register_child"><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_child</span></code></a>(block[, name])</p></td> |
| <td><p>Registers block as a child of self.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.ZoneoutCell.register_forward_hook" title="mxnet.gluon.rnn.ZoneoutCell.register_forward_hook"><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_forward_hook</span></code></a>(hook)</p></td> |
| <td><p>Registers a forward hook on the block.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.ZoneoutCell.register_forward_pre_hook" title="mxnet.gluon.rnn.ZoneoutCell.register_forward_pre_hook"><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_forward_pre_hook</span></code></a>(hook)</p></td> |
| <td><p>Registers a forward pre-hook on the block.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.ZoneoutCell.register_op_hook" title="mxnet.gluon.rnn.ZoneoutCell.register_op_hook"><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_op_hook</span></code></a>(callback[, monitor_all])</p></td> |
| <td><p>Install callback monitor.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.ZoneoutCell.reset" title="mxnet.gluon.rnn.ZoneoutCell.reset"><code class="xref py py-obj docutils literal notranslate"><span class="pre">reset</span></code></a>()</p></td> |
| <td><p>Reset before re-using the cell for another graph.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.ZoneoutCell.reset_ctx" title="mxnet.gluon.rnn.ZoneoutCell.reset_ctx"><code class="xref py py-obj docutils literal notranslate"><span class="pre">reset_ctx</span></code></a>(ctx)</p></td> |
| <td><p>This function has been deprecated.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.ZoneoutCell.reset_device" title="mxnet.gluon.rnn.ZoneoutCell.reset_device"><code class="xref py py-obj docutils literal notranslate"><span class="pre">reset_device</span></code></a>(device)</p></td> |
| <td><p>Re-assign all Parameters to other devices.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.ZoneoutCell.save" title="mxnet.gluon.rnn.ZoneoutCell.save"><code class="xref py py-obj docutils literal notranslate"><span class="pre">save</span></code></a>(prefix)</p></td> |
| <td><p>Save the model architecture and parameters to load again later</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.ZoneoutCell.save_parameters" title="mxnet.gluon.rnn.ZoneoutCell.save_parameters"><code class="xref py py-obj docutils literal notranslate"><span class="pre">save_parameters</span></code></a>(filename[, deduplicate])</p></td> |
| <td><p>Save parameters to file.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.ZoneoutCell.setattr" title="mxnet.gluon.rnn.ZoneoutCell.setattr"><code class="xref py py-obj docutils literal notranslate"><span class="pre">setattr</span></code></a>(name, value)</p></td> |
| <td><p>Set an attribute to a new value for all Parameters.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.ZoneoutCell.share_parameters" title="mxnet.gluon.rnn.ZoneoutCell.share_parameters"><code class="xref py py-obj docutils literal notranslate"><span class="pre">share_parameters</span></code></a>(shared)</p></td> |
| <td><p>Share parameters recursively inside the model.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.ZoneoutCell.summary" title="mxnet.gluon.rnn.ZoneoutCell.summary"><code class="xref py py-obj docutils literal notranslate"><span class="pre">summary</span></code></a>(*inputs)</p></td> |
| <td><p>Print the summary of the model’s output and parameters.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.ZoneoutCell.unroll" title="mxnet.gluon.rnn.ZoneoutCell.unroll"><code class="xref py py-obj docutils literal notranslate"><span class="pre">unroll</span></code></a>(length, inputs[, begin_state, …])</p></td> |
| <td><p>Unrolls an RNN cell across time steps.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.ZoneoutCell.zero_grad" title="mxnet.gluon.rnn.ZoneoutCell.zero_grad"><code class="xref py py-obj docutils literal notranslate"><span class="pre">zero_grad</span></code></a>()</p></td> |
| <td><p>Sets all Parameters’ gradient buffer to 0.</p></td> |
| </tr> |
| </tbody> |
| </table> |
| <p><strong>Attributes</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.rnn.ZoneoutCell.params" title="mxnet.gluon.rnn.ZoneoutCell.params"><code class="xref py py-obj docutils literal notranslate"><span class="pre">params</span></code></a></p></td> |
| <td><p>Return an attribute of instance, which is of type owner.</p></td> |
| </tr> |
| </tbody> |
| </table> |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.ZoneoutCell.apply"> |
| <code class="sig-name descname">apply</code><span class="sig-paren">(</span><em class="sig-param">fn</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.rnn.ZoneoutCell.apply" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Applies <code class="docutils literal notranslate"><span class="pre">fn</span></code> recursively to every child block as well as self.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><p><strong>fn</strong> (<em>callable</em>) – Function to be applied to each submodule, of form <cite>fn(block)</cite>.</p> |
| </dd> |
| <dt class="field-even">Returns</dt> |
| <dd class="field-even"><p></p> |
| </dd> |
| <dt class="field-odd">Return type</dt> |
| <dd class="field-odd"><p>this block</p> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.ZoneoutCell.cast"> |
| <code class="sig-name descname">cast</code><span class="sig-paren">(</span><em class="sig-param">dtype</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.rnn.ZoneoutCell.cast" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Cast this Block to use another data type.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><p><strong>dtype</strong> (<em>str</em><em> or </em><em>numpy.dtype</em>) – The new data type.</p> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.ZoneoutCell.collect_params"> |
| <code class="sig-name descname">collect_params</code><span class="sig-paren">(</span><em class="sig-param">select=None</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.rnn.ZoneoutCell.collect_params" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Returns a <code class="xref py py-class docutils literal notranslate"><span class="pre">Dict</span></code> containing this <code class="xref py py-class docutils literal notranslate"><span class="pre">Block</span></code> and all of its |
| children’s Parameters(default), also can returns the select <code class="xref py py-class docutils literal notranslate"><span class="pre">Dict</span></code> |
| which match some given regular expressions.</p> |
| <p>For example, collect the specified parameters in [‘conv1.weight’, ‘conv1.bias’, ‘fc.weight’, |
| ‘fc.bias’]:</p> |
| <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">model</span><span class="o">.</span><span class="n">collect_params</span><span class="p">(</span><span class="s1">'conv1.weight|conv1.bias|fc.weight|fc.bias'</span><span class="p">)</span> |
| </pre></div> |
| </div> |
| <p>or collect all parameters whose names end with ‘weight’ or ‘bias’, this can be done |
| using regular expressions:</p> |
| <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">model</span><span class="o">.</span><span class="n">collect_params</span><span class="p">(</span><span class="s1">'.*weight|.*bias'</span><span class="p">)</span> |
| </pre></div> |
| </div> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><p><strong>select</strong> (<em>str</em>) – regular expressions</p> |
| </dd> |
| <dt class="field-even">Returns</dt> |
| <dd class="field-even"><p></p> |
| </dd> |
| <dt class="field-odd">Return type</dt> |
| <dd class="field-odd"><p>The selected <code class="xref py py-class docutils literal notranslate"><span class="pre">Dict</span></code></p> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.ZoneoutCell.export"> |
| <code class="sig-name descname">export</code><span class="sig-paren">(</span><em class="sig-param">path</em>, <em class="sig-param">epoch=0</em>, <em class="sig-param">remove_amp_cast=True</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.rnn.ZoneoutCell.export" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Export HybridBlock to json format that can be loaded by |
| <cite>gluon.SymbolBlock.imports</cite> or the C++ interface.</p> |
| <div class="admonition note"> |
| <p class="admonition-title">Note</p> |
| <p>When there are only one input, it will have name <cite>data</cite>. When there |
| Are more than one inputs, they will be named as <cite>data0</cite>, <cite>data1</cite>, etc.</p> |
| </div> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>path</strong> (<em>str</em><em> or </em><em>None</em>) – Path to save model. Two files <cite>path-symbol.json</cite> and <cite>path-xxxx.params</cite> |
| will be created, where xxxx is the 4 digits epoch number. |
| If None, do not export to file but return Python Symbol object and |
| corresponding dictionary of parameters.</p></li> |
| <li><p><strong>epoch</strong> (<em>int</em>) – Epoch number of saved model.</p></li> |
| <li><p><strong>remove_amp_cast</strong> (<em>bool</em><em>, </em><em>optional</em>) – Whether to remove the amp_cast and amp_multicast operators, before saving the model.</p></li> |
| </ul> |
| </dd> |
| <dt class="field-even">Returns</dt> |
| <dd class="field-even"><p><ul class="simple"> |
| <li><p><strong>symbol_filename</strong> (<em>str</em>) – Filename to which model symbols were saved, including <cite>path</cite> prefix.</p></li> |
| <li><p><strong>params_filename</strong> (<em>str</em>) – Filename to which model parameters were saved, including <cite>path</cite> prefix.</p></li> |
| </ul> |
| </p> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.ZoneoutCell.forward"> |
| <code class="sig-name descname">forward</code><span class="sig-paren">(</span><em class="sig-param">inputs</em>, <em class="sig-param">states</em><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/mxnet/gluon/rnn/rnn_cell.html#ZoneoutCell.forward"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.gluon.rnn.ZoneoutCell.forward" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Unrolls the recurrent cell for one time step.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>inputs</strong> (<em>sym.Variable</em>) – Input symbol, 2D, of shape (batch_size * num_units).</p></li> |
| <li><p><strong>states</strong> (<em>list of sym.Variable</em>) – RNN state from previous step or the output of begin_state().</p></li> |
| </ul> |
| </dd> |
| <dt class="field-even">Returns</dt> |
| <dd class="field-even"><p><ul class="simple"> |
| <li><p><strong>output</strong> (<em>Symbol</em>) – Symbol corresponding to the output from the RNN when unrolling |
| for a single time step.</p></li> |
| <li><p><strong>states</strong> (<em>list of Symbol</em>) – The new state of this RNN after this unrolling. |
| The type of this symbol is same as the output of <cite>begin_state()</cite>. |
| This can be used as an input state to the next time step |
| of this RNN.</p></li> |
| </ul> |
| </p> |
| </dd> |
| </dl> |
| <div class="admonition seealso"> |
| <p class="admonition-title">See also</p> |
| <dl class="simple"> |
| <dt><code class="xref py py-meth docutils literal notranslate"><span class="pre">begin_state()</span></code></dt><dd><p>This function can provide the states for the first time step.</p> |
| </dd> |
| <dt><a class="reference internal" href="#mxnet.gluon.rnn.ZoneoutCell.unroll" title="mxnet.gluon.rnn.ZoneoutCell.unroll"><code class="xref py py-meth docutils literal notranslate"><span class="pre">unroll()</span></code></a></dt><dd><p>This function unrolls an RNN for a given number of (>=1) time steps.</p> |
| </dd> |
| </dl> |
| </div> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.ZoneoutCell.hybridize"> |
| <code class="sig-name descname">hybridize</code><span class="sig-paren">(</span><em class="sig-param">active=True</em>, <em class="sig-param">**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.rnn.ZoneoutCell.hybridize" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Please refer description of HybridBlock hybridize().</p> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.ZoneoutCell.infer_shape"> |
| <code class="sig-name descname">infer_shape</code><span class="sig-paren">(</span><em class="sig-param">i</em>, <em class="sig-param">x</em>, <em class="sig-param">is_bidirect</em><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/mxnet/gluon/rnn/rnn_cell.html#ZoneoutCell.infer_shape"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.gluon.rnn.ZoneoutCell.infer_shape" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Infers shape of Parameters from inputs.</p> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.ZoneoutCell.infer_type"> |
| <code class="sig-name descname">infer_type</code><span class="sig-paren">(</span><em class="sig-param">*args</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.rnn.ZoneoutCell.infer_type" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Infers data type of Parameters from inputs.</p> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.ZoneoutCell.initialize"> |
| <code class="sig-name descname">initialize</code><span class="sig-paren">(</span><em class="sig-param">init=<mxnet.initializer.Uniform object></em>, <em class="sig-param">device=None</em>, <em class="sig-param">verbose=False</em>, <em class="sig-param">force_reinit=False</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.rnn.ZoneoutCell.initialize" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Initializes <code class="xref py py-class docutils literal notranslate"><span class="pre">Parameter</span></code> s of this <code class="xref py py-class docutils literal notranslate"><span class="pre">Block</span></code> and its children.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>init</strong> (<a class="reference internal" href="../../initializer/index.html#mxnet.initializer.Initializer" title="mxnet.initializer.Initializer"><em>Initializer</em></a>) – Global default Initializer to be used when <code class="xref py py-meth docutils literal notranslate"><span class="pre">Parameter.init()</span></code> is <code class="docutils literal notranslate"><span class="pre">None</span></code>. |
| Otherwise, <code class="xref py py-meth docutils literal notranslate"><span class="pre">Parameter.init()</span></code> takes precedence.</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>) – Keeps a copy of Parameters on one or many device(s).</p></li> |
| <li><p><strong>verbose</strong> (<em>bool</em><em>, </em><em>default False</em>) – Whether to verbosely print out details on initialization.</p></li> |
| <li><p><strong>force_reinit</strong> (<em>bool</em><em>, </em><em>default False</em>) – Whether to force re-initialization if parameter is already initialized.</p></li> |
| </ul> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.ZoneoutCell.load"> |
| <code class="sig-name descname">load</code><span class="sig-paren">(</span><em class="sig-param">prefix</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.rnn.ZoneoutCell.load" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Load a model saved using the <cite>save</cite> API</p> |
| <p>Reconfigures a model using the saved configuration. This function |
| does not regenerate the model architecture. It resets each Block’s |
| parameter UUIDs as they were when saved in order to match the names of the |
| saved parameters.</p> |
| <p>This function assumes the Blocks in the model were created in the same |
| order they were when the model was saved. This is because each Block is |
| uniquely identified by Block class name and a unique ID in order (since |
| its an OrderedDict) and uses the unique ID to denote that specific Block.</p> |
| <p>Assumes that the model is created in an identical order every time. |
| If the model is not able to be recreated deterministically do not |
| use this set of APIs to save/load your model.</p> |
| <p>For HybridBlocks, the cached_graph (Symbol & inputs) and settings are |
| restored if it had been hybridized before saving.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><p><strong>prefix</strong> (<em>str</em>) – The prefix to use in filenames for loading this model: |
| <prefix>-model.json and <prefix>-model.params</p> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.ZoneoutCell.load_dict"> |
| <code class="sig-name descname">load_dict</code><span class="sig-paren">(</span><em class="sig-param">param_dict</em>, <em class="sig-param">device=None</em>, <em class="sig-param">allow_missing=False</em>, <em class="sig-param">ignore_extra=False</em>, <em class="sig-param">cast_dtype=False</em>, <em class="sig-param">dtype_source='current'</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.rnn.ZoneoutCell.load_dict" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Load parameters from dict</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>param_dict</strong> (<em>dict</em>) – Dictionary containing model 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>, </em><em>optional</em>) – Device context on which the memory is allocated. Default is |
| <cite>mxnet.device.current_device()</cite>.</p></li> |
| <li><p><strong>allow_missing</strong> (<em>bool</em><em>, </em><em>default False</em>) – Whether to silently skip loading parameters not represented in the file.</p></li> |
| <li><p><strong>ignore_extra</strong> (<em>bool</em><em>, </em><em>default False</em>) – Whether to silently ignore parameters from the file that are not |
| present in this dict.</p></li> |
| <li><p><strong>cast_dtype</strong> (<em>bool</em><em>, </em><em>default False</em>) – Cast the data type of the NDArray loaded from the checkpoint to the dtype |
| provided by the Parameter if any</p></li> |
| <li><p><strong>dtype_source</strong> (<em>str</em><em>, </em><em>default 'current'</em>) – must be in {‘current’, ‘saved’} |
| Only valid if cast_dtype=True, specify the source of the dtype for casting |
| the parameters</p></li> |
| </ul> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.ZoneoutCell.load_parameters"> |
| <code class="sig-name descname">load_parameters</code><span class="sig-paren">(</span><em class="sig-param">filename</em>, <em class="sig-param">device=None</em>, <em class="sig-param">allow_missing=False</em>, <em class="sig-param">ignore_extra=False</em>, <em class="sig-param">cast_dtype=False</em>, <em class="sig-param">dtype_source='current'</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.rnn.ZoneoutCell.load_parameters" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Load parameters from file previously saved by <cite>save_parameters</cite>.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>filename</strong> (<em>str</em>) – Path to parameter file.</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><em>, </em><em>default cpu</em><em>(</em><em>)</em>) – Device(s) to initialize loaded parameters on.</p></li> |
| <li><p><strong>allow_missing</strong> (<em>bool</em><em>, </em><em>default False</em>) – Whether to silently skip loading parameters not represents in the file.</p></li> |
| <li><p><strong>ignore_extra</strong> (<em>bool</em><em>, </em><em>default False</em>) – Whether to silently ignore parameters from the file that are not |
| present in this Block.</p></li> |
| <li><p><strong>cast_dtype</strong> (<em>bool</em><em>, </em><em>default False</em>) – Cast the data type of the NDArray loaded from the checkpoint to the dtype |
| provided by the Parameter if any.</p></li> |
| <li><p><strong>dtype_source</strong> (<em>str</em><em>, </em><em>default 'current'</em>) – must be in {‘current’, ‘saved’} |
| Only valid if cast_dtype=True, specify the source of the dtype for casting |
| the parameters</p></li> |
| </ul> |
| </dd> |
| </dl> |
| <p class="rubric">References</p> |
| <p><a class="reference external" href="https://mxnet.apache.org/api/python/docs/tutorials/packages/gluon/blocks/save_load_params.html">Saving and Loading Gluon Models</a></p> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.ZoneoutCell.optimize_for"> |
| <code class="sig-name descname">optimize_for</code><span class="sig-paren">(</span><em class="sig-param">x</em>, <em class="sig-param">*args</em>, <em class="sig-param">backend=None</em>, <em class="sig-param">clear=False</em>, <em class="sig-param">partition_if_dynamic=True</em>, <em class="sig-param">static_alloc=False</em>, <em class="sig-param">static_shape=False</em>, <em class="sig-param">inline_limit=2</em>, <em class="sig-param">forward_bulk_size=None</em>, <em class="sig-param">backward_bulk_size=None</em>, <em class="sig-param">**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.rnn.ZoneoutCell.optimize_for" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Partitions the current HybridBlock and optimizes it for a given backend |
| without executing a forward pass. Modifies the HybridBlock in-place.</p> |
| <p>Immediately partitions a HybridBlock using the specified backend. Combines |
| the work done in the hybridize API with part of the work done in the forward |
| pass without calling the CachedOp. Can be used in place of hybridize, |
| afterwards <cite>export</cite> can be called or inference can be run. See README.md in |
| example/extensions/lib_subgraph/README.md for more details.</p> |
| <p class="rubric">Examples</p> |
| <p># partition and then export to file |
| block.optimize_for(x, backend=’myPart’) |
| block.export(‘partitioned’)</p> |
| <p># partition and then run inference |
| block.optimize_for(x, backend=’myPart’) |
| block(x)</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>x</strong> (<a class="reference internal" href="../../legacy/ndarray/ndarray.html#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – first input to model</p></li> |
| <li><p><strong>*args</strong> (<a class="reference internal" href="../../legacy/ndarray/ndarray.html#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – other inputs to model</p></li> |
| <li><p><strong>backend</strong> (<em>str</em>) – The name of backend, as registered in <cite>SubgraphBackendRegistry</cite>, default None</p></li> |
| <li><p><strong>backend_opts</strong> (<em>dict of user-specified options to pass to the backend for partitioning</em><em>, </em><em>optional</em>) – Passed on to <cite>PrePartition</cite> and <cite>PostPartition</cite> functions of <cite>SubgraphProperty</cite></p></li> |
| <li><p><strong>clear</strong> (<em>bool</em><em>, </em><em>default False</em>) – clears any previous optimizations</p></li> |
| <li><p><strong>partition_if_dynamic</strong> (<em>bool</em><em>, </em><em>default False</em>) – whether to partition the graph when dynamic shape op exists</p></li> |
| <li><p><strong>static_alloc</strong> (<em>bool</em><em>, </em><em>default False</em>) – Statically allocate memory to improve speed. Memory usage may increase.</p></li> |
| <li><p><strong>static_shape</strong> (<em>bool</em><em>, </em><em>default False</em>) – Optimize for invariant input shapes between iterations. Must also |
| set static_alloc to True. Change of input shapes is still allowed |
| but slower.</p></li> |
| <li><p><strong>inline_limit</strong> (<em>optional int</em><em>, </em><em>default 2</em>) – Maximum number of operators that can be inlined.</p></li> |
| <li><p><strong>forward_bulk_size</strong> (<em>optional int</em><em>, </em><em>default None</em>) – Segment size of bulk execution during forward pass.</p></li> |
| <li><p><strong>backward_bulk_size</strong> (<em>optional int</em><em>, </em><em>default None</em>) – Segment size of bulk execution during backward pass.</p></li> |
| <li><p><strong>**kwargs</strong> (<em>The backend options</em><em>, </em><em>optional</em>) – Passed on to <cite>PrePartition</cite> and <cite>PostPartition</cite> functions of <cite>SubgraphProperty</cite></p></li> |
| </ul> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.ZoneoutCell.params"> |
| <em class="property">property </em><code class="sig-name descname">params</code><a class="headerlink" href="#mxnet.gluon.rnn.ZoneoutCell.params" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Return an attribute of instance, which is of type owner.</p> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.ZoneoutCell.register_child"> |
| <code class="sig-name descname">register_child</code><span class="sig-paren">(</span><em class="sig-param">block</em>, <em class="sig-param">name=None</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.rnn.ZoneoutCell.register_child" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Registers block as a child of self. <code class="xref py py-class docutils literal notranslate"><span class="pre">Block</span></code> s assigned to self as |
| attributes will be registered automatically.</p> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.ZoneoutCell.register_forward_hook"> |
| <code class="sig-name descname">register_forward_hook</code><span class="sig-paren">(</span><em class="sig-param">hook</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.rnn.ZoneoutCell.register_forward_hook" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Registers a forward hook on the block.</p> |
| <p>The hook function is called immediately after <a class="reference internal" href="#mxnet.gluon.rnn.ZoneoutCell.forward" title="mxnet.gluon.rnn.ZoneoutCell.forward"><code class="xref py py-func docutils literal notranslate"><span class="pre">forward()</span></code></a>. |
| It should not modify the input or output.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><p><strong>hook</strong> (<em>callable</em>) – The forward hook function of form <cite>hook(block, input, output) -> None</cite>.</p> |
| </dd> |
| <dt class="field-even">Returns</dt> |
| <dd class="field-even"><p></p> |
| </dd> |
| <dt class="field-odd">Return type</dt> |
| <dd class="field-odd"><p><code class="xref py py-class docutils literal notranslate"><span class="pre">mxnet.gluon.utils.HookHandle</span></code></p> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.ZoneoutCell.register_forward_pre_hook"> |
| <code class="sig-name descname">register_forward_pre_hook</code><span class="sig-paren">(</span><em class="sig-param">hook</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.rnn.ZoneoutCell.register_forward_pre_hook" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Registers a forward pre-hook on the block.</p> |
| <p>The hook function is called immediately before <a class="reference internal" href="#mxnet.gluon.rnn.ZoneoutCell.forward" title="mxnet.gluon.rnn.ZoneoutCell.forward"><code class="xref py py-func docutils literal notranslate"><span class="pre">forward()</span></code></a>. |
| It should not modify the input or output.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><p><strong>hook</strong> (<em>callable</em>) – The forward hook function of form <cite>hook(block, input) -> None</cite>.</p> |
| </dd> |
| <dt class="field-even">Returns</dt> |
| <dd class="field-even"><p></p> |
| </dd> |
| <dt class="field-odd">Return type</dt> |
| <dd class="field-odd"><p><code class="xref py py-class docutils literal notranslate"><span class="pre">mxnet.gluon.utils.HookHandle</span></code></p> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.ZoneoutCell.register_op_hook"> |
| <code class="sig-name descname">register_op_hook</code><span class="sig-paren">(</span><em class="sig-param">callback</em>, <em class="sig-param">monitor_all=False</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.rnn.ZoneoutCell.register_op_hook" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Install callback monitor.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>callback</strong> (<em>function</em>) – Function called to inspect the values of the intermediate outputs |
| of blocks after hybridization. It takes 3 parameters: |
| name of the tensor being inspected (str) |
| name of the operator producing or consuming that tensor (str) |
| tensor being inspected (NDArray).</p></li> |
| <li><p><strong>monitor_all</strong> (<em>bool</em><em>, </em><em>default False</em>) – If True, monitor both input and output, otherwise monitor output only.</p></li> |
| </ul> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.ZoneoutCell.reset"> |
| <code class="sig-name descname">reset</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/mxnet/gluon/rnn/rnn_cell.html#ZoneoutCell.reset"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.gluon.rnn.ZoneoutCell.reset" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Reset before re-using the cell for another graph.</p> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.ZoneoutCell.reset_ctx"> |
| <code class="sig-name descname">reset_ctx</code><span class="sig-paren">(</span><em class="sig-param">ctx</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.rnn.ZoneoutCell.reset_ctx" title="Permalink to this definition">¶</a></dt> |
| <dd><p>This function has been deprecated. Please refer to <code class="docutils literal notranslate"><span class="pre">Block.reset_device</span></code>.</p> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.ZoneoutCell.reset_device"> |
| <code class="sig-name descname">reset_device</code><span class="sig-paren">(</span><em class="sig-param">device</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.rnn.ZoneoutCell.reset_device" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Re-assign all Parameters to other devices.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><p><strong>device</strong> (Device or list of Device, default <code class="xref py py-meth docutils literal notranslate"><span class="pre">device.current_device()</span></code>.) – Assign Parameter to given device. If device is a list of Device, a |
| copy will be made for each device.</p> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.ZoneoutCell.save"> |
| <code class="sig-name descname">save</code><span class="sig-paren">(</span><em class="sig-param">prefix</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.rnn.ZoneoutCell.save" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Save the model architecture and parameters to load again later</p> |
| <p>Saves the model architecture as a nested dictionary where each Block |
| in the model is a dictionary and its children are sub-dictionaries.</p> |
| <p>Each Block is uniquely identified by Block class name and a unique ID. |
| We save each Block’s parameter UUID to restore later in order to match |
| the saved parameters.</p> |
| <p>Recursively traverses a Block’s children in order (since its an |
| OrderedDict) and uses the unique ID to denote that specific Block.</p> |
| <p>Assumes that the model is created in an identical order every time. |
| If the model is not able to be recreated deterministically do not |
| use this set of APIs to save/load your model.</p> |
| <p>For HybridBlocks, the cached_graph is saved (Symbol & inputs) if |
| it has already been hybridized.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><p><strong>prefix</strong> (<em>str</em>) – The prefix to use in filenames for saving this model: |
| <prefix>-model.json and <prefix>-model.params</p> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.ZoneoutCell.save_parameters"> |
| <code class="sig-name descname">save_parameters</code><span class="sig-paren">(</span><em class="sig-param">filename</em>, <em class="sig-param">deduplicate=False</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.rnn.ZoneoutCell.save_parameters" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Save parameters to file.</p> |
| <p>Saved parameters can only be loaded with <cite>load_parameters</cite>. Note that this |
| method only saves parameters, not model structure. If you want to save |
| model structures, please use <code class="xref py py-meth docutils literal notranslate"><span class="pre">HybridBlock.export()</span></code>.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>filename</strong> (<em>str</em>) – Path to file.</p></li> |
| <li><p><strong>deduplicate</strong> (<em>bool</em><em>, </em><em>default False</em>) – If True, save shared parameters only once. Otherwise, if a Block |
| contains multiple sub-blocks that share parameters, each of the |
| shared parameters will be separately saved for every sub-block.</p></li> |
| </ul> |
| </dd> |
| </dl> |
| <p class="rubric">References</p> |
| <p><a class="reference external" href="https://mxnet.apache.org/api/python/docs/tutorials/packages/gluon/blocks/save_load_params.html">Saving and Loading Gluon Models</a></p> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.ZoneoutCell.setattr"> |
| <code class="sig-name descname">setattr</code><span class="sig-paren">(</span><em class="sig-param">name</em>, <em class="sig-param">value</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.rnn.ZoneoutCell.setattr" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Set an attribute to a new value for all Parameters.</p> |
| <p>For example, set grad_req to null if you don’t need gradient w.r.t a |
| model’s Parameters:</p> |
| <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">model</span><span class="o">.</span><span class="n">setattr</span><span class="p">(</span><span class="s1">'grad_req'</span><span class="p">,</span> <span class="s1">'null'</span><span class="p">)</span> |
| </pre></div> |
| </div> |
| <p>or change the learning rate multiplier:</p> |
| <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">model</span><span class="o">.</span><span class="n">setattr</span><span class="p">(</span><span class="s1">'lr_mult'</span><span class="p">,</span> <span class="mf">0.5</span><span class="p">)</span> |
| </pre></div> |
| </div> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>name</strong> (<em>str</em>) – Name of the attribute.</p></li> |
| <li><p><strong>value</strong> (<em>valid type for attribute name</em>) – The new value for the attribute.</p></li> |
| </ul> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.ZoneoutCell.share_parameters"> |
| <code class="sig-name descname">share_parameters</code><span class="sig-paren">(</span><em class="sig-param">shared</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.rnn.ZoneoutCell.share_parameters" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Share parameters recursively inside the model.</p> |
| <p>For example, if you want <code class="docutils literal notranslate"><span class="pre">dense1</span></code> to share <code class="docutils literal notranslate"><span class="pre">dense0</span></code>’s weights, you can do:</p> |
| <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">dense0</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Dense</span><span class="p">(</span><span class="mi">20</span><span class="p">)</span> |
| <span class="n">dense1</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Dense</span><span class="p">(</span><span class="mi">20</span><span class="p">)</span> |
| <span class="n">dense1</span><span class="o">.</span><span class="n">share_parameters</span><span class="p">(</span><span class="n">dense0</span><span class="o">.</span><span class="n">collect_params</span><span class="p">())</span> |
| </pre></div> |
| </div> |
| <dl class="simple"> |
| <dt>which equals to</dt><dd><p>dense1.weight = dense0.weight |
| dense1.bias = dense0.bias</p> |
| </dd> |
| </dl> |
| <p>Note that unlike the <cite>load_parameters</cite> or <cite>load_dict</cite> functions, |
| <cite>share_parameters</cite> results in the <cite>Parameter</cite> object being shared (or |
| tied) between the models, whereas <cite>load_parameters</cite> or <cite>load_dict</cite> only |
| set the value of the data dictionary of a model. If you call |
| <cite>load_parameters</cite> or <cite>load_dict</cite> after <cite>share_parameters</cite>, the loaded |
| value will be reflected in all networks that use the shared (or tied) |
| <cite>Parameter</cite> object.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><p><strong>shared</strong> (<em>Dict</em>) – Dict of the shared parameters.</p> |
| </dd> |
| <dt class="field-even">Returns</dt> |
| <dd class="field-even"><p></p> |
| </dd> |
| <dt class="field-odd">Return type</dt> |
| <dd class="field-odd"><p>this block</p> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.ZoneoutCell.summary"> |
| <code class="sig-name descname">summary</code><span class="sig-paren">(</span><em class="sig-param">*inputs</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.rnn.ZoneoutCell.summary" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Print the summary of the model’s output and parameters.</p> |
| <p>The network must have been initialized, and must not have been hybridized.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><p><strong>inputs</strong> (<em>object</em>) – Any input that the model supports. For any tensor in the input, only |
| <a class="reference internal" href="../../legacy/ndarray/ndarray.html#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><code class="xref py py-class docutils literal notranslate"><span class="pre">mxnet.ndarray.NDArray</span></code></a> is supported.</p> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.ZoneoutCell.unroll"> |
| <code class="sig-name descname">unroll</code><span class="sig-paren">(</span><em class="sig-param">length</em>, <em class="sig-param">inputs</em>, <em class="sig-param">begin_state=None</em>, <em class="sig-param">layout='NTC'</em>, <em class="sig-param">merge_outputs=None</em>, <em class="sig-param">valid_length=None</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.rnn.ZoneoutCell.unroll" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Unrolls an RNN cell across time steps.</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>) – Number of steps to unroll.</p></li> |
| <li><p><strong>inputs</strong> (<a class="reference internal" href="../../legacy/symbol/symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a><em>, </em><em>list of Symbol</em><em>, or </em><em>None</em>) – <p>If <cite>inputs</cite> is a single Symbol (usually the output |
| of Embedding symbol), it should have shape |
| (batch_size, length, …) if <cite>layout</cite> is ‘NTC’, |
| or (length, batch_size, …) if <cite>layout</cite> is ‘TNC’.</p> |
| <p>If <cite>inputs</cite> is a list of symbols (usually output of |
| previous unroll), they should all have shape |
| (batch_size, …).</p> |
| </p></li> |
| <li><p><strong>begin_state</strong> (<em>nested list of Symbol</em><em>, </em><em>optional</em>) – Input states created by <cite>begin_state()</cite> |
| or output state of another cell. |
| Created from <cite>begin_state()</cite> if <cite>None</cite>.</p></li> |
| <li><p><strong>layout</strong> (<em>str</em><em>, </em><em>optional</em>) – <cite>layout</cite> of input symbol. Only used if inputs |
| is a single Symbol.</p></li> |
| <li><p><strong>merge_outputs</strong> (<em>bool</em><em>, </em><em>optional</em>) – If <cite>False</cite>, returns outputs as a list of Symbols. |
| If <cite>True</cite>, concatenates output across time steps |
| and returns a single symbol with shape |
| (batch_size, length, …) if layout is ‘NTC’, |
| or (length, batch_size, …) if layout is ‘TNC’. |
| If <cite>None</cite>, output whatever is faster.</p></li> |
| <li><p><strong>valid_length</strong> (<a class="reference internal" href="../../legacy/symbol/symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a><em>, </em><a class="reference internal" href="../../legacy/ndarray/ndarray.html#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a><em> or </em><em>None</em>) – <cite>valid_length</cite> specifies the length of the sequences in the batch without padding. |
| This option is especially useful for building sequence-to-sequence models where |
| the input and output sequences would potentially be padded. |
| If <cite>valid_length</cite> is None, all sequences are assumed to have the same length. |
| If <cite>valid_length</cite> is a Symbol or NDArray, it should have shape (batch_size,). |
| The ith element will be the length of the ith sequence in the batch. |
| The last valid state will be return and the padded outputs will be masked with 0. |
| Note that <cite>valid_length</cite> must be smaller or equal to <cite>length</cite>.</p></li> |
| </ul> |
| </dd> |
| <dt class="field-even">Returns</dt> |
| <dd class="field-even"><p><ul class="simple"> |
| <li><p><strong>outputs</strong> (<em>list of Symbol or Symbol</em>) – Symbol (if <cite>merge_outputs</cite> is True) or list of Symbols |
| (if <cite>merge_outputs</cite> is False) corresponding to the output from |
| the RNN from this unrolling.</p></li> |
| <li><p><strong>states</strong> (<em>list of Symbol</em>) – The new state of this RNN after this unrolling. |
| The type of this symbol is same as the output of <cite>begin_state()</cite>.</p></li> |
| </ul> |
| </p> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.ZoneoutCell.zero_grad"> |
| <code class="sig-name descname">zero_grad</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.rnn.ZoneoutCell.zero_grad" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Sets all Parameters’ gradient buffer to 0.</p> |
| </dd></dl> |
| |
| </dd></dl> |
| |
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| <li><a class="reference internal" href="#recurrent-cells">Recurrent Cells</a></li> |
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