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| <span class="mdl-layout-title toc">Table Of Contents</span> |
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| <li class="toctree-l1"><a class="reference internal" href="../../../tutorials/index.html">Python Tutorials</a><ul> |
| <li class="toctree-l2"><a class="reference internal" href="../../../tutorials/getting-started/index.html">Getting Started</a><ul> |
| <li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/index.html">Getting started with NP on MXNet</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/1-ndarray.html">Step 1: Manipulate data with NP on MXNet</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/2-nn.html">Step 2: Create a neural network</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/3-autograd.html">Step 3: Automatic differentiation with autograd</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/4-train.html">Step 4: Train the neural network</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-predict.html">Step 5: Predict with a pretrained model</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/6-use_gpus.html">Step 6: Use GPUs to increase efficiency</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/to-mxnet/index.html">Moving to MXNet from Other Frameworks</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/to-mxnet/pytorch.html">PyTorch vs Apache MXNet</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/gluon_from_experiment_to_deployment.html">Gluon: from experiment to deployment</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/logistic_regression_explained.html">Logistic regression explained</a></li> |
| <li class="toctree-l3"><a class="reference external" href="https://mxnet.apache.org/api/python/docs/tutorials/packages/gluon/image/mnist.html">MNIST</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l2"><a class="reference internal" href="../../../tutorials/packages/index.html">Packages</a><ul> |
| <li class="toctree-l3"><a class="reference internal" href="../../../tutorials/packages/autograd/index.html">Automatic Differentiation</a></li> |
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| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/packages/gluon/blocks/index.html">Blocks</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/blocks/custom-layer.html">Custom Layers</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/blocks/custom_layer_beginners.html">Customer Layers (Beginners)</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/blocks/hybridize.html">Hybridize</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/blocks/init.html">Initialization</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/blocks/naming.html">Parameter and Block Naming</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/blocks/nn.html">Layers and Blocks</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/blocks/parameters.html">Parameter Management</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/blocks/save_load_params.html">Saving and Loading Gluon Models</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/blocks/activations/activations.html">Activation Blocks</a></li> |
| </ul> |
| </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/data_augmentation.html#Spatial-Augmentation">Spatial Augmentation</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/data/data_augmentation.html#Color-Augmentation">Color Augmentation</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/data/data_augmentation.html#Composed-Augmentations">Composed Augmentations</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/data/datasets.html">Gluon <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/data/datasets.html#Using-own-data-with-included-Datasets">Using own data with included <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/data/datasets.html#Using-own-data-with-custom-Datasets">Using own data with custom <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/data/datasets.html#Appendix:-Upgrading-from-Module-DataIter-to-Gluon-DataLoader">Appendix: Upgrading from Module <code class="docutils literal notranslate"><span class="pre">DataIter</span></code> to Gluon <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li> |
| </ul> |
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| <li class="toctree-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> |
| <li class="toctree-l6"><a class="reference internal" href="../../../tutorials/packages/legacy/ndarray/sparse/train_gluon.html">Sparse NDArrays with Gluon</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> |
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| <li class="toctree-l3"><a class="reference internal" href="../../../tutorials/packages/optimizer/index.html">Optimizers</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-l2"><a class="reference internal" href="../../../tutorials/performance/index.html">Performance</a><ul> |
| <li class="toctree-l3"><a class="reference internal" href="../../../tutorials/performance/compression/index.html">Compression</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/performance/compression/int8.html">Deploy with int-8</a></li> |
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| <li class="toctree-l4"><a class="reference external" href="https://mxnet.apache.org/api/faq/gradient_compression">Gradient Compression</a></li> |
| <li class="toctree-l4"><a class="reference external" href="https://gluon-cv.mxnet.io/build/examples_deployment/int8_inference.html">GluonCV with Quantized Models</a></li> |
| </ul> |
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| <li class="toctree-l3"><a class="reference internal" href="../../../tutorials/performance/backend/index.html">Accelerated Backend Tools</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/performance/backend/mkldnn/index.html">Intel MKL-DNN</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/performance/backend/mkldnn/mkldnn_readme.html">Install MXNet with MKL-DNN</a></li> |
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| <li class="toctree-l4"><a class="reference internal" href="../../np/routines.io.html">Input and output</a><ul> |
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| <li class="toctree-l4"><a class="reference internal" href="../../np/routines.linalg.html">Linear algebra (<code class="xref py py-mod docutils literal notranslate"><span class="pre">numpy.linalg</span></code>)</a><ul> |
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| <span class="mdl-layout-title toc">Table Of Contents</span> |
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| <li class="toctree-l1"><a class="reference internal" href="../../../tutorials/index.html">Python Tutorials</a><ul> |
| <li class="toctree-l2"><a class="reference internal" href="../../../tutorials/getting-started/index.html">Getting Started</a><ul> |
| <li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/index.html">Getting started with NP on MXNet</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/1-ndarray.html">Step 1: Manipulate data with NP on MXNet</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/2-nn.html">Step 2: Create a neural network</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/3-autograd.html">Step 3: Automatic differentiation with autograd</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/4-train.html">Step 4: Train the neural network</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-predict.html">Step 5: Predict with a pretrained model</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/6-use_gpus.html">Step 6: Use GPUs to increase efficiency</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/to-mxnet/index.html">Moving to MXNet from Other Frameworks</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/to-mxnet/pytorch.html">PyTorch vs Apache MXNet</a></li> |
| </ul> |
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| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/data/datasets.html#Appendix:-Upgrading-from-Module-DataIter-to-Gluon-DataLoader">Appendix: Upgrading from Module <code class="docutils literal notranslate"><span class="pre">DataIter</span></code> to Gluon <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li> |
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| <li class="toctree-l5"><a class="reference internal" href="../../np/generated/mxnet.np.split.html">mxnet.np.split</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../np/generated/mxnet.np.hsplit.html">mxnet.np.hsplit</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../np/generated/mxnet.np.vsplit.html">mxnet.np.vsplit</a></li> |
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| <li class="toctree-l5"><a class="reference internal" href="../../np/generated/mxnet.np.unique.html">mxnet.np.unique</a></li> |
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| <li class="toctree-l5"><a class="reference internal" href="../../np/generated/mxnet.np.flip.html">mxnet.np.flip</a></li> |
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| <li class="toctree-l4"><a class="reference internal" href="../../np/routines.io.html">Input and output</a><ul> |
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| </ul> |
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| <li class="toctree-l4"><a class="reference internal" href="../../np/routines.linalg.html">Linear algebra (<code class="xref py py-mod docutils literal notranslate"><span class="pre">numpy.linalg</span></code>)</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../np/generated/mxnet.np.dot.html">mxnet.np.dot</a></li> |
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| <li class="toctree-l4"><a class="reference internal" href="../../np/routines.math.html">Mathematical functions</a><ul> |
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| <li class="toctree-l5"><a class="reference internal" href="../../np/generated/mxnet.np.cos.html">mxnet.np.cos</a></li> |
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| <li class="toctree-l5"><a class="reference internal" href="../../np/generated/mxnet.np.cosh.html">mxnet.np.cosh</a></li> |
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| <li class="toctree-l5"><a class="reference internal" href="../../np/generated/mxnet.np.multiply.html">mxnet.np.multiply</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../np/generated/mxnet.np.true_divide.html">mxnet.np.true_divide</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../np/generated/mxnet.np.remainder.html">mxnet.np.remainder</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../np/generated/mxnet.np.clip.html">mxnet.np.clip</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../np/generated/mxnet.np.sqrt.html">mxnet.np.sqrt</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../np/generated/mxnet.np.cbrt.html">mxnet.np.cbrt</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../np/generated/mxnet.np.square.html">mxnet.np.square</a></li> |
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| <li class="toctree-l5"><a class="reference internal" href="../../np/generated/mxnet.np.sign.html">mxnet.np.sign</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../np/generated/mxnet.np.maximum.html">mxnet.np.maximum</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../np/generated/mxnet.np.minimum.html">mxnet.np.minimum</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l4"><a class="reference internal" href="../../np/random/index.html">np.random</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../np/routines.sort.html">Sorting, searching, and counting</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../np/generated/mxnet.np.argmax.html">mxnet.np.argmax</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../np/generated/mxnet.np.argmin.html">mxnet.np.argmin</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l4"><a class="reference internal" href="../../np/routines.statistics.html">Statistics</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../np/generated/mxnet.np.min.html">mxnet.np.min</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../np/generated/mxnet.np.max.html">mxnet.np.max</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../np/generated/mxnet.np.mean.html">mxnet.np.mean</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../np/generated/mxnet.np.std.html">mxnet.np.std</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../np/generated/mxnet.np.var.html">mxnet.np.var</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../np/generated/mxnet.np.histogram.html">mxnet.np.histogram</a></li> |
| </ul> |
| </li> |
| </ul> |
| </li> |
| </ul> |
| </li> |
| <li class="toctree-l2"><a class="reference internal" href="../../npx/index.html">NPX: NumPy Neural Network Extension</a><ul> |
| <li class="toctree-l3"><a class="reference internal" href="../../npx/generated/mxnet.npx.set_np.html">mxnet.npx.set_np</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../npx/generated/mxnet.npx.reset_np.html">mxnet.npx.reset_np</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../npx/generated/mxnet.npx.cpu.html">mxnet.npx.cpu</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../npx/generated/mxnet.npx.cpu_pinned.html">mxnet.npx.cpu_pinned</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../npx/generated/mxnet.npx.gpu.html">mxnet.npx.gpu</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../npx/generated/mxnet.npx.gpu_memory_info.html">mxnet.npx.gpu_memory_info</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../npx/generated/mxnet.npx.current_context.html">mxnet.npx.current_context</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../npx/generated/mxnet.npx.num_gpus.html">mxnet.npx.num_gpus</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../npx/generated/mxnet.npx.activation.html">mxnet.npx.activation</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../npx/generated/mxnet.npx.batch_norm.html">mxnet.npx.batch_norm</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../npx/generated/mxnet.npx.convolution.html">mxnet.npx.convolution</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../npx/generated/mxnet.npx.dropout.html">mxnet.npx.dropout</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../npx/generated/mxnet.npx.embedding.html">mxnet.npx.embedding</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../npx/generated/mxnet.npx.fully_connected.html">mxnet.npx.fully_connected</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../npx/generated/mxnet.npx.layer_norm.html">mxnet.npx.layer_norm</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../npx/generated/mxnet.npx.pooling.html">mxnet.npx.pooling</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../npx/generated/mxnet.npx.rnn.html">mxnet.npx.rnn</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../npx/generated/mxnet.npx.leaky_relu.html">mxnet.npx.leaky_relu</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../npx/generated/mxnet.npx.multibox_detection.html">mxnet.npx.multibox_detection</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../npx/generated/mxnet.npx.multibox_prior.html">mxnet.npx.multibox_prior</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../npx/generated/mxnet.npx.multibox_target.html">mxnet.npx.multibox_target</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../npx/generated/mxnet.npx.roi_pooling.html">mxnet.npx.roi_pooling</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../npx/generated/mxnet.npx.sigmoid.html">mxnet.npx.sigmoid</a></li> |
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| <div class="document"> |
| <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> |
| <tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">mxnet.gluon.contrib.rnn</span></code></p></td> |
| <td><p></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-prename descclassname">mxnet.gluon.rnn.</code><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.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-even"><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-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> |
| </tbody> |
| </table> |
| <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.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.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> |
| |
| </dd></dl> |
| |
| <dl class="class"> |
| <dt id="mxnet.gluon.rnn.Conv1DGRUCell"> |
| <em class="property">class </em><code class="sig-prename descclassname">mxnet.gluon.rnn.</code><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-prename descclassname">mxnet.gluon.rnn.</code><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-prename descclassname">mxnet.gluon.rnn.</code><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-prename descclassname">mxnet.gluon.rnn.</code><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-prename descclassname">mxnet.gluon.rnn.</code><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-prename descclassname">mxnet.gluon.rnn.</code><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-prename descclassname">mxnet.gluon.rnn.</code><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-prename descclassname">mxnet.gluon.rnn.</code><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-prename descclassname">mxnet.gluon.rnn.</code><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-prename descclassname">mxnet.gluon.rnn.</code><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.hybrid_forward" title="mxnet.gluon.rnn.DropoutCell.hybrid_forward"><code class="xref py py-obj docutils literal notranslate"><span class="pre">hybrid_forward</span></code></a>(F, inputs, states)</p></td> |
| <td><p>Overrides to construct symbolic graph for this <cite>Block</cite>.</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.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> |
| </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.hybrid_forward"> |
| <code class="sig-name descname">hybrid_forward</code><span class="sig-paren">(</span><em class="sig-param">F</em>, <em class="sig-param">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.hybrid_forward"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.gluon.rnn.DropoutCell.hybrid_forward" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Overrides to construct symbolic graph for this <cite>Block</cite>.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>x</strong> (<a class="reference internal" href="../../legacy/symbol/symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a><em> or </em><a class="reference internal" href="../../legacy/ndarray/ndarray.html#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – The first input tensor.</p></li> |
| <li><p><strong>*args</strong> (<em>list of Symbol</em><em> or </em><em>list of NDArray</em>) – Additional input tensors.</p></li> |
| </ul> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <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.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> |
| |
| </dd></dl> |
| |
| <dl class="class"> |
| <dt id="mxnet.gluon.rnn.GRU"> |
| <em class="property">class </em><code class="sig-prename descclassname">mxnet.gluon.rnn.</code><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">nd</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">shape</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">nd</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">shape</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-prename descclassname">mxnet.gluon.rnn.</code><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.hybrid_forward" title="mxnet.gluon.rnn.GRUCell.hybrid_forward"><code class="xref py py-obj docutils literal notranslate"><span class="pre">hybrid_forward</span></code></a>(F, inputs, states, …)</p></td> |
| <td><p>Overrides to construct symbolic graph for this <cite>Block</cite>.</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> |
| </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.hybrid_forward"> |
| <code class="sig-name descname">hybrid_forward</code><span class="sig-paren">(</span><em class="sig-param">F</em>, <em class="sig-param">inputs</em>, <em class="sig-param">states</em>, <em class="sig-param">i2h_weight</em>, <em class="sig-param">h2h_weight</em>, <em class="sig-param">i2h_bias</em>, <em class="sig-param">h2h_bias</em><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/mxnet/gluon/rnn/rnn_cell.html#GRUCell.hybrid_forward"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.gluon.rnn.GRUCell.hybrid_forward" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Overrides to construct symbolic graph for this <cite>Block</cite>.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>x</strong> (<a class="reference internal" href="../../legacy/symbol/symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a><em> or </em><a class="reference internal" href="../../legacy/ndarray/ndarray.html#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – The first input tensor.</p></li> |
| <li><p><strong>*args</strong> (<em>list of Symbol</em><em> or </em><em>list of NDArray</em>) – Additional input tensors.</p></li> |
| </ul> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <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> |
| |
| </dd></dl> |
| |
| <dl class="class"> |
| <dt id="mxnet.gluon.rnn.HybridRecurrentCell"> |
| <em class="property">class </em><code class="sig-prename descclassname">mxnet.gluon.rnn.</code><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.hybrid_forward" title="mxnet.gluon.rnn.HybridRecurrentCell.hybrid_forward"><code class="xref py py-obj docutils literal notranslate"><span class="pre">hybrid_forward</span></code></a>(F, x, *args, **kwargs)</p></td> |
| <td><p>Overrides to construct symbolic graph for this <cite>Block</cite>.</p></td> |
| </tr> |
| </tbody> |
| </table> |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.HybridRecurrentCell.hybrid_forward"> |
| <code class="sig-name descname">hybrid_forward</code><span class="sig-paren">(</span><em class="sig-param">F</em>, <em class="sig-param">x</em>, <em class="sig-param">*args</em>, <em class="sig-param">**kwargs</em><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/mxnet/gluon/rnn/rnn_cell.html#HybridRecurrentCell.hybrid_forward"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.gluon.rnn.HybridRecurrentCell.hybrid_forward" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Overrides to construct symbolic graph for this <cite>Block</cite>.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>x</strong> (<a class="reference internal" href="../../legacy/symbol/symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a><em> or </em><a class="reference internal" href="../../legacy/ndarray/ndarray.html#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – The first input tensor.</p></li> |
| <li><p><strong>*args</strong> (<em>list of Symbol</em><em> or </em><em>list of NDArray</em>) – Additional input tensors.</p></li> |
| </ul> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| </dd></dl> |
| |
| <dl class="class"> |
| <dt id="mxnet.gluon.rnn.HybridSequentialRNNCell"> |
| <em class="property">class </em><code class="sig-prename descclassname">mxnet.gluon.rnn.</code><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.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-odd"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.HybridSequentialRNNCell.hybrid_forward" title="mxnet.gluon.rnn.HybridSequentialRNNCell.hybrid_forward"><code class="xref py py-obj docutils literal notranslate"><span class="pre">hybrid_forward</span></code></a>(F, inputs, states)</p></td> |
| <td><p>Overrides to construct symbolic graph for this <cite>Block</cite>.</p></td> |
| </tr> |
| <tr class="row-even"><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-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> |
| </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.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.hybrid_forward"> |
| <code class="sig-name descname">hybrid_forward</code><span class="sig-paren">(</span><em class="sig-param">F</em>, <em class="sig-param">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.hybrid_forward"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.gluon.rnn.HybridSequentialRNNCell.hybrid_forward" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Overrides to construct symbolic graph for this <cite>Block</cite>.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>x</strong> (<a class="reference internal" href="../../legacy/symbol/symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a><em> or </em><a class="reference internal" href="../../legacy/ndarray/ndarray.html#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – The first input tensor.</p></li> |
| <li><p><strong>*args</strong> (<em>list of Symbol</em><em> or </em><em>list of NDArray</em>) – Additional input tensors.</p></li> |
| </ul> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <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.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> |
| |
| </dd></dl> |
| |
| <dl class="class"> |
| <dt id="mxnet.gluon.rnn.LSTM"> |
| <em class="property">class </em><code class="sig-prename descclassname">mxnet.gluon.rnn.</code><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">nd</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">shape</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">nd</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">shape</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">nd</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">shape</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-prename descclassname">mxnet.gluon.rnn.</code><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.hybrid_forward" title="mxnet.gluon.rnn.LSTMCell.hybrid_forward"><code class="xref py py-obj docutils literal notranslate"><span class="pre">hybrid_forward</span></code></a>(F, inputs, states, …)</p></td> |
| <td><p>Overrides to construct symbolic graph for this <cite>Block</cite>.</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> |
| </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.hybrid_forward"> |
| <code class="sig-name descname">hybrid_forward</code><span class="sig-paren">(</span><em class="sig-param">F</em>, <em class="sig-param">inputs</em>, <em class="sig-param">states</em>, <em class="sig-param">i2h_weight</em>, <em class="sig-param">h2h_weight</em>, <em class="sig-param">i2h_bias</em>, <em class="sig-param">h2h_bias</em><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/mxnet/gluon/rnn/rnn_cell.html#LSTMCell.hybrid_forward"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.gluon.rnn.LSTMCell.hybrid_forward" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Overrides to construct symbolic graph for this <cite>Block</cite>.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>x</strong> (<a class="reference internal" href="../../legacy/symbol/symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a><em> or </em><a class="reference internal" href="../../legacy/ndarray/ndarray.html#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – The first input tensor.</p></li> |
| <li><p><strong>*args</strong> (<em>list of Symbol</em><em> or </em><em>list of NDArray</em>) – Additional input tensors.</p></li> |
| </ul> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <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> |
| |
| </dd></dl> |
| |
| <dl class="class"> |
| <dt id="mxnet.gluon.rnn.LSTMPCell"> |
| <em class="property">class </em><code class="sig-prename descclassname">mxnet.gluon.rnn.</code><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.hybrid_forward" title="mxnet.gluon.rnn.LSTMPCell.hybrid_forward"><code class="xref py py-obj docutils literal notranslate"><span class="pre">hybrid_forward</span></code></a>(F, inputs, states, …)</p></td> |
| <td><p>Overrides to construct symbolic graph for this <cite>Block</cite>.</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> |
| </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.hybrid_forward"> |
| <code class="sig-name descname">hybrid_forward</code><span class="sig-paren">(</span><em class="sig-param">F</em>, <em class="sig-param">inputs</em>, <em class="sig-param">states</em>, <em class="sig-param">i2h_weight</em>, <em class="sig-param">h2h_weight</em>, <em class="sig-param">h2r_weight</em>, <em class="sig-param">i2h_bias</em>, <em class="sig-param">h2h_bias</em><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/mxnet/gluon/rnn/rnn_cell.html#LSTMPCell.hybrid_forward"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.gluon.rnn.LSTMPCell.hybrid_forward" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Overrides to construct symbolic graph for this <cite>Block</cite>.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>x</strong> (<a class="reference internal" href="../../legacy/symbol/symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a><em> or </em><a class="reference internal" href="../../legacy/ndarray/ndarray.html#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – The first input tensor.</p></li> |
| <li><p><strong>*args</strong> (<em>list of Symbol</em><em> or </em><em>list of NDArray</em>) – Additional input tensors.</p></li> |
| </ul> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <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> |
| |
| </dd></dl> |
| |
| <dl class="class"> |
| <dt id="mxnet.gluon.rnn.ModifierCell"> |
| <em class="property">class </em><code class="sig-prename descclassname">mxnet.gluon.rnn.</code><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.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-even"><td><p><a class="reference internal" href="#mxnet.gluon.rnn.ModifierCell.hybrid_forward" title="mxnet.gluon.rnn.ModifierCell.hybrid_forward"><code class="xref py py-obj docutils literal notranslate"><span class="pre">hybrid_forward</span></code></a>(F, inputs, states)</p></td> |
| <td><p>Overrides to construct symbolic graph for this <cite>Block</cite>.</p></td> |
| </tr> |
| <tr class="row-odd"><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> |
| </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>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.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.hybrid_forward"> |
| <code class="sig-name descname">hybrid_forward</code><span class="sig-paren">(</span><em class="sig-param">F</em>, <em class="sig-param">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.hybrid_forward"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.gluon.rnn.ModifierCell.hybrid_forward" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Overrides to construct symbolic graph for this <cite>Block</cite>.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>x</strong> (<a class="reference internal" href="../../legacy/symbol/symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a><em> or </em><a class="reference internal" href="../../legacy/ndarray/ndarray.html#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – The first input tensor.</p></li> |
| <li><p><strong>*args</strong> (<em>list of Symbol</em><em> or </em><em>list of NDArray</em>) – Additional input tensors.</p></li> |
| </ul> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <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>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.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> |
| |
| </dd></dl> |
| |
| <dl class="class"> |
| <dt id="mxnet.gluon.rnn.RNN"> |
| <em class="property">class </em><code class="sig-prename descclassname">mxnet.gluon.rnn.</code><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">nd</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">shape</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">nd</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">shape</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-prename descclassname">mxnet.gluon.rnn.</code><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.hybrid_forward" title="mxnet.gluon.rnn.RNNCell.hybrid_forward"><code class="xref py py-obj docutils literal notranslate"><span class="pre">hybrid_forward</span></code></a>(F, inputs, states, …)</p></td> |
| <td><p>Overrides to construct symbolic graph for this <cite>Block</cite>.</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> |
| </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.hybrid_forward"> |
| <code class="sig-name descname">hybrid_forward</code><span class="sig-paren">(</span><em class="sig-param">F</em>, <em class="sig-param">inputs</em>, <em class="sig-param">states</em>, <em class="sig-param">i2h_weight</em>, <em class="sig-param">h2h_weight</em>, <em class="sig-param">i2h_bias</em>, <em class="sig-param">h2h_bias</em><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/mxnet/gluon/rnn/rnn_cell.html#RNNCell.hybrid_forward"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.gluon.rnn.RNNCell.hybrid_forward" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Overrides to construct symbolic graph for this <cite>Block</cite>.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>x</strong> (<a class="reference internal" href="../../legacy/symbol/symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a><em> or </em><a class="reference internal" href="../../legacy/ndarray/ndarray.html#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – The first input tensor.</p></li> |
| <li><p><strong>*args</strong> (<em>list of Symbol</em><em> or </em><em>list of NDArray</em>) – Additional input tensors.</p></li> |
| </ul> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <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> |
| |
| </dd></dl> |
| |
| <dl class="class"> |
| <dt id="mxnet.gluon.rnn.RecurrentCell"> |
| <em class="property">class </em><code class="sig-prename descclassname">mxnet.gluon.rnn.</code><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.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-even"><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-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.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.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> |
| </tbody> |
| </table> |
| <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.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.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.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.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> |
| |
| </dd></dl> |
| |
| <dl class="class"> |
| <dt id="mxnet.gluon.rnn.ResidualCell"> |
| <em class="property">class </em><code class="sig-prename descclassname">mxnet.gluon.rnn.</code><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.hybrid_forward" title="mxnet.gluon.rnn.ResidualCell.hybrid_forward"><code class="xref py py-obj docutils literal notranslate"><span class="pre">hybrid_forward</span></code></a>(F, inputs, states)</p></td> |
| <td><p>Overrides to construct symbolic graph for this <cite>Block</cite>.</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> |
| </tbody> |
| </table> |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.ResidualCell.hybrid_forward"> |
| <code class="sig-name descname">hybrid_forward</code><span class="sig-paren">(</span><em class="sig-param">F</em>, <em class="sig-param">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.hybrid_forward"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.gluon.rnn.ResidualCell.hybrid_forward" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Overrides to construct symbolic graph for this <cite>Block</cite>.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>x</strong> (<a class="reference internal" href="../../legacy/symbol/symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a><em> or </em><a class="reference internal" href="../../legacy/ndarray/ndarray.html#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – The first input tensor.</p></li> |
| <li><p><strong>*args</strong> (<em>list of Symbol</em><em> or </em><em>list of NDArray</em>) – Additional input tensors.</p></li> |
| </ul> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <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> |
| |
| </dd></dl> |
| |
| <dl class="class"> |
| <dt id="mxnet.gluon.rnn.SequentialRNNCell"> |
| <em class="property">class </em><code class="sig-prename descclassname">mxnet.gluon.rnn.</code><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.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-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.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> |
| </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.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.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.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> |
| |
| </dd></dl> |
| |
| <dl class="class"> |
| <dt id="mxnet.gluon.rnn.VariationalDropoutCell"> |
| <em class="property">class </em><code class="sig-prename descclassname">mxnet.gluon.rnn.</code><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.hybrid_forward" title="mxnet.gluon.rnn.VariationalDropoutCell.hybrid_forward"><code class="xref py py-obj docutils literal notranslate"><span class="pre">hybrid_forward</span></code></a>(F, inputs, states)</p></td> |
| <td><p>Overrides to construct symbolic graph for this <cite>Block</cite>.</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.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> |
| </tbody> |
| </table> |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.VariationalDropoutCell.hybrid_forward"> |
| <code class="sig-name descname">hybrid_forward</code><span class="sig-paren">(</span><em class="sig-param">F</em>, <em class="sig-param">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.hybrid_forward"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.gluon.rnn.VariationalDropoutCell.hybrid_forward" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Overrides to construct symbolic graph for this <cite>Block</cite>.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>x</strong> (<a class="reference internal" href="../../legacy/symbol/symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a><em> or </em><a class="reference internal" href="../../legacy/ndarray/ndarray.html#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – The first input tensor.</p></li> |
| <li><p><strong>*args</strong> (<em>list of Symbol</em><em> or </em><em>list of NDArray</em>) – Additional input tensors.</p></li> |
| </ul> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <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.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> |
| |
| </dd></dl> |
| |
| <dl class="class"> |
| <dt id="mxnet.gluon.rnn.ZoneoutCell"> |
| <em class="property">class </em><code class="sig-prename descclassname">mxnet.gluon.rnn.</code><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.hybrid_forward" title="mxnet.gluon.rnn.ZoneoutCell.hybrid_forward"><code class="xref py py-obj docutils literal notranslate"><span class="pre">hybrid_forward</span></code></a>(F, inputs, states)</p></td> |
| <td><p>Overrides to construct symbolic graph for this <cite>Block</cite>.</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> |
| </tbody> |
| </table> |
| <dl class="method"> |
| <dt id="mxnet.gluon.rnn.ZoneoutCell.hybrid_forward"> |
| <code class="sig-name descname">hybrid_forward</code><span class="sig-paren">(</span><em class="sig-param">F</em>, <em class="sig-param">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.hybrid_forward"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.gluon.rnn.ZoneoutCell.hybrid_forward" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Overrides to construct symbolic graph for this <cite>Block</cite>.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>x</strong> (<a class="reference internal" href="../../legacy/symbol/symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a><em> or </em><a class="reference internal" href="../../legacy/ndarray/ndarray.html#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – The first input tensor.</p></li> |
| <li><p><strong>*args</strong> (<em>list of Symbol</em><em> or </em><em>list of NDArray</em>) – Additional input tensors.</p></li> |
| </ul> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <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> |
| |
| </dd></dl> |
| |
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| <li><a class="reference internal" href="#recurrent-cells">Recurrent Cells</a></li> |
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