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| <li class="toctree-l3"><a class="reference external" href="https://mxnet.apache.org/api/python/docs/tutorials/packages/gluon/image/mnist.html">MNIST</a></li> |
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| <li class="toctree-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> |
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| <li class="toctree-l5"><a class="reference internal" href="../../../../tutorials/packages/gluon/image/info_gan.html">Image similarity search with InfoGAN</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../tutorials/packages/gluon/image/mnist.html">Handwritten Digit Recognition</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../tutorials/packages/gluon/image/pretrained_models.html">Using pre-trained models in MXNet</a></li> |
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| <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> |
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| <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> |
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| <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> |
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| <li class="toctree-l5"><a class="reference internal" href="../../../../tutorials/packages/gluon/training/normalization/index.html">Normalization Blocks</a></li> |
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| <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> |
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| <li class="toctree-l3"><a class="reference internal" href="../../../../tutorials/packages/ndarray/index.html">NDArray</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/packages/ndarray/01-ndarray-intro.html">An Intro: Manipulate Data the MXNet Way with NDArray</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/packages/ndarray/02-ndarray-operations.html">NDArray Operations</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/packages/ndarray/03-ndarray-contexts.html">NDArray Contexts</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/packages/ndarray/gotchas_numpy_in_mxnet.html">Gotchas using NumPy in Apache MXNet</a></li> |
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| <li class="toctree-l5"><a class="reference internal" href="../../../../tutorials/packages/ndarray/sparse/csr.html">CSRNDArray - NDArray in Compressed Sparse Row Storage Format</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../tutorials/packages/ndarray/sparse/row_sparse.html">RowSparseNDArray - NDArray for Sparse Gradient Updates</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../tutorials/packages/ndarray/sparse/train.html">Train a Linear Regression Model with Sparse Symbols</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../tutorials/packages/ndarray/sparse/train_gluon.html">Sparse NDArrays with Gluon</a></li> |
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| <li class="toctree-l1"><a class="reference internal" href="../../../../tutorials/index.html">Python Tutorials</a><ul> |
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| <li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/1-ndarray.html">Manipulate data with <code class="docutils literal notranslate"><span class="pre">ndarray</span></code></a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/2-nn.html">Create a neural network</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/3-autograd.html">Automatic differentiation with <code class="docutils literal notranslate"><span class="pre">autograd</span></code></a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/4-train.html">Train the neural network</a></li> |
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| <li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/6-use_gpus.html">Use GPUs</a></li> |
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| <li class="toctree-l3"><a class="reference internal" href="../../../../tutorials/getting-started/to-mxnet/index.html">Moving to MXNet from Other Frameworks</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/to-mxnet/pytorch.html">PyTorch vs Apache MXNet</a></li> |
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| <li class="toctree-l3"><a class="reference internal" href="../../../../tutorials/getting-started/gluon_from_experiment_to_deployment.html">Gluon: from experiment to deployment</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../../tutorials/getting-started/logistic_regression_explained.html">Logistic regression explained</a></li> |
| <li class="toctree-l3"><a class="reference external" href="https://mxnet.apache.org/api/python/docs/tutorials/packages/gluon/image/mnist.html">MNIST</a></li> |
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| <li class="toctree-l3"><a class="reference internal" href="../../../../tutorials/packages/autograd/index.html">Automatic Differentiation</a></li> |
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| <li class="toctree-l5"><a class="reference internal" href="../../../../tutorials/packages/gluon/blocks/custom_layer_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> |
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| <li class="toctree-l5"><a class="reference internal" href="../../../../tutorials/packages/gluon/blocks/naming.html">Parameter and Block Naming</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../tutorials/packages/gluon/blocks/nn.html">Layers and Blocks</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../tutorials/packages/gluon/blocks/parameters.html">Parameter Management</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../tutorials/packages/gluon/blocks/save_load_params.html">Saving and Loading Gluon Models</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../tutorials/packages/gluon/blocks/activations/activations.html">Activation Blocks</a></li> |
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| <li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/packages/gluon/data/index.html">Data Tutorials</a><ul> |
| <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> |
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| <li class="toctree-l5"><a class="reference internal" href="../../../../tutorials/packages/gluon/image/image-augmentation.html">Image Augmentation</a></li> |
| <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> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../tutorials/packages/gluon/image/pretrained_models.html">Using pre-trained models in MXNet</a></li> |
| </ul> |
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| <h1>Source code for mxnet.gluon.rnn.rnn_cell</h1><div class="highlight"><pre> |
| <span></span><span class="c1"># Licensed to the Apache Software Foundation (ASF) under one</span> |
| <span class="c1"># or more contributor license agreements. See the NOTICE file</span> |
| <span class="c1"># distributed with this work for additional information</span> |
| <span class="c1"># regarding copyright ownership. The ASF licenses this file</span> |
| <span class="c1"># to you under the Apache License, Version 2.0 (the</span> |
| <span class="c1"># "License"); you may not use this file except in compliance</span> |
| <span class="c1"># with the License. You may obtain a copy of the License at</span> |
| <span class="c1">#</span> |
| <span class="c1"># http://www.apache.org/licenses/LICENSE-2.0</span> |
| <span class="c1">#</span> |
| <span class="c1"># Unless required by applicable law or agreed to in writing,</span> |
| <span class="c1"># software distributed under the License is distributed on an</span> |
| <span class="c1"># "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY</span> |
| <span class="c1"># KIND, either express or implied. See the License for the</span> |
| <span class="c1"># specific language governing permissions and limitations</span> |
| <span class="c1"># under the License.</span> |
| |
| <span class="c1"># coding: utf-8</span> |
| <span class="c1"># pylint: disable=no-member, invalid-name, protected-access, no-self-use</span> |
| <span class="c1"># pylint: disable=too-many-branches, too-many-arguments, no-self-use</span> |
| <span class="c1"># pylint: disable=too-many-lines, arguments-differ</span> |
| <span class="sd">"""Definition of various recurrent neural network cells."""</span> |
| <span class="n">__all__</span> <span class="o">=</span> <span class="p">[</span><span class="s1">'RecurrentCell'</span><span class="p">,</span> <span class="s1">'HybridRecurrentCell'</span><span class="p">,</span> |
| <span class="s1">'RNNCell'</span><span class="p">,</span> <span class="s1">'LSTMCell'</span><span class="p">,</span> <span class="s1">'GRUCell'</span><span class="p">,</span> |
| <span class="s1">'SequentialRNNCell'</span><span class="p">,</span> <span class="s1">'HybridSequentialRNNCell'</span><span class="p">,</span> <span class="s1">'DropoutCell'</span><span class="p">,</span> |
| <span class="s1">'ModifierCell'</span><span class="p">,</span> <span class="s1">'ZoneoutCell'</span><span class="p">,</span> <span class="s1">'ResidualCell'</span><span class="p">,</span> |
| <span class="s1">'BidirectionalCell'</span><span class="p">]</span> |
| |
| <span class="kn">from</span> <span class="nn">...</span> <span class="kn">import</span> <span class="n">symbol</span><span class="p">,</span> <span class="n">ndarray</span> |
| <span class="kn">from</span> <span class="nn">...base</span> <span class="kn">import</span> <span class="n">string_types</span><span class="p">,</span> <span class="n">numeric_types</span><span class="p">,</span> <span class="n">_as_list</span> |
| <span class="kn">from</span> <span class="nn">..block</span> <span class="kn">import</span> <span class="n">Block</span><span class="p">,</span> <span class="n">HybridBlock</span> |
| <span class="kn">from</span> <span class="nn">..utils</span> <span class="kn">import</span> <span class="n">_indent</span> |
| <span class="kn">from</span> <span class="nn">..</span> <span class="kn">import</span> <span class="n">tensor_types</span> |
| <span class="kn">from</span> <span class="nn">..nn</span> <span class="kn">import</span> <span class="n">LeakyReLU</span> |
| |
| |
| <span class="k">def</span> <span class="nf">_cells_state_info</span><span class="p">(</span><span class="n">cells</span><span class="p">,</span> <span class="n">batch_size</span><span class="p">):</span> |
| <span class="k">return</span> <span class="nb">sum</span><span class="p">([</span><span class="n">c</span><span class="o">.</span><span class="n">state_info</span><span class="p">(</span><span class="n">batch_size</span><span class="p">)</span> <span class="k">for</span> <span class="n">c</span> <span class="ow">in</span> <span class="n">cells</span><span class="p">],</span> <span class="p">[])</span> |
| |
| <span class="k">def</span> <span class="nf">_cells_begin_state</span><span class="p">(</span><span class="n">cells</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span> |
| <span class="k">return</span> <span class="nb">sum</span><span class="p">([</span><span class="n">c</span><span class="o">.</span><span class="n">begin_state</span><span class="p">(</span><span class="o">**</span><span class="n">kwargs</span><span class="p">)</span> <span class="k">for</span> <span class="n">c</span> <span class="ow">in</span> <span class="n">cells</span><span class="p">],</span> <span class="p">[])</span> |
| |
| <span class="k">def</span> <span class="nf">_get_begin_state</span><span class="p">(</span><span class="n">cell</span><span class="p">,</span> <span class="n">F</span><span class="p">,</span> <span class="n">begin_state</span><span class="p">,</span> <span class="n">inputs</span><span class="p">,</span> <span class="n">batch_size</span><span class="p">):</span> |
| <span class="k">if</span> <span class="n">begin_state</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span> |
| <span class="k">if</span> <span class="n">F</span> <span class="ow">is</span> <span class="n">ndarray</span><span class="p">:</span> |
| <span class="n">ctx</span> <span class="o">=</span> <span class="n">inputs</span><span class="o">.</span><span class="n">context</span> <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">inputs</span><span class="p">,</span> <span class="n">tensor_types</span><span class="p">)</span> <span class="k">else</span> <span class="n">inputs</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">context</span> |
| <span class="k">with</span> <span class="n">ctx</span><span class="p">:</span> |
| <span class="n">begin_state</span> <span class="o">=</span> <span class="n">cell</span><span class="o">.</span><span class="n">begin_state</span><span class="p">(</span><span class="n">func</span><span class="o">=</span><span class="n">F</span><span class="o">.</span><span class="n">zeros</span><span class="p">,</span> <span class="n">batch_size</span><span class="o">=</span><span class="n">batch_size</span><span class="p">)</span> |
| <span class="k">else</span><span class="p">:</span> |
| <span class="n">begin_state</span> <span class="o">=</span> <span class="n">cell</span><span class="o">.</span><span class="n">begin_state</span><span class="p">(</span><span class="n">func</span><span class="o">=</span><span class="n">F</span><span class="o">.</span><span class="n">zeros</span><span class="p">,</span> <span class="n">batch_size</span><span class="o">=</span><span class="n">batch_size</span><span class="p">)</span> |
| <span class="k">return</span> <span class="n">begin_state</span> |
| |
| <span class="k">def</span> <span class="nf">_format_sequence</span><span class="p">(</span><span class="n">length</span><span class="p">,</span> <span class="n">inputs</span><span class="p">,</span> <span class="n">layout</span><span class="p">,</span> <span class="n">merge</span><span class="p">,</span> <span class="n">in_layout</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> |
| <span class="k">assert</span> <span class="n">inputs</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">,</span> \ |
| <span class="s2">"unroll(inputs=None) has been deprecated. "</span> \ |
| <span class="s2">"Please create input variables outside unroll."</span> |
| |
| <span class="n">axis</span> <span class="o">=</span> <span class="n">layout</span><span class="o">.</span><span class="n">find</span><span class="p">(</span><span class="s1">'T'</span><span class="p">)</span> |
| <span class="n">batch_axis</span> <span class="o">=</span> <span class="n">layout</span><span class="o">.</span><span class="n">find</span><span class="p">(</span><span class="s1">'N'</span><span class="p">)</span> |
| <span class="n">batch_size</span> <span class="o">=</span> <span class="mi">0</span> |
| <span class="n">in_axis</span> <span class="o">=</span> <span class="n">in_layout</span><span class="o">.</span><span class="n">find</span><span class="p">(</span><span class="s1">'T'</span><span class="p">)</span> <span class="k">if</span> <span class="n">in_layout</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="k">else</span> <span class="n">axis</span> |
| <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">inputs</span><span class="p">,</span> <span class="n">symbol</span><span class="o">.</span><span class="n">Symbol</span><span class="p">):</span> |
| <span class="n">F</span> <span class="o">=</span> <span class="n">symbol</span> |
| <span class="k">if</span> <span class="n">merge</span> <span class="ow">is</span> <span class="kc">False</span><span class="p">:</span> |
| <span class="k">assert</span> <span class="nb">len</span><span class="p">(</span><span class="n">inputs</span><span class="o">.</span><span class="n">list_outputs</span><span class="p">())</span> <span class="o">==</span> <span class="mi">1</span><span class="p">,</span> \ |
| <span class="s2">"unroll doesn't allow grouped symbol as input. Please convert "</span> \ |
| <span class="s2">"to list with list(inputs) first or let unroll handle splitting."</span> |
| <span class="n">inputs</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="n">symbol</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="n">inputs</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="n">in_axis</span><span class="p">,</span> <span class="n">num_outputs</span><span class="o">=</span><span class="n">length</span><span class="p">,</span> |
| <span class="n">squeeze_axis</span><span class="o">=</span><span class="mi">1</span><span class="p">))</span> |
| <span class="k">elif</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">inputs</span><span class="p">,</span> <span class="n">ndarray</span><span class="o">.</span><span class="n">NDArray</span><span class="p">):</span> |
| <span class="n">F</span> <span class="o">=</span> <span class="n">ndarray</span> |
| <span class="n">batch_size</span> <span class="o">=</span> <span class="n">inputs</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="n">batch_axis</span><span class="p">]</span> |
| <span class="k">if</span> <span class="n">merge</span> <span class="ow">is</span> <span class="kc">False</span><span class="p">:</span> |
| <span class="k">assert</span> <span class="n">length</span> <span class="ow">is</span> <span class="kc">None</span> <span class="ow">or</span> <span class="n">length</span> <span class="o">==</span> <span class="n">inputs</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="n">in_axis</span><span class="p">]</span> |
| <span class="n">inputs</span> <span class="o">=</span> <span class="n">_as_list</span><span class="p">(</span><span class="n">ndarray</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="n">inputs</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="n">in_axis</span><span class="p">,</span> |
| <span class="n">num_outputs</span><span class="o">=</span><span class="n">inputs</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="n">in_axis</span><span class="p">],</span> |
| <span class="n">squeeze_axis</span><span class="o">=</span><span class="mi">1</span><span class="p">))</span> |
| <span class="k">else</span><span class="p">:</span> |
| <span class="k">assert</span> <span class="n">length</span> <span class="ow">is</span> <span class="kc">None</span> <span class="ow">or</span> <span class="nb">len</span><span class="p">(</span><span class="n">inputs</span><span class="p">)</span> <span class="o">==</span> <span class="n">length</span> |
| <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">inputs</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">symbol</span><span class="o">.</span><span class="n">Symbol</span><span class="p">):</span> |
| <span class="n">F</span> <span class="o">=</span> <span class="n">symbol</span> |
| <span class="k">else</span><span class="p">:</span> |
| <span class="n">F</span> <span class="o">=</span> <span class="n">ndarray</span> |
| <span class="n">batch_size</span> <span class="o">=</span> <span class="n">inputs</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> |
| <span class="k">if</span> <span class="n">merge</span> <span class="ow">is</span> <span class="kc">True</span><span class="p">:</span> |
| <span class="n">inputs</span> <span class="o">=</span> <span class="n">F</span><span class="o">.</span><span class="n">stack</span><span class="p">(</span><span class="o">*</span><span class="n">inputs</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="n">axis</span><span class="p">)</span> |
| <span class="n">in_axis</span> <span class="o">=</span> <span class="n">axis</span> |
| |
| <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">inputs</span><span class="p">,</span> <span class="n">tensor_types</span><span class="p">)</span> <span class="ow">and</span> <span class="n">axis</span> <span class="o">!=</span> <span class="n">in_axis</span><span class="p">:</span> |
| <span class="n">inputs</span> <span class="o">=</span> <span class="n">F</span><span class="o">.</span><span class="n">swapaxes</span><span class="p">(</span><span class="n">inputs</span><span class="p">,</span> <span class="n">dim1</span><span class="o">=</span><span class="n">axis</span><span class="p">,</span> <span class="n">dim2</span><span class="o">=</span><span class="n">in_axis</span><span class="p">)</span> |
| |
| <span class="k">return</span> <span class="n">inputs</span><span class="p">,</span> <span class="n">axis</span><span class="p">,</span> <span class="n">F</span><span class="p">,</span> <span class="n">batch_size</span> |
| |
| <span class="k">def</span> <span class="nf">_mask_sequence_variable_length</span><span class="p">(</span><span class="n">F</span><span class="p">,</span> <span class="n">data</span><span class="p">,</span> <span class="n">length</span><span class="p">,</span> <span class="n">valid_length</span><span class="p">,</span> <span class="n">time_axis</span><span class="p">,</span> <span class="n">merge</span><span class="p">):</span> |
| <span class="k">assert</span> <span class="n">valid_length</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> |
| <span class="k">if</span> <span class="ow">not</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">tensor_types</span><span class="p">):</span> |
| <span class="n">data</span> <span class="o">=</span> <span class="n">F</span><span class="o">.</span><span class="n">stack</span><span class="p">(</span><span class="o">*</span><span class="n">data</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="n">time_axis</span><span class="p">)</span> |
| <span class="n">outputs</span> <span class="o">=</span> <span class="n">F</span><span class="o">.</span><span class="n">SequenceMask</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">sequence_length</span><span class="o">=</span><span class="n">valid_length</span><span class="p">,</span> <span class="n">use_sequence_length</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> |
| <span class="n">axis</span><span class="o">=</span><span class="n">time_axis</span><span class="p">)</span> |
| <span class="k">if</span> <span class="ow">not</span> <span class="n">merge</span><span class="p">:</span> |
| <span class="n">outputs</span> <span class="o">=</span> <span class="n">_as_list</span><span class="p">(</span><span class="n">F</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="n">outputs</span><span class="p">,</span> <span class="n">num_outputs</span><span class="o">=</span><span class="n">length</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="n">time_axis</span><span class="p">,</span> |
| <span class="n">squeeze_axis</span><span class="o">=</span><span class="kc">True</span><span class="p">))</span> |
| <span class="k">return</span> <span class="n">outputs</span> |
| |
| <span class="k">def</span> <span class="nf">_reverse_sequences</span><span class="p">(</span><span class="n">sequences</span><span class="p">,</span> <span class="n">unroll_step</span><span class="p">,</span> <span class="n">valid_length</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> |
| <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">sequences</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">symbol</span><span class="o">.</span><span class="n">Symbol</span><span class="p">):</span> |
| <span class="n">F</span> <span class="o">=</span> <span class="n">symbol</span> |
| <span class="k">else</span><span class="p">:</span> |
| <span class="n">F</span> <span class="o">=</span> <span class="n">ndarray</span> |
| |
| <span class="k">if</span> <span class="n">valid_length</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span> |
| <span class="n">reversed_sequences</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="nb">reversed</span><span class="p">(</span><span class="n">sequences</span><span class="p">))</span> |
| <span class="k">else</span><span class="p">:</span> |
| <span class="n">reversed_sequences</span> <span class="o">=</span> <span class="n">F</span><span class="o">.</span><span class="n">SequenceReverse</span><span class="p">(</span><span class="n">F</span><span class="o">.</span><span class="n">stack</span><span class="p">(</span><span class="o">*</span><span class="n">sequences</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">),</span> |
| <span class="n">sequence_length</span><span class="o">=</span><span class="n">valid_length</span><span class="p">,</span> |
| <span class="n">use_sequence_length</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span> |
| <span class="k">if</span> <span class="n">unroll_step</span> <span class="o">></span> <span class="mi">1</span> <span class="ow">or</span> <span class="n">F</span> <span class="ow">is</span> <span class="n">symbol</span><span class="p">:</span> |
| <span class="n">reversed_sequences</span> <span class="o">=</span> <span class="n">F</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="n">reversed_sequences</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">num_outputs</span><span class="o">=</span><span class="n">unroll_step</span><span class="p">,</span> <span class="n">squeeze_axis</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span> |
| <span class="k">else</span><span class="p">:</span> |
| <span class="n">reversed_sequences</span> <span class="o">=</span> <span class="p">[</span><span class="n">reversed_sequences</span><span class="p">[</span><span class="mi">0</span><span class="p">]]</span> |
| |
| <span class="k">return</span> <span class="n">reversed_sequences</span> |
| |
| |
| <div class="viewcode-block" id="RecurrentCell"><a class="viewcode-back" href="../../../../api/gluon/rnn/index.html#mxnet.gluon.rnn.RecurrentCell">[docs]</a><span class="k">class</span> <span class="nc">RecurrentCell</span><span class="p">(</span><span class="n">Block</span><span class="p">):</span> |
| <span class="sd">"""Abstract base class for RNN cells</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> prefix : str, optional</span> |
| <span class="sd"> Prefix for names of `Block`s</span> |
| <span class="sd"> (this prefix is also used for names of weights if `params` is `None`</span> |
| <span class="sd"> i.e. if `params` are being created and not reused)</span> |
| <span class="sd"> params : Parameter or None, default None</span> |
| <span class="sd"> Container for weight sharing between cells.</span> |
| <span class="sd"> A new Parameter container is created if `params` is `None`.</span> |
| <span class="sd"> """</span> |
| <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">prefix</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">params</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> |
| <span class="nb">super</span><span class="p">(</span><span class="n">RecurrentCell</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="n">prefix</span><span class="o">=</span><span class="n">prefix</span><span class="p">,</span> <span class="n">params</span><span class="o">=</span><span class="n">params</span><span class="p">)</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_modified</span> <span class="o">=</span> <span class="kc">False</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">reset</span><span class="p">()</span> |
| |
| <div class="viewcode-block" id="RecurrentCell.reset"><a class="viewcode-back" href="../../../../api/gluon/rnn/index.html#mxnet.gluon.rnn.RecurrentCell.reset">[docs]</a> <span class="k">def</span> <span class="nf">reset</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span> |
| <span class="sd">"""Reset before re-using the cell for another graph."""</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_init_counter</span> <span class="o">=</span> <span class="o">-</span><span class="mi">1</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_counter</span> <span class="o">=</span> <span class="o">-</span><span class="mi">1</span> |
| <span class="k">for</span> <span class="n">cell</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">_children</span><span class="o">.</span><span class="n">values</span><span class="p">():</span> |
| <span class="n">cell</span><span class="o">.</span><span class="n">reset</span><span class="p">()</span></div> |
| |
| <div class="viewcode-block" id="RecurrentCell.state_info"><a class="viewcode-back" href="../../../../api/gluon/rnn/index.html#mxnet.gluon.rnn.RecurrentCell.state_info">[docs]</a> <span class="k">def</span> <span class="nf">state_info</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">batch_size</span><span class="o">=</span><span class="mi">0</span><span class="p">):</span> |
| <span class="sd">"""shape and layout information of states"""</span> |
| <span class="k">raise</span> <span class="ne">NotImplementedError</span><span class="p">()</span></div> |
| |
| <div class="viewcode-block" id="RecurrentCell.begin_state"><a class="viewcode-back" href="../../../../api/gluon/rnn/index.html#mxnet.gluon.rnn.RecurrentCell.begin_state">[docs]</a> <span class="k">def</span> <span class="nf">begin_state</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">batch_size</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">func</span><span class="o">=</span><span class="n">ndarray</span><span class="o">.</span><span class="n">zeros</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span> |
| <span class="sd">"""Initial state for this cell.</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> func : callable, default symbol.zeros</span> |
| <span class="sd"> Function for creating initial state.</span> |
| |
| <span class="sd"> For Symbol API, func can be `symbol.zeros`, `symbol.uniform`,</span> |
| <span class="sd"> `symbol.var etc`. Use `symbol.var` if you want to directly</span> |
| <span class="sd"> feed input as states.</span> |
| |
| <span class="sd"> For NDArray API, func can be `ndarray.zeros`, `ndarray.ones`, etc.</span> |
| <span class="sd"> batch_size: int, default 0</span> |
| <span class="sd"> Only required for NDArray API. Size of the batch ('N' in layout)</span> |
| <span class="sd"> dimension of input.</span> |
| |
| <span class="sd"> **kwargs :</span> |
| <span class="sd"> Additional keyword arguments passed to func. For example</span> |
| <span class="sd"> `mean`, `std`, `dtype`, etc.</span> |
| |
| <span class="sd"> Returns</span> |
| <span class="sd"> -------</span> |
| <span class="sd"> states : nested list of Symbol</span> |
| <span class="sd"> Starting states for the first RNN step.</span> |
| <span class="sd"> """</span> |
| <span class="k">assert</span> <span class="ow">not</span> <span class="bp">self</span><span class="o">.</span><span class="n">_modified</span><span class="p">,</span> \ |
| <span class="s2">"After applying modifier cells (e.g. ZoneoutCell) the base "</span> \ |
| <span class="s2">"cell cannot be called directly. Call the modifier cell instead."</span> |
| <span class="n">states</span> <span class="o">=</span> <span class="p">[]</span> |
| <span class="k">for</span> <span class="n">info</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">state_info</span><span class="p">(</span><span class="n">batch_size</span><span class="p">):</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_init_counter</span> <span class="o">+=</span> <span class="mi">1</span> |
| <span class="k">if</span> <span class="n">info</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span> |
| <span class="n">info</span><span class="o">.</span><span class="n">update</span><span class="p">(</span><span class="n">kwargs</span><span class="p">)</span> |
| <span class="k">else</span><span class="p">:</span> |
| <span class="n">info</span> <span class="o">=</span> <span class="n">kwargs</span> |
| <span class="n">state</span> <span class="o">=</span> <span class="n">func</span><span class="p">(</span><span class="n">name</span><span class="o">=</span><span class="s1">'</span><span class="si">%s</span><span class="s1">begin_state_</span><span class="si">%d</span><span class="s1">'</span><span class="o">%</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_prefix</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">_init_counter</span><span class="p">),</span> |
| <span class="o">**</span><span class="n">info</span><span class="p">)</span> |
| <span class="n">states</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">state</span><span class="p">)</span> |
| <span class="k">return</span> <span class="n">states</span></div> |
| |
| <div class="viewcode-block" id="RecurrentCell.unroll"><a class="viewcode-back" href="../../../../api/gluon/rnn/index.html#mxnet.gluon.rnn.RecurrentCell.unroll">[docs]</a> <span class="k">def</span> <span class="nf">unroll</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">length</span><span class="p">,</span> <span class="n">inputs</span><span class="p">,</span> <span class="n">begin_state</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">layout</span><span class="o">=</span><span class="s1">'NTC'</span><span class="p">,</span> <span class="n">merge_outputs</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> |
| <span class="n">valid_length</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> |
| <span class="sd">"""Unrolls an RNN cell across time steps.</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> length : int</span> |
| <span class="sd"> Number of steps to unroll.</span> |
| <span class="sd"> inputs : Symbol, list of Symbol, or None</span> |
| <span class="sd"> If `inputs` is a single Symbol (usually the output</span> |
| <span class="sd"> of Embedding symbol), it should have shape</span> |
| <span class="sd"> (batch_size, length, ...) if `layout` is 'NTC',</span> |
| <span class="sd"> or (length, batch_size, ...) if `layout` is 'TNC'.</span> |
| |
| <span class="sd"> If `inputs` is a list of symbols (usually output of</span> |
| <span class="sd"> previous unroll), they should all have shape</span> |
| <span class="sd"> (batch_size, ...).</span> |
| <span class="sd"> begin_state : nested list of Symbol, optional</span> |
| <span class="sd"> Input states created by `begin_state()`</span> |
| <span class="sd"> or output state of another cell.</span> |
| <span class="sd"> Created from `begin_state()` if `None`.</span> |
| <span class="sd"> layout : str, optional</span> |
| <span class="sd"> `layout` of input symbol. Only used if inputs</span> |
| <span class="sd"> is a single Symbol.</span> |
| <span class="sd"> merge_outputs : bool, optional</span> |
| <span class="sd"> If `False`, returns outputs as a list of Symbols.</span> |
| <span class="sd"> If `True`, concatenates output across time steps</span> |
| <span class="sd"> and returns a single symbol with shape</span> |
| <span class="sd"> (batch_size, length, ...) if layout is 'NTC',</span> |
| <span class="sd"> or (length, batch_size, ...) if layout is 'TNC'.</span> |
| <span class="sd"> If `None`, output whatever is faster.</span> |
| <span class="sd"> valid_length : Symbol, NDArray or None</span> |
| <span class="sd"> `valid_length` specifies the length of the sequences in the batch without padding.</span> |
| <span class="sd"> This option is especially useful for building sequence-to-sequence models where</span> |
| <span class="sd"> the input and output sequences would potentially be padded.</span> |
| <span class="sd"> If `valid_length` is None, all sequences are assumed to have the same length.</span> |
| <span class="sd"> If `valid_length` is a Symbol or NDArray, it should have shape (batch_size,).</span> |
| <span class="sd"> The ith element will be the length of the ith sequence in the batch.</span> |
| <span class="sd"> The last valid state will be return and the padded outputs will be masked with 0.</span> |
| <span class="sd"> Note that `valid_length` must be smaller or equal to `length`.</span> |
| |
| <span class="sd"> Returns</span> |
| <span class="sd"> -------</span> |
| <span class="sd"> outputs : list of Symbol or Symbol</span> |
| <span class="sd"> Symbol (if `merge_outputs` is True) or list of Symbols</span> |
| <span class="sd"> (if `merge_outputs` is False) corresponding to the output from</span> |
| <span class="sd"> the RNN from this unrolling.</span> |
| |
| <span class="sd"> states : list of Symbol</span> |
| <span class="sd"> The new state of this RNN after this unrolling.</span> |
| <span class="sd"> The type of this symbol is same as the output of `begin_state()`.</span> |
| <span class="sd"> """</span> |
| <span class="c1"># pylint: disable=too-many-locals</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">reset</span><span class="p">()</span> |
| |
| <span class="n">inputs</span><span class="p">,</span> <span class="n">axis</span><span class="p">,</span> <span class="n">F</span><span class="p">,</span> <span class="n">batch_size</span> <span class="o">=</span> <span class="n">_format_sequence</span><span class="p">(</span><span class="n">length</span><span class="p">,</span> <span class="n">inputs</span><span class="p">,</span> <span class="n">layout</span><span class="p">,</span> <span class="kc">False</span><span class="p">)</span> |
| <span class="n">begin_state</span> <span class="o">=</span> <span class="n">_get_begin_state</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">F</span><span class="p">,</span> <span class="n">begin_state</span><span class="p">,</span> <span class="n">inputs</span><span class="p">,</span> <span class="n">batch_size</span><span class="p">)</span> |
| |
| <span class="n">states</span> <span class="o">=</span> <span class="n">begin_state</span> |
| <span class="n">outputs</span> <span class="o">=</span> <span class="p">[]</span> |
| <span class="n">all_states</span> <span class="o">=</span> <span class="p">[]</span> |
| <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">length</span><span class="p">):</span> |
| <span class="n">output</span><span class="p">,</span> <span class="n">states</span> <span class="o">=</span> <span class="bp">self</span><span class="p">(</span><span class="n">inputs</span><span class="p">[</span><span class="n">i</span><span class="p">],</span> <span class="n">states</span><span class="p">)</span> |
| <span class="n">outputs</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">output</span><span class="p">)</span> |
| <span class="k">if</span> <span class="n">valid_length</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span> |
| <span class="n">all_states</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">states</span><span class="p">)</span> |
| <span class="k">if</span> <span class="n">valid_length</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span> |
| <span class="n">states</span> <span class="o">=</span> <span class="p">[</span><span class="n">F</span><span class="o">.</span><span class="n">SequenceLast</span><span class="p">(</span><span class="n">F</span><span class="o">.</span><span class="n">stack</span><span class="p">(</span><span class="o">*</span><span class="n">ele_list</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">),</span> |
| <span class="n">sequence_length</span><span class="o">=</span><span class="n">valid_length</span><span class="p">,</span> |
| <span class="n">use_sequence_length</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> |
| <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span> |
| <span class="k">for</span> <span class="n">ele_list</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="o">*</span><span class="n">all_states</span><span class="p">)]</span> |
| <span class="n">outputs</span> <span class="o">=</span> <span class="n">_mask_sequence_variable_length</span><span class="p">(</span><span class="n">F</span><span class="p">,</span> <span class="n">outputs</span><span class="p">,</span> <span class="n">length</span><span class="p">,</span> <span class="n">valid_length</span><span class="p">,</span> <span class="n">axis</span><span class="p">,</span> <span class="kc">True</span><span class="p">)</span> |
| <span class="n">outputs</span><span class="p">,</span> <span class="n">_</span><span class="p">,</span> <span class="n">_</span><span class="p">,</span> <span class="n">_</span> <span class="o">=</span> <span class="n">_format_sequence</span><span class="p">(</span><span class="n">length</span><span class="p">,</span> <span class="n">outputs</span><span class="p">,</span> <span class="n">layout</span><span class="p">,</span> <span class="n">merge_outputs</span><span class="p">)</span> |
| |
| <span class="k">return</span> <span class="n">outputs</span><span class="p">,</span> <span class="n">states</span></div> |
| |
| <span class="c1">#pylint: disable=no-self-use</span> |
| <span class="k">def</span> <span class="nf">_get_activation</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">F</span><span class="p">,</span> <span class="n">inputs</span><span class="p">,</span> <span class="n">activation</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span> |
| <span class="sd">"""Get activation function. Convert if is string"""</span> |
| <span class="n">func</span> <span class="o">=</span> <span class="p">{</span><span class="s1">'tanh'</span><span class="p">:</span> <span class="n">F</span><span class="o">.</span><span class="n">tanh</span><span class="p">,</span> |
| <span class="s1">'relu'</span><span class="p">:</span> <span class="n">F</span><span class="o">.</span><span class="n">relu</span><span class="p">,</span> |
| <span class="s1">'sigmoid'</span><span class="p">:</span> <span class="n">F</span><span class="o">.</span><span class="n">sigmoid</span><span class="p">,</span> |
| <span class="s1">'softsign'</span><span class="p">:</span> <span class="n">F</span><span class="o">.</span><span class="n">softsign</span><span class="p">}</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="n">activation</span><span class="p">)</span> |
| <span class="k">if</span> <span class="n">func</span><span class="p">:</span> |
| <span class="k">return</span> <span class="n">func</span><span class="p">(</span><span class="n">inputs</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span> |
| <span class="k">elif</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">activation</span><span class="p">,</span> <span class="n">string_types</span><span class="p">):</span> |
| <span class="k">return</span> <span class="n">F</span><span class="o">.</span><span class="n">Activation</span><span class="p">(</span><span class="n">inputs</span><span class="p">,</span> <span class="n">act_type</span><span class="o">=</span><span class="n">activation</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span> |
| <span class="k">elif</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">activation</span><span class="p">,</span> <span class="n">LeakyReLU</span><span class="p">):</span> |
| <span class="k">return</span> <span class="n">F</span><span class="o">.</span><span class="n">LeakyReLU</span><span class="p">(</span><span class="n">inputs</span><span class="p">,</span> <span class="n">act_type</span><span class="o">=</span><span class="s1">'leaky'</span><span class="p">,</span> <span class="n">slope</span><span class="o">=</span><span class="n">activation</span><span class="o">.</span><span class="n">_alpha</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span> |
| <span class="k">return</span> <span class="n">activation</span><span class="p">(</span><span class="n">inputs</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span> |
| |
| <div class="viewcode-block" id="RecurrentCell.forward"><a class="viewcode-back" href="../../../../api/gluon/rnn/index.html#mxnet.gluon.rnn.RecurrentCell.forward">[docs]</a> <span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">inputs</span><span class="p">,</span> <span class="n">states</span><span class="p">):</span> |
| <span class="sd">"""Unrolls the recurrent cell for one time step.</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> inputs : sym.Variable</span> |
| <span class="sd"> Input symbol, 2D, of shape (batch_size * num_units).</span> |
| <span class="sd"> states : list of sym.Variable</span> |
| <span class="sd"> RNN state from previous step or the output of begin_state().</span> |
| |
| <span class="sd"> Returns</span> |
| <span class="sd"> -------</span> |
| <span class="sd"> output : Symbol</span> |
| <span class="sd"> Symbol corresponding to the output from the RNN when unrolling</span> |
| <span class="sd"> for a single time step.</span> |
| <span class="sd"> states : list of Symbol</span> |
| <span class="sd"> The new state of this RNN after this unrolling.</span> |
| <span class="sd"> The type of this symbol is same as the output of `begin_state()`.</span> |
| <span class="sd"> This can be used as an input state to the next time step</span> |
| <span class="sd"> of this RNN.</span> |
| |
| <span class="sd"> See Also</span> |
| <span class="sd"> --------</span> |
| <span class="sd"> begin_state: This function can provide the states for the first time step.</span> |
| <span class="sd"> unroll: This function unrolls an RNN for a given number of (>=1) time steps.</span> |
| <span class="sd"> """</span> |
| <span class="c1"># pylint: disable= arguments-differ</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_counter</span> <span class="o">+=</span> <span class="mi">1</span> |
| <span class="k">return</span> <span class="nb">super</span><span class="p">(</span><span class="n">RecurrentCell</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="n">forward</span><span class="p">(</span><span class="n">inputs</span><span class="p">,</span> <span class="n">states</span><span class="p">)</span></div></div> |
| |
| |
| <div class="viewcode-block" id="HybridRecurrentCell"><a class="viewcode-back" href="../../../../api/gluon/rnn/index.html#mxnet.gluon.rnn.HybridRecurrentCell">[docs]</a><span class="k">class</span> <span class="nc">HybridRecurrentCell</span><span class="p">(</span><span class="n">RecurrentCell</span><span class="p">,</span> <span class="n">HybridBlock</span><span class="p">):</span> |
| <span class="sd">"""HybridRecurrentCell supports hybridize."""</span> |
| <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">prefix</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">params</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> |
| <span class="nb">super</span><span class="p">(</span><span class="n">HybridRecurrentCell</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="n">prefix</span><span class="o">=</span><span class="n">prefix</span><span class="p">,</span> <span class="n">params</span><span class="o">=</span><span class="n">params</span><span class="p">)</span> |
| |
| <div class="viewcode-block" id="HybridRecurrentCell.hybrid_forward"><a class="viewcode-back" href="../../../../api/gluon/rnn/index.html#mxnet.gluon.rnn.HybridRecurrentCell.hybrid_forward">[docs]</a> <span class="k">def</span> <span class="nf">hybrid_forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">F</span><span class="p">,</span> <span class="n">x</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span> |
| <span class="k">raise</span> <span class="ne">NotImplementedError</span></div></div> |
| |
| |
| <div class="viewcode-block" id="RNNCell"><a class="viewcode-back" href="../../../../api/gluon/rnn/index.html#mxnet.gluon.rnn.RNNCell">[docs]</a><span class="k">class</span> <span class="nc">RNNCell</span><span class="p">(</span><span class="n">HybridRecurrentCell</span><span class="p">):</span> |
| <span class="sa">r</span><span class="sd">"""Elman RNN recurrent neural network cell.</span> |
| |
| <span class="sd"> Each call computes the following function:</span> |
| |
| <span class="sd"> .. math::</span> |
| |
| <span class="sd"> h_t = \tanh(w_{ih} * x_t + b_{ih} + w_{hh} * h_{(t-1)} + b_{hh})</span> |
| |
| <span class="sd"> where :math:`h_t` is the hidden state at time `t`, and :math:`x_t` is the hidden</span> |
| <span class="sd"> state of the previous layer at time `t` or :math:`input_t` for the first layer.</span> |
| <span class="sd"> If nonlinearity='relu', then `ReLU` is used instead of `tanh`.</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> hidden_size : int</span> |
| <span class="sd"> Number of units in output symbol</span> |
| <span class="sd"> activation : str or Symbol, default 'tanh'</span> |
| <span class="sd"> Type of activation function.</span> |
| <span class="sd"> i2h_weight_initializer : str or Initializer</span> |
| <span class="sd"> Initializer for the input weights matrix, used for the linear</span> |
| <span class="sd"> transformation of the inputs.</span> |
| <span class="sd"> h2h_weight_initializer : str or Initializer</span> |
| <span class="sd"> Initializer for the recurrent weights matrix, used for the linear</span> |
| <span class="sd"> transformation of the recurrent state.</span> |
| <span class="sd"> i2h_bias_initializer : str or Initializer, default 'zeros'</span> |
| <span class="sd"> Initializer for the bias vector.</span> |
| <span class="sd"> h2h_bias_initializer : str or Initializer, default 'zeros'</span> |
| <span class="sd"> Initializer for the bias vector.</span> |
| <span class="sd"> prefix : str, default ``'rnn_'``</span> |
| <span class="sd"> Prefix for name of `Block`s</span> |
| <span class="sd"> (and name of weight if params is `None`).</span> |
| <span class="sd"> params : Parameter or None</span> |
| <span class="sd"> Container for weight sharing between cells.</span> |
| <span class="sd"> Created if `None`.</span> |
| |
| |
| <span class="sd"> Inputs:</span> |
| <span class="sd"> - **data**: input tensor with shape `(batch_size, input_size)`.</span> |
| <span class="sd"> - **states**: a list of one initial recurrent state tensor with shape</span> |
| <span class="sd"> `(batch_size, num_hidden)`.</span> |
| |
| <span class="sd"> Outputs:</span> |
| <span class="sd"> - **out**: output tensor with shape `(batch_size, num_hidden)`.</span> |
| <span class="sd"> - **next_states**: a list of one output recurrent state tensor with the</span> |
| <span class="sd"> same shape as `states`.</span> |
| <span class="sd"> """</span> |
| <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">hidden_size</span><span class="p">,</span> <span class="n">activation</span><span class="o">=</span><span class="s1">'tanh'</span><span class="p">,</span> |
| <span class="n">i2h_weight_initializer</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">h2h_weight_initializer</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> |
| <span class="n">i2h_bias_initializer</span><span class="o">=</span><span class="s1">'zeros'</span><span class="p">,</span> <span class="n">h2h_bias_initializer</span><span class="o">=</span><span class="s1">'zeros'</span><span class="p">,</span> |
| <span class="n">input_size</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">prefix</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">params</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> |
| <span class="nb">super</span><span class="p">(</span><span class="n">RNNCell</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="n">prefix</span><span class="o">=</span><span class="n">prefix</span><span class="p">,</span> <span class="n">params</span><span class="o">=</span><span class="n">params</span><span class="p">)</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_hidden_size</span> <span class="o">=</span> <span class="n">hidden_size</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_activation</span> <span class="o">=</span> <span class="n">activation</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_input_size</span> <span class="o">=</span> <span class="n">input_size</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">i2h_weight</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">params</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">'i2h_weight'</span><span class="p">,</span> <span class="n">shape</span><span class="o">=</span><span class="p">(</span><span class="n">hidden_size</span><span class="p">,</span> <span class="n">input_size</span><span class="p">),</span> |
| <span class="n">init</span><span class="o">=</span><span class="n">i2h_weight_initializer</span><span class="p">,</span> |
| <span class="n">allow_deferred_init</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">h2h_weight</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">params</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">'h2h_weight'</span><span class="p">,</span> <span class="n">shape</span><span class="o">=</span><span class="p">(</span><span class="n">hidden_size</span><span class="p">,</span> <span class="n">hidden_size</span><span class="p">),</span> |
| <span class="n">init</span><span class="o">=</span><span class="n">h2h_weight_initializer</span><span class="p">,</span> |
| <span class="n">allow_deferred_init</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">i2h_bias</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">params</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">'i2h_bias'</span><span class="p">,</span> <span class="n">shape</span><span class="o">=</span><span class="p">(</span><span class="n">hidden_size</span><span class="p">,),</span> |
| <span class="n">init</span><span class="o">=</span><span class="n">i2h_bias_initializer</span><span class="p">,</span> |
| <span class="n">allow_deferred_init</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">h2h_bias</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">params</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">'h2h_bias'</span><span class="p">,</span> <span class="n">shape</span><span class="o">=</span><span class="p">(</span><span class="n">hidden_size</span><span class="p">,),</span> |
| <span class="n">init</span><span class="o">=</span><span class="n">h2h_bias_initializer</span><span class="p">,</span> |
| <span class="n">allow_deferred_init</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span> |
| |
| <div class="viewcode-block" id="RNNCell.state_info"><a class="viewcode-back" href="../../../../api/gluon/rnn/index.html#mxnet.gluon.rnn.RNNCell.state_info">[docs]</a> <span class="k">def</span> <span class="nf">state_info</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">batch_size</span><span class="o">=</span><span class="mi">0</span><span class="p">):</span> |
| <span class="k">return</span> <span class="p">[{</span><span class="s1">'shape'</span><span class="p">:</span> <span class="p">(</span><span class="n">batch_size</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">_hidden_size</span><span class="p">),</span> <span class="s1">'__layout__'</span><span class="p">:</span> <span class="s1">'NC'</span><span class="p">}]</span></div> |
| |
| <span class="k">def</span> <span class="nf">_alias</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span> |
| <span class="k">return</span> <span class="s1">'rnn'</span> |
| |
| <span class="k">def</span> <span class="fm">__repr__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span> |
| <span class="n">s</span> <span class="o">=</span> <span class="s1">'</span><span class="si">{name}</span><span class="s1">(</span><span class="si">{mapping}</span><span class="s1">'</span> |
| <span class="k">if</span> <span class="nb">hasattr</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="s1">'_activation'</span><span class="p">):</span> |
| <span class="n">s</span> <span class="o">+=</span> <span class="s1">', </span><span class="si">{_activation}</span><span class="s1">'</span> |
| <span class="n">s</span> <span class="o">+=</span> <span class="s1">')'</span> |
| <span class="n">shape</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">i2h_weight</span><span class="o">.</span><span class="n">shape</span> |
| <span class="n">mapping</span> <span class="o">=</span> <span class="s1">'</span><span class="si">{0}</span><span class="s1"> -> </span><span class="si">{1}</span><span class="s1">'</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="k">if</span> <span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="k">else</span> <span class="kc">None</span><span class="p">,</span> <span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span> |
| <span class="k">return</span> <span class="n">s</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">name</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="vm">__class__</span><span class="o">.</span><span class="vm">__name__</span><span class="p">,</span> |
| <span class="n">mapping</span><span class="o">=</span><span class="n">mapping</span><span class="p">,</span> |
| <span class="o">**</span><span class="bp">self</span><span class="o">.</span><span class="vm">__dict__</span><span class="p">)</span> |
| |
| <div class="viewcode-block" id="RNNCell.hybrid_forward"><a class="viewcode-back" href="../../../../api/gluon/rnn/index.html#mxnet.gluon.rnn.RNNCell.hybrid_forward">[docs]</a> <span class="k">def</span> <span class="nf">hybrid_forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">F</span><span class="p">,</span> <span class="n">inputs</span><span class="p">,</span> <span class="n">states</span><span class="p">,</span> <span class="n">i2h_weight</span><span class="p">,</span> |
| <span class="n">h2h_weight</span><span class="p">,</span> <span class="n">i2h_bias</span><span class="p">,</span> <span class="n">h2h_bias</span><span class="p">):</span> |
| <span class="n">prefix</span> <span class="o">=</span> <span class="s1">'t</span><span class="si">%d</span><span class="s1">_'</span><span class="o">%</span><span class="bp">self</span><span class="o">.</span><span class="n">_counter</span> |
| <span class="n">i2h</span> <span class="o">=</span> <span class="n">F</span><span class="o">.</span><span class="n">FullyConnected</span><span class="p">(</span><span class="n">data</span><span class="o">=</span><span class="n">inputs</span><span class="p">,</span> <span class="n">weight</span><span class="o">=</span><span class="n">i2h_weight</span><span class="p">,</span> <span class="n">bias</span><span class="o">=</span><span class="n">i2h_bias</span><span class="p">,</span> |
| <span class="n">num_hidden</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">_hidden_size</span><span class="p">,</span> |
| <span class="n">name</span><span class="o">=</span><span class="n">prefix</span><span class="o">+</span><span class="s1">'i2h'</span><span class="p">)</span> |
| <span class="n">h2h</span> <span class="o">=</span> <span class="n">F</span><span class="o">.</span><span class="n">FullyConnected</span><span class="p">(</span><span class="n">data</span><span class="o">=</span><span class="n">states</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">weight</span><span class="o">=</span><span class="n">h2h_weight</span><span class="p">,</span> <span class="n">bias</span><span class="o">=</span><span class="n">h2h_bias</span><span class="p">,</span> |
| <span class="n">num_hidden</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">_hidden_size</span><span class="p">,</span> |
| <span class="n">name</span><span class="o">=</span><span class="n">prefix</span><span class="o">+</span><span class="s1">'h2h'</span><span class="p">)</span> |
| <span class="n">i2h_plus_h2h</span> <span class="o">=</span> <span class="n">F</span><span class="o">.</span><span class="n">elemwise_add</span><span class="p">(</span><span class="n">i2h</span><span class="p">,</span> <span class="n">h2h</span><span class="p">,</span> <span class="n">name</span><span class="o">=</span><span class="n">prefix</span><span class="o">+</span><span class="s1">'plus0'</span><span class="p">)</span> |
| <span class="n">output</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_activation</span><span class="p">(</span><span class="n">F</span><span class="p">,</span> <span class="n">i2h_plus_h2h</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">_activation</span><span class="p">,</span> |
| <span class="n">name</span><span class="o">=</span><span class="n">prefix</span><span class="o">+</span><span class="s1">'out'</span><span class="p">)</span> |
| |
| <span class="k">return</span> <span class="n">output</span><span class="p">,</span> <span class="p">[</span><span class="n">output</span><span class="p">]</span></div></div> |
| |
| |
| <div class="viewcode-block" id="LSTMCell"><a class="viewcode-back" href="../../../../api/gluon/rnn/index.html#mxnet.gluon.rnn.LSTMCell">[docs]</a><span class="k">class</span> <span class="nc">LSTMCell</span><span class="p">(</span><span class="n">HybridRecurrentCell</span><span class="p">):</span> |
| <span class="sa">r</span><span class="sd">"""Long-Short Term Memory (LSTM) network cell.</span> |
| |
| <span class="sd"> Each call computes the following function:</span> |
| |
| <span class="sd"> .. math::</span> |
| <span class="sd"> \begin{array}{ll}</span> |
| <span class="sd"> i_t = sigmoid(W_{ii} x_t + b_{ii} + W_{hi} h_{(t-1)} + b_{hi}) \\</span> |
| <span class="sd"> f_t = sigmoid(W_{if} x_t + b_{if} + W_{hf} h_{(t-1)} + b_{hf}) \\</span> |
| <span class="sd"> g_t = \tanh(W_{ig} x_t + b_{ig} + W_{hc} h_{(t-1)} + b_{hg}) \\</span> |
| <span class="sd"> o_t = sigmoid(W_{io} x_t + b_{io} + W_{ho} h_{(t-1)} + b_{ho}) \\</span> |
| <span class="sd"> c_t = f_t * c_{(t-1)} + i_t * g_t \\</span> |
| <span class="sd"> h_t = o_t * \tanh(c_t)</span> |
| <span class="sd"> \end{array}</span> |
| |
| <span class="sd"> where :math:`h_t` is the hidden state at time `t`, :math:`c_t` is the</span> |
| <span class="sd"> cell state at time `t`, :math:`x_t` is the hidden state of the previous</span> |
| <span class="sd"> layer at time `t` or :math:`input_t` for the first layer, and :math:`i_t`,</span> |
| <span class="sd"> :math:`f_t`, :math:`g_t`, :math:`o_t` are the input, forget, cell, and</span> |
| <span class="sd"> out gates, respectively.</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> hidden_size : int</span> |
| <span class="sd"> Number of units in output symbol.</span> |
| <span class="sd"> i2h_weight_initializer : str or Initializer</span> |
| <span class="sd"> Initializer for the input weights matrix, used for the linear</span> |
| <span class="sd"> transformation of the inputs.</span> |
| <span class="sd"> h2h_weight_initializer : str or Initializer</span> |
| <span class="sd"> Initializer for the recurrent weights matrix, used for the linear</span> |
| <span class="sd"> transformation of the recurrent state.</span> |
| <span class="sd"> i2h_bias_initializer : str or Initializer, default 'zeros'</span> |
| <span class="sd"> Initializer for the bias vector.</span> |
| <span class="sd"> h2h_bias_initializer : str or Initializer, default 'zeros'</span> |
| <span class="sd"> Initializer for the bias vector.</span> |
| <span class="sd"> prefix : str, default ``'lstm_'``</span> |
| <span class="sd"> Prefix for name of `Block`s</span> |
| <span class="sd"> (and name of weight if params is `None`).</span> |
| <span class="sd"> params : Parameter or None, default None</span> |
| <span class="sd"> Container for weight sharing between cells.</span> |
| <span class="sd"> Created if `None`.</span> |
| <span class="sd"> activation : str, default 'tanh'</span> |
| <span class="sd"> Activation type to use. See nd/symbol Activation</span> |
| <span class="sd"> for supported types.</span> |
| <span class="sd"> recurrent_activation : str, default 'sigmoid'</span> |
| <span class="sd"> Activation type to use for the recurrent step. See nd/symbol Activation</span> |
| <span class="sd"> for supported types.</span> |
| |
| <span class="sd"> Inputs:</span> |
| <span class="sd"> - **data**: input tensor with shape `(batch_size, input_size)`.</span> |
| <span class="sd"> - **states**: a list of two initial recurrent state tensors. Each has shape</span> |
| <span class="sd"> `(batch_size, num_hidden)`.</span> |
| |
| <span class="sd"> Outputs:</span> |
| <span class="sd"> - **out**: output tensor with shape `(batch_size, num_hidden)`.</span> |
| <span class="sd"> - **next_states**: a list of two output recurrent state tensors. Each has</span> |
| <span class="sd"> the same shape as `states`.</span> |
| <span class="sd"> """</span> |
| <span class="c1"># pylint: disable=too-many-instance-attributes</span> |
| <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">hidden_size</span><span class="p">,</span> |
| <span class="n">i2h_weight_initializer</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">h2h_weight_initializer</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> |
| <span class="n">i2h_bias_initializer</span><span class="o">=</span><span class="s1">'zeros'</span><span class="p">,</span> <span class="n">h2h_bias_initializer</span><span class="o">=</span><span class="s1">'zeros'</span><span class="p">,</span> |
| <span class="n">input_size</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">prefix</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">params</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">activation</span><span class="o">=</span><span class="s1">'tanh'</span><span class="p">,</span> |
| <span class="n">recurrent_activation</span><span class="o">=</span><span class="s1">'sigmoid'</span><span class="p">):</span> |
| <span class="nb">super</span><span class="p">(</span><span class="n">LSTMCell</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="n">prefix</span><span class="o">=</span><span class="n">prefix</span><span class="p">,</span> <span class="n">params</span><span class="o">=</span><span class="n">params</span><span class="p">)</span> |
| |
| <span class="bp">self</span><span class="o">.</span><span class="n">_hidden_size</span> <span class="o">=</span> <span class="n">hidden_size</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_input_size</span> <span class="o">=</span> <span class="n">input_size</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">i2h_weight</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">params</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">'i2h_weight'</span><span class="p">,</span> <span class="n">shape</span><span class="o">=</span><span class="p">(</span><span class="mi">4</span><span class="o">*</span><span class="n">hidden_size</span><span class="p">,</span> <span class="n">input_size</span><span class="p">),</span> |
| <span class="n">init</span><span class="o">=</span><span class="n">i2h_weight_initializer</span><span class="p">,</span> |
| <span class="n">allow_deferred_init</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">h2h_weight</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">params</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">'h2h_weight'</span><span class="p">,</span> <span class="n">shape</span><span class="o">=</span><span class="p">(</span><span class="mi">4</span><span class="o">*</span><span class="n">hidden_size</span><span class="p">,</span> <span class="n">hidden_size</span><span class="p">),</span> |
| <span class="n">init</span><span class="o">=</span><span class="n">h2h_weight_initializer</span><span class="p">,</span> |
| <span class="n">allow_deferred_init</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">i2h_bias</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">params</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">'i2h_bias'</span><span class="p">,</span> <span class="n">shape</span><span class="o">=</span><span class="p">(</span><span class="mi">4</span><span class="o">*</span><span class="n">hidden_size</span><span class="p">,),</span> |
| <span class="n">init</span><span class="o">=</span><span class="n">i2h_bias_initializer</span><span class="p">,</span> |
| <span class="n">allow_deferred_init</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">h2h_bias</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">params</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">'h2h_bias'</span><span class="p">,</span> <span class="n">shape</span><span class="o">=</span><span class="p">(</span><span class="mi">4</span><span class="o">*</span><span class="n">hidden_size</span><span class="p">,),</span> |
| <span class="n">init</span><span class="o">=</span><span class="n">h2h_bias_initializer</span><span class="p">,</span> |
| <span class="n">allow_deferred_init</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_activation</span> <span class="o">=</span> <span class="n">activation</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_recurrent_activation</span> <span class="o">=</span> <span class="n">recurrent_activation</span> |
| |
| |
| <div class="viewcode-block" id="LSTMCell.state_info"><a class="viewcode-back" href="../../../../api/gluon/rnn/index.html#mxnet.gluon.rnn.LSTMCell.state_info">[docs]</a> <span class="k">def</span> <span class="nf">state_info</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">batch_size</span><span class="o">=</span><span class="mi">0</span><span class="p">):</span> |
| <span class="k">return</span> <span class="p">[{</span><span class="s1">'shape'</span><span class="p">:</span> <span class="p">(</span><span class="n">batch_size</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">_hidden_size</span><span class="p">),</span> <span class="s1">'__layout__'</span><span class="p">:</span> <span class="s1">'NC'</span><span class="p">},</span> |
| <span class="p">{</span><span class="s1">'shape'</span><span class="p">:</span> <span class="p">(</span><span class="n">batch_size</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">_hidden_size</span><span class="p">),</span> <span class="s1">'__layout__'</span><span class="p">:</span> <span class="s1">'NC'</span><span class="p">}]</span></div> |
| |
| <span class="k">def</span> <span class="nf">_alias</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span> |
| <span class="k">return</span> <span class="s1">'lstm'</span> |
| |
| <span class="k">def</span> <span class="fm">__repr__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span> |
| <span class="n">s</span> <span class="o">=</span> <span class="s1">'</span><span class="si">{name}</span><span class="s1">(</span><span class="si">{mapping}</span><span class="s1">)'</span> |
| <span class="n">shape</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">i2h_weight</span><span class="o">.</span><span class="n">shape</span> |
| <span class="n">mapping</span> <span class="o">=</span> <span class="s1">'</span><span class="si">{0}</span><span class="s1"> -> </span><span class="si">{1}</span><span class="s1">'</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="k">if</span> <span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="k">else</span> <span class="kc">None</span><span class="p">,</span> <span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span> |
| <span class="k">return</span> <span class="n">s</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">name</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="vm">__class__</span><span class="o">.</span><span class="vm">__name__</span><span class="p">,</span> |
| <span class="n">mapping</span><span class="o">=</span><span class="n">mapping</span><span class="p">,</span> |
| <span class="o">**</span><span class="bp">self</span><span class="o">.</span><span class="vm">__dict__</span><span class="p">)</span> |
| |
| <div class="viewcode-block" id="LSTMCell.hybrid_forward"><a class="viewcode-back" href="../../../../api/gluon/rnn/index.html#mxnet.gluon.rnn.LSTMCell.hybrid_forward">[docs]</a> <span class="k">def</span> <span class="nf">hybrid_forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">F</span><span class="p">,</span> <span class="n">inputs</span><span class="p">,</span> <span class="n">states</span><span class="p">,</span> <span class="n">i2h_weight</span><span class="p">,</span> |
| <span class="n">h2h_weight</span><span class="p">,</span> <span class="n">i2h_bias</span><span class="p">,</span> <span class="n">h2h_bias</span><span class="p">):</span> |
| <span class="c1"># pylint: disable=too-many-locals</span> |
| <span class="n">prefix</span> <span class="o">=</span> <span class="s1">'t</span><span class="si">%d</span><span class="s1">_'</span><span class="o">%</span><span class="bp">self</span><span class="o">.</span><span class="n">_counter</span> |
| <span class="n">i2h</span> <span class="o">=</span> <span class="n">F</span><span class="o">.</span><span class="n">FullyConnected</span><span class="p">(</span><span class="n">data</span><span class="o">=</span><span class="n">inputs</span><span class="p">,</span> <span class="n">weight</span><span class="o">=</span><span class="n">i2h_weight</span><span class="p">,</span> <span class="n">bias</span><span class="o">=</span><span class="n">i2h_bias</span><span class="p">,</span> |
| <span class="n">num_hidden</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">_hidden_size</span><span class="o">*</span><span class="mi">4</span><span class="p">,</span> <span class="n">name</span><span class="o">=</span><span class="n">prefix</span><span class="o">+</span><span class="s1">'i2h'</span><span class="p">)</span> |
| <span class="n">h2h</span> <span class="o">=</span> <span class="n">F</span><span class="o">.</span><span class="n">FullyConnected</span><span class="p">(</span><span class="n">data</span><span class="o">=</span><span class="n">states</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">weight</span><span class="o">=</span><span class="n">h2h_weight</span><span class="p">,</span> <span class="n">bias</span><span class="o">=</span><span class="n">h2h_bias</span><span class="p">,</span> |
| <span class="n">num_hidden</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">_hidden_size</span><span class="o">*</span><span class="mi">4</span><span class="p">,</span> <span class="n">name</span><span class="o">=</span><span class="n">prefix</span><span class="o">+</span><span class="s1">'h2h'</span><span class="p">)</span> |
| <span class="n">gates</span> <span class="o">=</span> <span class="n">F</span><span class="o">.</span><span class="n">elemwise_add</span><span class="p">(</span><span class="n">i2h</span><span class="p">,</span> <span class="n">h2h</span><span class="p">,</span> <span class="n">name</span><span class="o">=</span><span class="n">prefix</span><span class="o">+</span><span class="s1">'plus0'</span><span class="p">)</span> |
| <span class="n">slice_gates</span> <span class="o">=</span> <span class="n">F</span><span class="o">.</span><span class="n">SliceChannel</span><span class="p">(</span><span class="n">gates</span><span class="p">,</span> <span class="n">num_outputs</span><span class="o">=</span><span class="mi">4</span><span class="p">,</span> <span class="n">name</span><span class="o">=</span><span class="n">prefix</span><span class="o">+</span><span class="s1">'slice'</span><span class="p">)</span> |
| <span class="n">in_gate</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_activation</span><span class="p">(</span> |
| <span class="n">F</span><span class="p">,</span> <span class="n">slice_gates</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="bp">self</span><span class="o">.</span><span class="n">_recurrent_activation</span><span class="p">,</span> <span class="n">name</span><span class="o">=</span><span class="n">prefix</span><span class="o">+</span><span class="s1">'i'</span><span class="p">)</span> |
| <span class="n">forget_gate</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_activation</span><span class="p">(</span> |
| <span class="n">F</span><span class="p">,</span> <span class="n">slice_gates</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="bp">self</span><span class="o">.</span><span class="n">_recurrent_activation</span><span class="p">,</span> <span class="n">name</span><span class="o">=</span><span class="n">prefix</span><span class="o">+</span><span class="s1">'f'</span><span class="p">)</span> |
| <span class="n">in_transform</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_activation</span><span class="p">(</span> |
| <span class="n">F</span><span class="p">,</span> <span class="n">slice_gates</span><span class="p">[</span><span class="mi">2</span><span class="p">],</span> <span class="bp">self</span><span class="o">.</span><span class="n">_activation</span><span class="p">,</span> <span class="n">name</span><span class="o">=</span><span class="n">prefix</span><span class="o">+</span><span class="s1">'c'</span><span class="p">)</span> |
| <span class="n">out_gate</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_activation</span><span class="p">(</span> |
| <span class="n">F</span><span class="p">,</span> <span class="n">slice_gates</span><span class="p">[</span><span class="mi">3</span><span class="p">],</span> <span class="bp">self</span><span class="o">.</span><span class="n">_recurrent_activation</span><span class="p">,</span> <span class="n">name</span><span class="o">=</span><span class="n">prefix</span><span class="o">+</span><span class="s1">'o'</span><span class="p">)</span> |
| <span class="n">next_c</span> <span class="o">=</span> <span class="n">F</span><span class="o">.</span><span class="n">elemwise_add</span><span class="p">(</span><span class="n">F</span><span class="o">.</span><span class="n">elemwise_mul</span><span class="p">(</span><span class="n">forget_gate</span><span class="p">,</span> <span class="n">states</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="n">name</span><span class="o">=</span><span class="n">prefix</span><span class="o">+</span><span class="s1">'mul0'</span><span class="p">),</span> |
| <span class="n">F</span><span class="o">.</span><span class="n">elemwise_mul</span><span class="p">(</span><span class="n">in_gate</span><span class="p">,</span> <span class="n">in_transform</span><span class="p">,</span> <span class="n">name</span><span class="o">=</span><span class="n">prefix</span><span class="o">+</span><span class="s1">'mul1'</span><span class="p">),</span> |
| <span class="n">name</span><span class="o">=</span><span class="n">prefix</span><span class="o">+</span><span class="s1">'state'</span><span class="p">)</span> |
| <span class="n">next_h</span> <span class="o">=</span> <span class="n">F</span><span class="o">.</span><span class="n">elemwise_mul</span><span class="p">(</span><span class="n">out_gate</span><span class="p">,</span> <span class="n">F</span><span class="o">.</span><span class="n">Activation</span><span class="p">(</span><span class="n">next_c</span><span class="p">,</span> <span class="n">act_type</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">_activation</span><span class="p">,</span> <span class="n">name</span><span class="o">=</span><span class="n">prefix</span><span class="o">+</span><span class="s1">'activation0'</span><span class="p">),</span> |
| <span class="n">name</span><span class="o">=</span><span class="n">prefix</span><span class="o">+</span><span class="s1">'out'</span><span class="p">)</span> |
| |
| <span class="k">return</span> <span class="n">next_h</span><span class="p">,</span> <span class="p">[</span><span class="n">next_h</span><span class="p">,</span> <span class="n">next_c</span><span class="p">]</span></div></div> |
| |
| |
| <div class="viewcode-block" id="GRUCell"><a class="viewcode-back" href="../../../../api/gluon/rnn/index.html#mxnet.gluon.rnn.GRUCell">[docs]</a><span class="k">class</span> <span class="nc">GRUCell</span><span class="p">(</span><span class="n">HybridRecurrentCell</span><span class="p">):</span> |
| <span class="sa">r</span><span class="sd">"""Gated Rectified Unit (GRU) network cell.</span> |
| <span class="sd"> Note: this is an implementation of the cuDNN version of GRUs</span> |
| <span class="sd"> (slight modification compared to Cho et al. 2014; the reset gate :math:`r_t`</span> |
| <span class="sd"> is applied after matrix multiplication).</span> |
| |
| <span class="sd"> Each call computes the following function:</span> |
| |
| <span class="sd"> .. math::</span> |
| <span class="sd"> \begin{array}{ll}</span> |
| <span class="sd"> r_t = sigmoid(W_{ir} x_t + b_{ir} + W_{hr} h_{(t-1)} + b_{hr}) \\</span> |
| <span class="sd"> i_t = sigmoid(W_{ii} x_t + b_{ii} + W_{hi} h_{(t-1)} + b_{hi}) \\</span> |
| <span class="sd"> n_t = \tanh(W_{in} x_t + b_{in} + r_t * (W_{hn} h_{(t-1)} + b_{hn})) \\</span> |
| <span class="sd"> h_t = (1 - i_t) * n_t + i_t * h_{(t-1)} \\</span> |
| <span class="sd"> \end{array}</span> |
| |
| <span class="sd"> where :math:`h_t` is the hidden state at time `t`, :math:`x_t` is the hidden</span> |
| <span class="sd"> state of the previous layer at time `t` or :math:`input_t` for the first layer,</span> |
| <span class="sd"> and :math:`r_t`, :math:`i_t`, :math:`n_t` are the reset, input, and new gates, respectively.</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> hidden_size : int</span> |
| <span class="sd"> Number of units in output symbol.</span> |
| <span class="sd"> i2h_weight_initializer : str or Initializer</span> |
| <span class="sd"> Initializer for the input weights matrix, used for the linear</span> |
| <span class="sd"> transformation of the inputs.</span> |
| <span class="sd"> h2h_weight_initializer : str or Initializer</span> |
| <span class="sd"> Initializer for the recurrent weights matrix, used for the linear</span> |
| <span class="sd"> transformation of the recurrent state.</span> |
| <span class="sd"> i2h_bias_initializer : str or Initializer, default 'zeros'</span> |
| <span class="sd"> Initializer for the bias vector.</span> |
| <span class="sd"> h2h_bias_initializer : str or Initializer, default 'zeros'</span> |
| <span class="sd"> Initializer for the bias vector.</span> |
| <span class="sd"> prefix : str, default ``'gru_'``</span> |
| <span class="sd"> prefix for name of `Block`s</span> |
| <span class="sd"> (and name of weight if params is `None`).</span> |
| <span class="sd"> params : Parameter or None, default None</span> |
| <span class="sd"> Container for weight sharing between cells.</span> |
| <span class="sd"> Created if `None`.</span> |
| |
| |
| <span class="sd"> Inputs:</span> |
| <span class="sd"> - **data**: input tensor with shape `(batch_size, input_size)`.</span> |
| <span class="sd"> - **states**: a list of one initial recurrent state tensor with shape</span> |
| <span class="sd"> `(batch_size, num_hidden)`.</span> |
| |
| <span class="sd"> Outputs:</span> |
| <span class="sd"> - **out**: output tensor with shape `(batch_size, num_hidden)`.</span> |
| <span class="sd"> - **next_states**: a list of one output recurrent state tensor with the</span> |
| <span class="sd"> same shape as `states`.</span> |
| <span class="sd"> """</span> |
| <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">hidden_size</span><span class="p">,</span> |
| <span class="n">i2h_weight_initializer</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">h2h_weight_initializer</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> |
| <span class="n">i2h_bias_initializer</span><span class="o">=</span><span class="s1">'zeros'</span><span class="p">,</span> <span class="n">h2h_bias_initializer</span><span class="o">=</span><span class="s1">'zeros'</span><span class="p">,</span> |
| <span class="n">input_size</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">prefix</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">params</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> |
| <span class="nb">super</span><span class="p">(</span><span class="n">GRUCell</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="n">prefix</span><span class="o">=</span><span class="n">prefix</span><span class="p">,</span> <span class="n">params</span><span class="o">=</span><span class="n">params</span><span class="p">)</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_hidden_size</span> <span class="o">=</span> <span class="n">hidden_size</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_input_size</span> <span class="o">=</span> <span class="n">input_size</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">i2h_weight</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">params</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">'i2h_weight'</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="o">*</span><span class="n">hidden_size</span><span class="p">,</span> <span class="n">input_size</span><span class="p">),</span> |
| <span class="n">init</span><span class="o">=</span><span class="n">i2h_weight_initializer</span><span class="p">,</span> |
| <span class="n">allow_deferred_init</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">h2h_weight</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">params</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">'h2h_weight'</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="o">*</span><span class="n">hidden_size</span><span class="p">,</span> <span class="n">hidden_size</span><span class="p">),</span> |
| <span class="n">init</span><span class="o">=</span><span class="n">h2h_weight_initializer</span><span class="p">,</span> |
| <span class="n">allow_deferred_init</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">i2h_bias</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">params</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">'i2h_bias'</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="o">*</span><span class="n">hidden_size</span><span class="p">,),</span> |
| <span class="n">init</span><span class="o">=</span><span class="n">i2h_bias_initializer</span><span class="p">,</span> |
| <span class="n">allow_deferred_init</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">h2h_bias</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">params</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">'h2h_bias'</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="o">*</span><span class="n">hidden_size</span><span class="p">,),</span> |
| <span class="n">init</span><span class="o">=</span><span class="n">h2h_bias_initializer</span><span class="p">,</span> |
| <span class="n">allow_deferred_init</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span> |
| |
| <div class="viewcode-block" id="GRUCell.state_info"><a class="viewcode-back" href="../../../../api/gluon/rnn/index.html#mxnet.gluon.rnn.GRUCell.state_info">[docs]</a> <span class="k">def</span> <span class="nf">state_info</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">batch_size</span><span class="o">=</span><span class="mi">0</span><span class="p">):</span> |
| <span class="k">return</span> <span class="p">[{</span><span class="s1">'shape'</span><span class="p">:</span> <span class="p">(</span><span class="n">batch_size</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">_hidden_size</span><span class="p">),</span> <span class="s1">'__layout__'</span><span class="p">:</span> <span class="s1">'NC'</span><span class="p">}]</span></div> |
| |
| <span class="k">def</span> <span class="nf">_alias</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span> |
| <span class="k">return</span> <span class="s1">'gru'</span> |
| |
| <span class="k">def</span> <span class="fm">__repr__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span> |
| <span class="n">s</span> <span class="o">=</span> <span class="s1">'</span><span class="si">{name}</span><span class="s1">(</span><span class="si">{mapping}</span><span class="s1">)'</span> |
| <span class="n">shape</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">i2h_weight</span><span class="o">.</span><span class="n">shape</span> |
| <span class="n">mapping</span> <span class="o">=</span> <span class="s1">'</span><span class="si">{0}</span><span class="s1"> -> </span><span class="si">{1}</span><span class="s1">'</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="k">if</span> <span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="k">else</span> <span class="kc">None</span><span class="p">,</span> <span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span> |
| <span class="k">return</span> <span class="n">s</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">name</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="vm">__class__</span><span class="o">.</span><span class="vm">__name__</span><span class="p">,</span> |
| <span class="n">mapping</span><span class="o">=</span><span class="n">mapping</span><span class="p">,</span> |
| <span class="o">**</span><span class="bp">self</span><span class="o">.</span><span class="vm">__dict__</span><span class="p">)</span> |
| |
| <div class="viewcode-block" id="GRUCell.hybrid_forward"><a class="viewcode-back" href="../../../../api/gluon/rnn/index.html#mxnet.gluon.rnn.GRUCell.hybrid_forward">[docs]</a> <span class="k">def</span> <span class="nf">hybrid_forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">F</span><span class="p">,</span> <span class="n">inputs</span><span class="p">,</span> <span class="n">states</span><span class="p">,</span> <span class="n">i2h_weight</span><span class="p">,</span> |
| <span class="n">h2h_weight</span><span class="p">,</span> <span class="n">i2h_bias</span><span class="p">,</span> <span class="n">h2h_bias</span><span class="p">):</span> |
| <span class="c1"># pylint: disable=too-many-locals</span> |
| <span class="n">prefix</span> <span class="o">=</span> <span class="s1">'t</span><span class="si">%d</span><span class="s1">_'</span><span class="o">%</span><span class="bp">self</span><span class="o">.</span><span class="n">_counter</span> |
| <span class="n">prev_state_h</span> <span class="o">=</span> <span class="n">states</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> |
| <span class="n">i2h</span> <span class="o">=</span> <span class="n">F</span><span class="o">.</span><span class="n">FullyConnected</span><span class="p">(</span><span class="n">data</span><span class="o">=</span><span class="n">inputs</span><span class="p">,</span> |
| <span class="n">weight</span><span class="o">=</span><span class="n">i2h_weight</span><span class="p">,</span> |
| <span class="n">bias</span><span class="o">=</span><span class="n">i2h_bias</span><span class="p">,</span> |
| <span class="n">num_hidden</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">_hidden_size</span> <span class="o">*</span> <span class="mi">3</span><span class="p">,</span> |
| <span class="n">name</span><span class="o">=</span><span class="n">prefix</span><span class="o">+</span><span class="s1">'i2h'</span><span class="p">)</span> |
| <span class="n">h2h</span> <span class="o">=</span> <span class="n">F</span><span class="o">.</span><span class="n">FullyConnected</span><span class="p">(</span><span class="n">data</span><span class="o">=</span><span class="n">prev_state_h</span><span class="p">,</span> |
| <span class="n">weight</span><span class="o">=</span><span class="n">h2h_weight</span><span class="p">,</span> |
| <span class="n">bias</span><span class="o">=</span><span class="n">h2h_bias</span><span class="p">,</span> |
| <span class="n">num_hidden</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">_hidden_size</span> <span class="o">*</span> <span class="mi">3</span><span class="p">,</span> |
| <span class="n">name</span><span class="o">=</span><span class="n">prefix</span><span class="o">+</span><span class="s1">'h2h'</span><span class="p">)</span> |
| |
| <span class="n">i2h_r</span><span class="p">,</span> <span class="n">i2h_z</span><span class="p">,</span> <span class="n">i2h</span> <span class="o">=</span> <span class="n">F</span><span class="o">.</span><span class="n">SliceChannel</span><span class="p">(</span><span class="n">i2h</span><span class="p">,</span> <span class="n">num_outputs</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span> |
| <span class="n">name</span><span class="o">=</span><span class="n">prefix</span><span class="o">+</span><span class="s1">'i2h_slice'</span><span class="p">)</span> |
| <span class="n">h2h_r</span><span class="p">,</span> <span class="n">h2h_z</span><span class="p">,</span> <span class="n">h2h</span> <span class="o">=</span> <span class="n">F</span><span class="o">.</span><span class="n">SliceChannel</span><span class="p">(</span><span class="n">h2h</span><span class="p">,</span> <span class="n">num_outputs</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span> |
| <span class="n">name</span><span class="o">=</span><span class="n">prefix</span><span class="o">+</span><span class="s1">'h2h_slice'</span><span class="p">)</span> |
| |
| <span class="n">reset_gate</span> <span class="o">=</span> <span class="n">F</span><span class="o">.</span><span class="n">Activation</span><span class="p">(</span><span class="n">F</span><span class="o">.</span><span class="n">elemwise_add</span><span class="p">(</span><span class="n">i2h_r</span><span class="p">,</span> <span class="n">h2h_r</span><span class="p">,</span> <span class="n">name</span><span class="o">=</span><span class="n">prefix</span><span class="o">+</span><span class="s1">'plus0'</span><span class="p">),</span> <span class="n">act_type</span><span class="o">=</span><span class="s2">"sigmoid"</span><span class="p">,</span> |
| <span class="n">name</span><span class="o">=</span><span class="n">prefix</span><span class="o">+</span><span class="s1">'r_act'</span><span class="p">)</span> |
| <span class="n">update_gate</span> <span class="o">=</span> <span class="n">F</span><span class="o">.</span><span class="n">Activation</span><span class="p">(</span><span class="n">F</span><span class="o">.</span><span class="n">elemwise_add</span><span class="p">(</span><span class="n">i2h_z</span><span class="p">,</span> <span class="n">h2h_z</span><span class="p">,</span> <span class="n">name</span><span class="o">=</span><span class="n">prefix</span><span class="o">+</span><span class="s1">'plus1'</span><span class="p">),</span> <span class="n">act_type</span><span class="o">=</span><span class="s2">"sigmoid"</span><span class="p">,</span> |
| <span class="n">name</span><span class="o">=</span><span class="n">prefix</span><span class="o">+</span><span class="s1">'z_act'</span><span class="p">)</span> |
| |
| <span class="n">next_h_tmp</span> <span class="o">=</span> <span class="n">F</span><span class="o">.</span><span class="n">Activation</span><span class="p">(</span><span class="n">F</span><span class="o">.</span><span class="n">elemwise_add</span><span class="p">(</span><span class="n">i2h</span><span class="p">,</span> |
| <span class="n">F</span><span class="o">.</span><span class="n">elemwise_mul</span><span class="p">(</span><span class="n">reset_gate</span><span class="p">,</span> <span class="n">h2h</span><span class="p">,</span> <span class="n">name</span><span class="o">=</span><span class="n">prefix</span><span class="o">+</span><span class="s1">'mul0'</span><span class="p">),</span> |
| <span class="n">name</span><span class="o">=</span><span class="n">prefix</span><span class="o">+</span><span class="s1">'plus2'</span><span class="p">),</span> |
| <span class="n">act_type</span><span class="o">=</span><span class="s2">"tanh"</span><span class="p">,</span> |
| <span class="n">name</span><span class="o">=</span><span class="n">prefix</span><span class="o">+</span><span class="s1">'h_act'</span><span class="p">)</span> |
| |
| <span class="n">ones</span> <span class="o">=</span> <span class="n">F</span><span class="o">.</span><span class="n">ones_like</span><span class="p">(</span><span class="n">update_gate</span><span class="p">,</span> <span class="n">name</span><span class="o">=</span><span class="n">prefix</span><span class="o">+</span><span class="s2">"ones_like0"</span><span class="p">)</span> |
| <span class="n">next_h</span> <span class="o">=</span> <span class="n">F</span><span class="o">.</span><span class="n">elemwise_add</span><span class="p">(</span><span class="n">F</span><span class="o">.</span><span class="n">elemwise_mul</span><span class="p">(</span><span class="n">F</span><span class="o">.</span><span class="n">elemwise_sub</span><span class="p">(</span><span class="n">ones</span><span class="p">,</span> <span class="n">update_gate</span><span class="p">,</span> <span class="n">name</span><span class="o">=</span><span class="n">prefix</span><span class="o">+</span><span class="s1">'minus0'</span><span class="p">),</span> |
| <span class="n">next_h_tmp</span><span class="p">,</span> |
| <span class="n">name</span><span class="o">=</span><span class="n">prefix</span><span class="o">+</span><span class="s1">'mul1'</span><span class="p">),</span> |
| <span class="n">F</span><span class="o">.</span><span class="n">elemwise_mul</span><span class="p">(</span><span class="n">update_gate</span><span class="p">,</span> <span class="n">prev_state_h</span><span class="p">,</span> <span class="n">name</span><span class="o">=</span><span class="n">prefix</span><span class="o">+</span><span class="s1">'mul20'</span><span class="p">),</span> |
| <span class="n">name</span><span class="o">=</span><span class="n">prefix</span><span class="o">+</span><span class="s1">'out'</span><span class="p">)</span> |
| |
| <span class="k">return</span> <span class="n">next_h</span><span class="p">,</span> <span class="p">[</span><span class="n">next_h</span><span class="p">]</span></div></div> |
| |
| |
| <div class="viewcode-block" id="SequentialRNNCell"><a class="viewcode-back" href="../../../../api/gluon/rnn/index.html#mxnet.gluon.rnn.SequentialRNNCell">[docs]</a><span class="k">class</span> <span class="nc">SequentialRNNCell</span><span class="p">(</span><span class="n">RecurrentCell</span><span class="p">):</span> |
| <span class="sd">"""Sequentially stacking multiple RNN cells."""</span> |
| <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">prefix</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">params</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> |
| <span class="nb">super</span><span class="p">(</span><span class="n">SequentialRNNCell</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="n">prefix</span><span class="o">=</span><span class="n">prefix</span><span class="p">,</span> <span class="n">params</span><span class="o">=</span><span class="n">params</span><span class="p">)</span> |
| |
| <span class="k">def</span> <span class="fm">__repr__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span> |
| <span class="n">s</span> <span class="o">=</span> <span class="s1">'</span><span class="si">{name}</span><span class="s1">(</span><span class="se">\n</span><span class="si">{modstr}</span><span class="se">\n</span><span class="s1">)'</span> |
| <span class="k">return</span> <span class="n">s</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">name</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="vm">__class__</span><span class="o">.</span><span class="vm">__name__</span><span class="p">,</span> |
| <span class="n">modstr</span><span class="o">=</span><span class="s1">'</span><span class="se">\n</span><span class="s1">'</span><span class="o">.</span><span class="n">join</span><span class="p">([</span><span class="s1">'(</span><span class="si">{i}</span><span class="s1">): </span><span class="si">{m}</span><span class="s1">'</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">i</span><span class="o">=</span><span class="n">i</span><span class="p">,</span> <span class="n">m</span><span class="o">=</span><span class="n">_indent</span><span class="p">(</span><span class="n">m</span><span class="o">.</span><span class="fm">__repr__</span><span class="p">(),</span> <span class="mi">2</span><span class="p">))</span> |
| <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">m</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">_children</span><span class="o">.</span><span class="n">items</span><span class="p">()]))</span> |
| |
| <div class="viewcode-block" id="SequentialRNNCell.add"><a class="viewcode-back" href="../../../../api/gluon/rnn/index.html#mxnet.gluon.rnn.SequentialRNNCell.add">[docs]</a> <span class="k">def</span> <span class="nf">add</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">cell</span><span class="p">):</span> |
| <span class="sd">"""Appends a cell into the stack.</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> cell : RecurrentCell</span> |
| <span class="sd"> The cell to add.</span> |
| <span class="sd"> """</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">register_child</span><span class="p">(</span><span class="n">cell</span><span class="p">)</span></div> |
| |
| <div class="viewcode-block" id="SequentialRNNCell.state_info"><a class="viewcode-back" href="../../../../api/gluon/rnn/index.html#mxnet.gluon.rnn.SequentialRNNCell.state_info">[docs]</a> <span class="k">def</span> <span class="nf">state_info</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">batch_size</span><span class="o">=</span><span class="mi">0</span><span class="p">):</span> |
| <span class="k">return</span> <span class="n">_cells_state_info</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_children</span><span class="o">.</span><span class="n">values</span><span class="p">(),</span> <span class="n">batch_size</span><span class="p">)</span></div> |
| |
| <div class="viewcode-block" id="SequentialRNNCell.begin_state"><a class="viewcode-back" href="../../../../api/gluon/rnn/index.html#mxnet.gluon.rnn.SequentialRNNCell.begin_state">[docs]</a> <span class="k">def</span> <span class="nf">begin_state</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span> |
| <span class="k">assert</span> <span class="ow">not</span> <span class="bp">self</span><span class="o">.</span><span class="n">_modified</span><span class="p">,</span> \ |
| <span class="s2">"After applying modifier cells (e.g. ZoneoutCell) the base "</span> \ |
| <span class="s2">"cell cannot be called directly. Call the modifier cell instead."</span> |
| <span class="k">return</span> <span class="n">_cells_begin_state</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_children</span><span class="o">.</span><span class="n">values</span><span class="p">(),</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span></div> |
| |
| <span class="k">def</span> <span class="fm">__call__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">inputs</span><span class="p">,</span> <span class="n">states</span><span class="p">):</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_counter</span> <span class="o">+=</span> <span class="mi">1</span> |
| <span class="n">next_states</span> <span class="o">=</span> <span class="p">[]</span> |
| <span class="n">p</span> <span class="o">=</span> <span class="mi">0</span> |
| <span class="k">assert</span> <span class="nb">all</span><span class="p">(</span><span class="ow">not</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">cell</span><span class="p">,</span> <span class="n">BidirectionalCell</span><span class="p">)</span> <span class="k">for</span> <span class="n">cell</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">_children</span><span class="o">.</span><span class="n">values</span><span class="p">())</span> |
| <span class="k">for</span> <span class="n">cell</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">_children</span><span class="o">.</span><span class="n">values</span><span class="p">():</span> |
| <span class="k">assert</span> <span class="ow">not</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">cell</span><span class="p">,</span> <span class="n">BidirectionalCell</span><span class="p">)</span> |
| <span class="n">n</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">cell</span><span class="o">.</span><span class="n">state_info</span><span class="p">())</span> |
| <span class="n">state</span> <span class="o">=</span> <span class="n">states</span><span class="p">[</span><span class="n">p</span><span class="p">:</span><span class="n">p</span><span class="o">+</span><span class="n">n</span><span class="p">]</span> |
| <span class="n">p</span> <span class="o">+=</span> <span class="n">n</span> |
| <span class="n">inputs</span><span class="p">,</span> <span class="n">state</span> <span class="o">=</span> <span class="n">cell</span><span class="p">(</span><span class="n">inputs</span><span class="p">,</span> <span class="n">state</span><span class="p">)</span> |
| <span class="n">next_states</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">state</span><span class="p">)</span> |
| <span class="k">return</span> <span class="n">inputs</span><span class="p">,</span> <span class="nb">sum</span><span class="p">(</span><span class="n">next_states</span><span class="p">,</span> <span class="p">[])</span> |
| |
| <div class="viewcode-block" id="SequentialRNNCell.unroll"><a class="viewcode-back" href="../../../../api/gluon/rnn/index.html#mxnet.gluon.rnn.SequentialRNNCell.unroll">[docs]</a> <span class="k">def</span> <span class="nf">unroll</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">length</span><span class="p">,</span> <span class="n">inputs</span><span class="p">,</span> <span class="n">begin_state</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">layout</span><span class="o">=</span><span class="s1">'NTC'</span><span class="p">,</span> <span class="n">merge_outputs</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> |
| <span class="n">valid_length</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> |
| <span class="c1"># pylint: disable=too-many-locals</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">reset</span><span class="p">()</span> |
| |
| <span class="n">inputs</span><span class="p">,</span> <span class="n">_</span><span class="p">,</span> <span class="n">F</span><span class="p">,</span> <span class="n">batch_size</span> <span class="o">=</span> <span class="n">_format_sequence</span><span class="p">(</span><span class="n">length</span><span class="p">,</span> <span class="n">inputs</span><span class="p">,</span> <span class="n">layout</span><span class="p">,</span> <span class="kc">None</span><span class="p">)</span> |
| <span class="n">num_cells</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_children</span><span class="p">)</span> |
| <span class="n">begin_state</span> <span class="o">=</span> <span class="n">_get_begin_state</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">F</span><span class="p">,</span> <span class="n">begin_state</span><span class="p">,</span> <span class="n">inputs</span><span class="p">,</span> <span class="n">batch_size</span><span class="p">)</span> |
| |
| <span class="n">p</span> <span class="o">=</span> <span class="mi">0</span> |
| <span class="n">next_states</span> <span class="o">=</span> <span class="p">[]</span> |
| <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">cell</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_children</span><span class="o">.</span><span class="n">values</span><span class="p">()):</span> |
| <span class="n">n</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">cell</span><span class="o">.</span><span class="n">state_info</span><span class="p">())</span> |
| <span class="n">states</span> <span class="o">=</span> <span class="n">begin_state</span><span class="p">[</span><span class="n">p</span><span class="p">:</span><span class="n">p</span><span class="o">+</span><span class="n">n</span><span class="p">]</span> |
| <span class="n">p</span> <span class="o">+=</span> <span class="n">n</span> |
| <span class="n">inputs</span><span class="p">,</span> <span class="n">states</span> <span class="o">=</span> <span class="n">cell</span><span class="o">.</span><span class="n">unroll</span><span class="p">(</span><span class="n">length</span><span class="p">,</span> <span class="n">inputs</span><span class="o">=</span><span class="n">inputs</span><span class="p">,</span> <span class="n">begin_state</span><span class="o">=</span><span class="n">states</span><span class="p">,</span> |
| <span class="n">layout</span><span class="o">=</span><span class="n">layout</span><span class="p">,</span> |
| <span class="n">merge_outputs</span><span class="o">=</span><span class="kc">None</span> <span class="k">if</span> <span class="n">i</span> <span class="o"><</span> <span class="n">num_cells</span><span class="o">-</span><span class="mi">1</span> <span class="k">else</span> <span class="n">merge_outputs</span><span class="p">,</span> |
| <span class="n">valid_length</span><span class="o">=</span><span class="n">valid_length</span><span class="p">)</span> |
| <span class="n">next_states</span><span class="o">.</span><span class="n">extend</span><span class="p">(</span><span class="n">states</span><span class="p">)</span> |
| |
| <span class="k">return</span> <span class="n">inputs</span><span class="p">,</span> <span class="n">next_states</span></div> |
| |
| <span class="k">def</span> <span class="fm">__getitem__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">i</span><span class="p">):</span> |
| <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_children</span><span class="p">[</span><span class="nb">str</span><span class="p">(</span><span class="n">i</span><span class="p">)]</span> |
| |
| <span class="k">def</span> <span class="fm">__len__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span> |
| <span class="k">return</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_children</span><span class="p">)</span> |
| |
| <span class="k">def</span> <span class="nf">hybrid_forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span> |
| <span class="c1"># pylint: disable=missing-docstring</span> |
| <span class="k">raise</span> <span class="ne">NotImplementedError</span></div> |
| |
| |
| <div class="viewcode-block" id="HybridSequentialRNNCell"><a class="viewcode-back" href="../../../../api/gluon/rnn/index.html#mxnet.gluon.rnn.HybridSequentialRNNCell">[docs]</a><span class="k">class</span> <span class="nc">HybridSequentialRNNCell</span><span class="p">(</span><span class="n">HybridRecurrentCell</span><span class="p">):</span> |
| <span class="sd">"""Sequentially stacking multiple HybridRNN cells."""</span> |
| <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">prefix</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">params</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> |
| <span class="nb">super</span><span class="p">(</span><span class="n">HybridSequentialRNNCell</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="n">prefix</span><span class="o">=</span><span class="n">prefix</span><span class="p">,</span> <span class="n">params</span><span class="o">=</span><span class="n">params</span><span class="p">)</span> |
| |
| <span class="k">def</span> <span class="fm">__repr__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span> |
| <span class="n">s</span> <span class="o">=</span> <span class="s1">'</span><span class="si">{name}</span><span class="s1">(</span><span class="se">\n</span><span class="si">{modstr}</span><span class="se">\n</span><span class="s1">)'</span> |
| <span class="k">return</span> <span class="n">s</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">name</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="vm">__class__</span><span class="o">.</span><span class="vm">__name__</span><span class="p">,</span> |
| <span class="n">modstr</span><span class="o">=</span><span class="s1">'</span><span class="se">\n</span><span class="s1">'</span><span class="o">.</span><span class="n">join</span><span class="p">([</span><span class="s1">'(</span><span class="si">{i}</span><span class="s1">): </span><span class="si">{m}</span><span class="s1">'</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">i</span><span class="o">=</span><span class="n">i</span><span class="p">,</span> <span class="n">m</span><span class="o">=</span><span class="n">_indent</span><span class="p">(</span><span class="n">m</span><span class="o">.</span><span class="fm">__repr__</span><span class="p">(),</span> <span class="mi">2</span><span class="p">))</span> |
| <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">m</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">_children</span><span class="o">.</span><span class="n">items</span><span class="p">()]))</span> |
| |
| <div class="viewcode-block" id="HybridSequentialRNNCell.add"><a class="viewcode-back" href="../../../../api/gluon/rnn/index.html#mxnet.gluon.rnn.HybridSequentialRNNCell.add">[docs]</a> <span class="k">def</span> <span class="nf">add</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">cell</span><span class="p">):</span> |
| <span class="sd">"""Appends a cell into the stack.</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> cell : RecurrentCell</span> |
| <span class="sd"> The cell to add.</span> |
| <span class="sd"> """</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">register_child</span><span class="p">(</span><span class="n">cell</span><span class="p">)</span></div> |
| |
| <div class="viewcode-block" id="HybridSequentialRNNCell.state_info"><a class="viewcode-back" href="../../../../api/gluon/rnn/index.html#mxnet.gluon.rnn.HybridSequentialRNNCell.state_info">[docs]</a> <span class="k">def</span> <span class="nf">state_info</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">batch_size</span><span class="o">=</span><span class="mi">0</span><span class="p">):</span> |
| <span class="k">return</span> <span class="n">_cells_state_info</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_children</span><span class="o">.</span><span class="n">values</span><span class="p">(),</span> <span class="n">batch_size</span><span class="p">)</span></div> |
| |
| <div class="viewcode-block" id="HybridSequentialRNNCell.begin_state"><a class="viewcode-back" href="../../../../api/gluon/rnn/index.html#mxnet.gluon.rnn.HybridSequentialRNNCell.begin_state">[docs]</a> <span class="k">def</span> <span class="nf">begin_state</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span> |
| <span class="k">assert</span> <span class="ow">not</span> <span class="bp">self</span><span class="o">.</span><span class="n">_modified</span><span class="p">,</span> \ |
| <span class="s2">"After applying modifier cells (e.g. ZoneoutCell) the base "</span> \ |
| <span class="s2">"cell cannot be called directly. Call the modifier cell instead."</span> |
| <span class="k">return</span> <span class="n">_cells_begin_state</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_children</span><span class="o">.</span><span class="n">values</span><span class="p">(),</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span></div> |
| |
| <span class="k">def</span> <span class="fm">__call__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">inputs</span><span class="p">,</span> <span class="n">states</span><span class="p">):</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_counter</span> <span class="o">+=</span> <span class="mi">1</span> |
| <span class="n">next_states</span> <span class="o">=</span> <span class="p">[]</span> |
| <span class="n">p</span> <span class="o">=</span> <span class="mi">0</span> |
| <span class="k">assert</span> <span class="nb">all</span><span class="p">(</span><span class="ow">not</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">cell</span><span class="p">,</span> <span class="n">BidirectionalCell</span><span class="p">)</span> <span class="k">for</span> <span class="n">cell</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">_children</span><span class="o">.</span><span class="n">values</span><span class="p">())</span> |
| <span class="k">for</span> <span class="n">cell</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">_children</span><span class="o">.</span><span class="n">values</span><span class="p">():</span> |
| <span class="n">n</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">cell</span><span class="o">.</span><span class="n">state_info</span><span class="p">())</span> |
| <span class="n">state</span> <span class="o">=</span> <span class="n">states</span><span class="p">[</span><span class="n">p</span><span class="p">:</span><span class="n">p</span><span class="o">+</span><span class="n">n</span><span class="p">]</span> |
| <span class="n">p</span> <span class="o">+=</span> <span class="n">n</span> |
| <span class="n">inputs</span><span class="p">,</span> <span class="n">state</span> <span class="o">=</span> <span class="n">cell</span><span class="p">(</span><span class="n">inputs</span><span class="p">,</span> <span class="n">state</span><span class="p">)</span> |
| <span class="n">next_states</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">state</span><span class="p">)</span> |
| <span class="k">return</span> <span class="n">inputs</span><span class="p">,</span> <span class="nb">sum</span><span class="p">(</span><span class="n">next_states</span><span class="p">,</span> <span class="p">[])</span> |
| |
| <div class="viewcode-block" id="HybridSequentialRNNCell.unroll"><a class="viewcode-back" href="../../../../api/gluon/rnn/index.html#mxnet.gluon.rnn.HybridSequentialRNNCell.unroll">[docs]</a> <span class="k">def</span> <span class="nf">unroll</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">length</span><span class="p">,</span> <span class="n">inputs</span><span class="p">,</span> <span class="n">begin_state</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">layout</span><span class="o">=</span><span class="s1">'NTC'</span><span class="p">,</span> <span class="n">merge_outputs</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> |
| <span class="n">valid_length</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">reset</span><span class="p">()</span> |
| |
| <span class="n">inputs</span><span class="p">,</span> <span class="n">_</span><span class="p">,</span> <span class="n">F</span><span class="p">,</span> <span class="n">batch_size</span> <span class="o">=</span> <span class="n">_format_sequence</span><span class="p">(</span><span class="n">length</span><span class="p">,</span> <span class="n">inputs</span><span class="p">,</span> <span class="n">layout</span><span class="p">,</span> <span class="kc">None</span><span class="p">)</span> |
| <span class="n">num_cells</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_children</span><span class="p">)</span> |
| <span class="n">begin_state</span> <span class="o">=</span> <span class="n">_get_begin_state</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">F</span><span class="p">,</span> <span class="n">begin_state</span><span class="p">,</span> <span class="n">inputs</span><span class="p">,</span> <span class="n">batch_size</span><span class="p">)</span> |
| |
| <span class="n">p</span> <span class="o">=</span> <span class="mi">0</span> |
| <span class="n">next_states</span> <span class="o">=</span> <span class="p">[]</span> |
| <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">cell</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_children</span><span class="o">.</span><span class="n">values</span><span class="p">()):</span> |
| <span class="n">n</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">cell</span><span class="o">.</span><span class="n">state_info</span><span class="p">())</span> |
| <span class="n">states</span> <span class="o">=</span> <span class="n">begin_state</span><span class="p">[</span><span class="n">p</span><span class="p">:</span><span class="n">p</span><span class="o">+</span><span class="n">n</span><span class="p">]</span> |
| <span class="n">p</span> <span class="o">+=</span> <span class="n">n</span> |
| <span class="n">inputs</span><span class="p">,</span> <span class="n">states</span> <span class="o">=</span> <span class="n">cell</span><span class="o">.</span><span class="n">unroll</span><span class="p">(</span><span class="n">length</span><span class="p">,</span> <span class="n">inputs</span><span class="o">=</span><span class="n">inputs</span><span class="p">,</span> <span class="n">begin_state</span><span class="o">=</span><span class="n">states</span><span class="p">,</span> |
| <span class="n">layout</span><span class="o">=</span><span class="n">layout</span><span class="p">,</span> |
| <span class="n">merge_outputs</span><span class="o">=</span><span class="kc">None</span> <span class="k">if</span> <span class="n">i</span> <span class="o"><</span> <span class="n">num_cells</span><span class="o">-</span><span class="mi">1</span> <span class="k">else</span> <span class="n">merge_outputs</span><span class="p">,</span> |
| <span class="n">valid_length</span><span class="o">=</span><span class="n">valid_length</span><span class="p">)</span> |
| <span class="n">next_states</span><span class="o">.</span><span class="n">extend</span><span class="p">(</span><span class="n">states</span><span class="p">)</span> |
| |
| <span class="k">return</span> <span class="n">inputs</span><span class="p">,</span> <span class="n">next_states</span></div> |
| |
| <span class="k">def</span> <span class="fm">__getitem__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">i</span><span class="p">):</span> |
| <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_children</span><span class="p">[</span><span class="nb">str</span><span class="p">(</span><span class="n">i</span><span class="p">)]</span> |
| |
| <span class="k">def</span> <span class="fm">__len__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span> |
| <span class="k">return</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_children</span><span class="p">)</span> |
| |
| <div class="viewcode-block" id="HybridSequentialRNNCell.hybrid_forward"><a class="viewcode-back" href="../../../../api/gluon/rnn/index.html#mxnet.gluon.rnn.HybridSequentialRNNCell.hybrid_forward">[docs]</a> <span class="k">def</span> <span class="nf">hybrid_forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">F</span><span class="p">,</span> <span class="n">inputs</span><span class="p">,</span> <span class="n">states</span><span class="p">):</span> |
| <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="fm">__call__</span><span class="p">(</span><span class="n">inputs</span><span class="p">,</span> <span class="n">states</span><span class="p">)</span></div></div> |
| |
| |
| <div class="viewcode-block" id="DropoutCell"><a class="viewcode-back" href="../../../../api/gluon/rnn/index.html#mxnet.gluon.rnn.DropoutCell">[docs]</a><span class="k">class</span> <span class="nc">DropoutCell</span><span class="p">(</span><span class="n">HybridRecurrentCell</span><span class="p">):</span> |
| <span class="sd">"""Applies dropout on input.</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> rate : float</span> |
| <span class="sd"> Percentage of elements to drop out, which</span> |
| <span class="sd"> is 1 - percentage to retain.</span> |
| <span class="sd"> axes : tuple of int, default ()</span> |
| <span class="sd"> The axes on which dropout mask is shared. If empty, regular dropout is applied.</span> |
| |
| |
| <span class="sd"> Inputs:</span> |
| <span class="sd"> - **data**: input tensor with shape `(batch_size, size)`.</span> |
| <span class="sd"> - **states**: a list of recurrent state tensors.</span> |
| |
| <span class="sd"> Outputs:</span> |
| <span class="sd"> - **out**: output tensor with shape `(batch_size, size)`.</span> |
| <span class="sd"> - **next_states**: returns input `states` directly.</span> |
| <span class="sd"> """</span> |
| <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">rate</span><span class="p">,</span> <span class="n">axes</span><span class="o">=</span><span class="p">(),</span> <span class="n">prefix</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">params</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> |
| <span class="nb">super</span><span class="p">(</span><span class="n">DropoutCell</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="n">prefix</span><span class="p">,</span> <span class="n">params</span><span class="p">)</span> |
| <span class="k">assert</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">rate</span><span class="p">,</span> <span class="n">numeric_types</span><span class="p">),</span> <span class="s2">"rate must be a number"</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_rate</span> <span class="o">=</span> <span class="n">rate</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_axes</span> <span class="o">=</span> <span class="n">axes</span> |
| |
| <span class="k">def</span> <span class="fm">__repr__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span> |
| <span class="n">s</span> <span class="o">=</span> <span class="s1">'</span><span class="si">{name}</span><span class="s1">(rate=</span><span class="si">{_rate}</span><span class="s1">, axes=</span><span class="si">{_axes}</span><span class="s1">)'</span> |
| <span class="k">return</span> <span class="n">s</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">name</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="vm">__class__</span><span class="o">.</span><span class="vm">__name__</span><span class="p">,</span> |
| <span class="o">**</span><span class="bp">self</span><span class="o">.</span><span class="vm">__dict__</span><span class="p">)</span> |
| |
| <div class="viewcode-block" id="DropoutCell.state_info"><a class="viewcode-back" href="../../../../api/gluon/rnn/index.html#mxnet.gluon.rnn.DropoutCell.state_info">[docs]</a> <span class="k">def</span> <span class="nf">state_info</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">batch_size</span><span class="o">=</span><span class="mi">0</span><span class="p">):</span> |
| <span class="k">return</span> <span class="p">[]</span></div> |
| |
| <span class="k">def</span> <span class="nf">_alias</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span> |
| <span class="k">return</span> <span class="s1">'dropout'</span> |
| |
| <div class="viewcode-block" id="DropoutCell.hybrid_forward"><a class="viewcode-back" href="../../../../api/gluon/rnn/index.html#mxnet.gluon.rnn.DropoutCell.hybrid_forward">[docs]</a> <span class="k">def</span> <span class="nf">hybrid_forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">F</span><span class="p">,</span> <span class="n">inputs</span><span class="p">,</span> <span class="n">states</span><span class="p">):</span> |
| <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_rate</span> <span class="o">></span> <span class="mi">0</span><span class="p">:</span> |
| <span class="n">inputs</span> <span class="o">=</span> <span class="n">F</span><span class="o">.</span><span class="n">Dropout</span><span class="p">(</span><span class="n">data</span><span class="o">=</span><span class="n">inputs</span><span class="p">,</span> <span class="n">p</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">_rate</span><span class="p">,</span> <span class="n">axes</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">_axes</span><span class="p">,</span> |
| <span class="n">name</span><span class="o">=</span><span class="s1">'t</span><span class="si">%d</span><span class="s1">_fwd'</span><span class="o">%</span><span class="bp">self</span><span class="o">.</span><span class="n">_counter</span><span class="p">)</span> |
| <span class="k">return</span> <span class="n">inputs</span><span class="p">,</span> <span class="n">states</span></div> |
| |
| <div class="viewcode-block" id="DropoutCell.unroll"><a class="viewcode-back" href="../../../../api/gluon/rnn/index.html#mxnet.gluon.rnn.DropoutCell.unroll">[docs]</a> <span class="k">def</span> <span class="nf">unroll</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">length</span><span class="p">,</span> <span class="n">inputs</span><span class="p">,</span> <span class="n">begin_state</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">layout</span><span class="o">=</span><span class="s1">'NTC'</span><span class="p">,</span> <span class="n">merge_outputs</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> |
| <span class="n">valid_length</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">reset</span><span class="p">()</span> |
| |
| <span class="n">inputs</span><span class="p">,</span> <span class="n">_</span><span class="p">,</span> <span class="n">F</span><span class="p">,</span> <span class="n">_</span> <span class="o">=</span> <span class="n">_format_sequence</span><span class="p">(</span><span class="n">length</span><span class="p">,</span> <span class="n">inputs</span><span class="p">,</span> <span class="n">layout</span><span class="p">,</span> <span class="n">merge_outputs</span><span class="p">)</span> |
| <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">inputs</span><span class="p">,</span> <span class="n">tensor_types</span><span class="p">):</span> |
| <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">hybrid_forward</span><span class="p">(</span><span class="n">F</span><span class="p">,</span> <span class="n">inputs</span><span class="p">,</span> <span class="n">begin_state</span> <span class="k">if</span> <span class="n">begin_state</span> <span class="k">else</span> <span class="p">[])</span> |
| <span class="k">return</span> <span class="nb">super</span><span class="p">(</span><span class="n">DropoutCell</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="n">unroll</span><span class="p">(</span> |
| <span class="n">length</span><span class="p">,</span> <span class="n">inputs</span><span class="p">,</span> <span class="n">begin_state</span><span class="o">=</span><span class="n">begin_state</span><span class="p">,</span> <span class="n">layout</span><span class="o">=</span><span class="n">layout</span><span class="p">,</span> |
| <span class="n">merge_outputs</span><span class="o">=</span><span class="n">merge_outputs</span><span class="p">,</span> <span class="n">valid_length</span><span class="o">=</span><span class="kc">None</span><span class="p">)</span></div></div> |
| |
| |
| <div class="viewcode-block" id="ModifierCell"><a class="viewcode-back" href="../../../../api/gluon/rnn/index.html#mxnet.gluon.rnn.ModifierCell">[docs]</a><span class="k">class</span> <span class="nc">ModifierCell</span><span class="p">(</span><span class="n">HybridRecurrentCell</span><span class="p">):</span> |
| <span class="sd">"""Base class for modifier cells. A modifier</span> |
| <span class="sd"> cell takes a base cell, apply modifications</span> |
| <span class="sd"> on it (e.g. Zoneout), and returns a new cell.</span> |
| |
| <span class="sd"> After applying modifiers the base cell should</span> |
| <span class="sd"> no longer be called directly. The modifier cell</span> |
| <span class="sd"> should be used instead.</span> |
| <span class="sd"> """</span> |
| <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">base_cell</span><span class="p">):</span> |
| <span class="k">assert</span> <span class="ow">not</span> <span class="n">base_cell</span><span class="o">.</span><span class="n">_modified</span><span class="p">,</span> \ |
| <span class="s2">"Cell </span><span class="si">%s</span><span class="s2"> is already modified. One cell cannot be modified twice"</span><span class="o">%</span><span class="n">base_cell</span><span class="o">.</span><span class="n">name</span> |
| <span class="n">base_cell</span><span class="o">.</span><span class="n">_modified</span> <span class="o">=</span> <span class="kc">True</span> |
| <span class="nb">super</span><span class="p">(</span><span class="n">ModifierCell</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="n">prefix</span><span class="o">=</span><span class="n">base_cell</span><span class="o">.</span><span class="n">prefix</span><span class="o">+</span><span class="bp">self</span><span class="o">.</span><span class="n">_alias</span><span class="p">(),</span> |
| <span class="n">params</span><span class="o">=</span><span class="kc">None</span><span class="p">)</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">base_cell</span> <span class="o">=</span> <span class="n">base_cell</span> |
| |
| <span class="nd">@property</span> |
| <span class="k">def</span> <span class="nf">params</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span> |
| <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">base_cell</span><span class="o">.</span><span class="n">params</span> |
| |
| <div class="viewcode-block" id="ModifierCell.state_info"><a class="viewcode-back" href="../../../../api/gluon/rnn/index.html#mxnet.gluon.rnn.ModifierCell.state_info">[docs]</a> <span class="k">def</span> <span class="nf">state_info</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">batch_size</span><span class="o">=</span><span class="mi">0</span><span class="p">):</span> |
| <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">base_cell</span><span class="o">.</span><span class="n">state_info</span><span class="p">(</span><span class="n">batch_size</span><span class="p">)</span></div> |
| |
| <div class="viewcode-block" id="ModifierCell.begin_state"><a class="viewcode-back" href="../../../../api/gluon/rnn/index.html#mxnet.gluon.rnn.ModifierCell.begin_state">[docs]</a> <span class="k">def</span> <span class="nf">begin_state</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">func</span><span class="o">=</span><span class="n">symbol</span><span class="o">.</span><span class="n">zeros</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span> |
| <span class="k">assert</span> <span class="ow">not</span> <span class="bp">self</span><span class="o">.</span><span class="n">_modified</span><span class="p">,</span> \ |
| <span class="s2">"After applying modifier cells (e.g. DropoutCell) the base "</span> \ |
| <span class="s2">"cell cannot be called directly. Call the modifier cell instead."</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">base_cell</span><span class="o">.</span><span class="n">_modified</span> <span class="o">=</span> <span class="kc">False</span> |
| <span class="n">begin</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">base_cell</span><span class="o">.</span><span class="n">begin_state</span><span class="p">(</span><span class="n">func</span><span class="o">=</span><span class="n">func</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">base_cell</span><span class="o">.</span><span class="n">_modified</span> <span class="o">=</span> <span class="kc">True</span> |
| <span class="k">return</span> <span class="n">begin</span></div> |
| |
| <div class="viewcode-block" id="ModifierCell.hybrid_forward"><a class="viewcode-back" href="../../../../api/gluon/rnn/index.html#mxnet.gluon.rnn.ModifierCell.hybrid_forward">[docs]</a> <span class="k">def</span> <span class="nf">hybrid_forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">F</span><span class="p">,</span> <span class="n">inputs</span><span class="p">,</span> <span class="n">states</span><span class="p">):</span> |
| <span class="k">raise</span> <span class="ne">NotImplementedError</span></div> |
| |
| <span class="k">def</span> <span class="fm">__repr__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span> |
| <span class="n">s</span> <span class="o">=</span> <span class="s1">'</span><span class="si">{name}</span><span class="s1">(</span><span class="si">{base_cell}</span><span class="s1">)'</span> |
| <span class="k">return</span> <span class="n">s</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">name</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="vm">__class__</span><span class="o">.</span><span class="vm">__name__</span><span class="p">,</span> |
| <span class="o">**</span><span class="bp">self</span><span class="o">.</span><span class="vm">__dict__</span><span class="p">)</span></div> |
| |
| |
| <div class="viewcode-block" id="ZoneoutCell"><a class="viewcode-back" href="../../../../api/gluon/rnn/index.html#mxnet.gluon.rnn.ZoneoutCell">[docs]</a><span class="k">class</span> <span class="nc">ZoneoutCell</span><span class="p">(</span><span class="n">ModifierCell</span><span class="p">):</span> |
| <span class="sd">"""Applies Zoneout on base cell."""</span> |
| <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">base_cell</span><span class="p">,</span> <span class="n">zoneout_outputs</span><span class="o">=</span><span class="mf">0.</span><span class="p">,</span> <span class="n">zoneout_states</span><span class="o">=</span><span class="mf">0.</span><span class="p">):</span> |
| <span class="k">assert</span> <span class="ow">not</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">base_cell</span><span class="p">,</span> <span class="n">BidirectionalCell</span><span class="p">),</span> \ |
| <span class="s2">"BidirectionalCell doesn't support zoneout since it doesn't support step. "</span> \ |
| <span class="s2">"Please add ZoneoutCell to the cells underneath instead."</span> |
| <span class="k">assert</span> <span class="ow">not</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">base_cell</span><span class="p">,</span> <span class="n">SequentialRNNCell</span><span class="p">)</span> <span class="ow">or</span> <span class="ow">not</span> <span class="n">base_cell</span><span class="o">.</span><span class="n">_bidirectional</span><span class="p">,</span> \ |
| <span class="s2">"Bidirectional SequentialRNNCell doesn't support zoneout. "</span> \ |
| <span class="s2">"Please add ZoneoutCell to the cells underneath instead."</span> |
| <span class="nb">super</span><span class="p">(</span><span class="n">ZoneoutCell</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="n">base_cell</span><span class="p">)</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">zoneout_outputs</span> <span class="o">=</span> <span class="n">zoneout_outputs</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">zoneout_states</span> <span class="o">=</span> <span class="n">zoneout_states</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_prev_output</span> <span class="o">=</span> <span class="kc">None</span> |
| |
| <span class="k">def</span> <span class="fm">__repr__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span> |
| <span class="n">s</span> <span class="o">=</span> <span class="s1">'</span><span class="si">{name}</span><span class="s1">(p_out=</span><span class="si">{zoneout_outputs}</span><span class="s1">, p_state=</span><span class="si">{zoneout_states}</span><span class="s1">, </span><span class="si">{base_cell}</span><span class="s1">)'</span> |
| <span class="k">return</span> <span class="n">s</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">name</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="vm">__class__</span><span class="o">.</span><span class="vm">__name__</span><span class="p">,</span> |
| <span class="o">**</span><span class="bp">self</span><span class="o">.</span><span class="vm">__dict__</span><span class="p">)</span> |
| |
| <span class="k">def</span> <span class="nf">_alias</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span> |
| <span class="k">return</span> <span class="s1">'zoneout'</span> |
| |
| <div class="viewcode-block" id="ZoneoutCell.reset"><a class="viewcode-back" href="../../../../api/gluon/rnn/index.html#mxnet.gluon.rnn.ZoneoutCell.reset">[docs]</a> <span class="k">def</span> <span class="nf">reset</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span> |
| <span class="nb">super</span><span class="p">(</span><span class="n">ZoneoutCell</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="n">reset</span><span class="p">()</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_prev_output</span> <span class="o">=</span> <span class="kc">None</span></div> |
| |
| <div class="viewcode-block" id="ZoneoutCell.hybrid_forward"><a class="viewcode-back" href="../../../../api/gluon/rnn/index.html#mxnet.gluon.rnn.ZoneoutCell.hybrid_forward">[docs]</a> <span class="k">def</span> <span class="nf">hybrid_forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">F</span><span class="p">,</span> <span class="n">inputs</span><span class="p">,</span> <span class="n">states</span><span class="p">):</span> |
| <span class="n">cell</span><span class="p">,</span> <span class="n">p_outputs</span><span class="p">,</span> <span class="n">p_states</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">base_cell</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">zoneout_outputs</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">zoneout_states</span> |
| <span class="n">next_output</span><span class="p">,</span> <span class="n">next_states</span> <span class="o">=</span> <span class="n">cell</span><span class="p">(</span><span class="n">inputs</span><span class="p">,</span> <span class="n">states</span><span class="p">)</span> |
| <span class="n">mask</span> <span class="o">=</span> <span class="p">(</span><span class="k">lambda</span> <span class="n">p</span><span class="p">,</span> <span class="n">like</span><span class="p">:</span> <span class="n">F</span><span class="o">.</span><span class="n">Dropout</span><span class="p">(</span><span class="n">F</span><span class="o">.</span><span class="n">ones_like</span><span class="p">(</span><span class="n">like</span><span class="p">),</span> <span class="n">p</span><span class="o">=</span><span class="n">p</span><span class="p">))</span> |
| |
| <span class="n">prev_output</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_prev_output</span> |
| <span class="k">if</span> <span class="n">prev_output</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span> |
| <span class="n">prev_output</span> <span class="o">=</span> <span class="n">F</span><span class="o">.</span><span class="n">zeros_like</span><span class="p">(</span><span class="n">next_output</span><span class="p">)</span> |
| |
| <span class="n">output</span> <span class="o">=</span> <span class="p">(</span><span class="n">F</span><span class="o">.</span><span class="n">where</span><span class="p">(</span><span class="n">mask</span><span class="p">(</span><span class="n">p_outputs</span><span class="p">,</span> <span class="n">next_output</span><span class="p">),</span> <span class="n">next_output</span><span class="p">,</span> <span class="n">prev_output</span><span class="p">)</span> |
| <span class="k">if</span> <span class="n">p_outputs</span> <span class="o">!=</span> <span class="mf">0.</span> <span class="k">else</span> <span class="n">next_output</span><span class="p">)</span> |
| <span class="n">states</span> <span class="o">=</span> <span class="p">([</span><span class="n">F</span><span class="o">.</span><span class="n">where</span><span class="p">(</span><span class="n">mask</span><span class="p">(</span><span class="n">p_states</span><span class="p">,</span> <span class="n">new_s</span><span class="p">),</span> <span class="n">new_s</span><span class="p">,</span> <span class="n">old_s</span><span class="p">)</span> <span class="k">for</span> <span class="n">new_s</span><span class="p">,</span> <span class="n">old_s</span> <span class="ow">in</span> |
| <span class="nb">zip</span><span class="p">(</span><span class="n">next_states</span><span class="p">,</span> <span class="n">states</span><span class="p">)]</span> <span class="k">if</span> <span class="n">p_states</span> <span class="o">!=</span> <span class="mf">0.</span> <span class="k">else</span> <span class="n">next_states</span><span class="p">)</span> |
| |
| <span class="bp">self</span><span class="o">.</span><span class="n">_prev_output</span> <span class="o">=</span> <span class="n">output</span> |
| |
| <span class="k">return</span> <span class="n">output</span><span class="p">,</span> <span class="n">states</span></div></div> |
| |
| |
| <div class="viewcode-block" id="ResidualCell"><a class="viewcode-back" href="../../../../api/gluon/rnn/index.html#mxnet.gluon.rnn.ResidualCell">[docs]</a><span class="k">class</span> <span class="nc">ResidualCell</span><span class="p">(</span><span class="n">ModifierCell</span><span class="p">):</span> |
| <span class="sd">"""</span> |
| <span class="sd"> Adds residual connection as described in Wu et al, 2016</span> |
| <span class="sd"> (https://arxiv.org/abs/1609.08144).</span> |
| <span class="sd"> Output of the cell is output of the base cell plus input.</span> |
| <span class="sd"> """</span> |
| |
| <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">base_cell</span><span class="p">):</span> |
| <span class="c1"># pylint: disable=useless-super-delegation</span> |
| <span class="nb">super</span><span class="p">(</span><span class="n">ResidualCell</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="n">base_cell</span><span class="p">)</span> |
| |
| <div class="viewcode-block" id="ResidualCell.hybrid_forward"><a class="viewcode-back" href="../../../../api/gluon/rnn/index.html#mxnet.gluon.rnn.ResidualCell.hybrid_forward">[docs]</a> <span class="k">def</span> <span class="nf">hybrid_forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">F</span><span class="p">,</span> <span class="n">inputs</span><span class="p">,</span> <span class="n">states</span><span class="p">):</span> |
| <span class="n">output</span><span class="p">,</span> <span class="n">states</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">base_cell</span><span class="p">(</span><span class="n">inputs</span><span class="p">,</span> <span class="n">states</span><span class="p">)</span> |
| <span class="n">output</span> <span class="o">=</span> <span class="n">F</span><span class="o">.</span><span class="n">elemwise_add</span><span class="p">(</span><span class="n">output</span><span class="p">,</span> <span class="n">inputs</span><span class="p">,</span> <span class="n">name</span><span class="o">=</span><span class="s1">'t</span><span class="si">%d</span><span class="s1">_fwd'</span><span class="o">%</span><span class="bp">self</span><span class="o">.</span><span class="n">_counter</span><span class="p">)</span> |
| <span class="k">return</span> <span class="n">output</span><span class="p">,</span> <span class="n">states</span></div> |
| |
| <div class="viewcode-block" id="ResidualCell.unroll"><a class="viewcode-back" href="../../../../api/gluon/rnn/index.html#mxnet.gluon.rnn.ResidualCell.unroll">[docs]</a> <span class="k">def</span> <span class="nf">unroll</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">length</span><span class="p">,</span> <span class="n">inputs</span><span class="p">,</span> <span class="n">begin_state</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">layout</span><span class="o">=</span><span class="s1">'NTC'</span><span class="p">,</span> <span class="n">merge_outputs</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> |
| <span class="n">valid_length</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">reset</span><span class="p">()</span> |
| |
| <span class="bp">self</span><span class="o">.</span><span class="n">base_cell</span><span class="o">.</span><span class="n">_modified</span> <span class="o">=</span> <span class="kc">False</span> |
| <span class="n">outputs</span><span class="p">,</span> <span class="n">states</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">base_cell</span><span class="o">.</span><span class="n">unroll</span><span class="p">(</span><span class="n">length</span><span class="p">,</span> <span class="n">inputs</span><span class="o">=</span><span class="n">inputs</span><span class="p">,</span> <span class="n">begin_state</span><span class="o">=</span><span class="n">begin_state</span><span class="p">,</span> |
| <span class="n">layout</span><span class="o">=</span><span class="n">layout</span><span class="p">,</span> <span class="n">merge_outputs</span><span class="o">=</span><span class="n">merge_outputs</span><span class="p">,</span> |
| <span class="n">valid_length</span><span class="o">=</span><span class="n">valid_length</span><span class="p">)</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">base_cell</span><span class="o">.</span><span class="n">_modified</span> <span class="o">=</span> <span class="kc">True</span> |
| |
| <span class="n">merge_outputs</span> <span class="o">=</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">outputs</span><span class="p">,</span> <span class="n">tensor_types</span><span class="p">)</span> <span class="k">if</span> <span class="n">merge_outputs</span> <span class="ow">is</span> <span class="kc">None</span> <span class="k">else</span> \ |
| <span class="n">merge_outputs</span> |
| <span class="n">inputs</span><span class="p">,</span> <span class="n">axis</span><span class="p">,</span> <span class="n">F</span><span class="p">,</span> <span class="n">_</span> <span class="o">=</span> <span class="n">_format_sequence</span><span class="p">(</span><span class="n">length</span><span class="p">,</span> <span class="n">inputs</span><span class="p">,</span> <span class="n">layout</span><span class="p">,</span> <span class="n">merge_outputs</span><span class="p">)</span> |
| <span class="k">if</span> <span class="n">valid_length</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span> |
| <span class="c1"># mask the padded inputs to zero</span> |
| <span class="n">inputs</span> <span class="o">=</span> <span class="n">_mask_sequence_variable_length</span><span class="p">(</span><span class="n">F</span><span class="p">,</span> <span class="n">inputs</span><span class="p">,</span> <span class="n">length</span><span class="p">,</span> <span class="n">valid_length</span><span class="p">,</span> <span class="n">axis</span><span class="p">,</span> |
| <span class="n">merge_outputs</span><span class="p">)</span> |
| <span class="k">if</span> <span class="n">merge_outputs</span><span class="p">:</span> |
| <span class="n">outputs</span> <span class="o">=</span> <span class="n">F</span><span class="o">.</span><span class="n">elemwise_add</span><span class="p">(</span><span class="n">outputs</span><span class="p">,</span> <span class="n">inputs</span><span class="p">)</span> |
| <span class="k">else</span><span class="p">:</span> |
| <span class="n">outputs</span> <span class="o">=</span> <span class="p">[</span><span class="n">F</span><span class="o">.</span><span class="n">elemwise_add</span><span class="p">(</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">)</span> <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">j</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="n">outputs</span><span class="p">,</span> <span class="n">inputs</span><span class="p">)]</span> |
| |
| <span class="k">return</span> <span class="n">outputs</span><span class="p">,</span> <span class="n">states</span></div></div> |
| |
| |
| <div class="viewcode-block" id="BidirectionalCell"><a class="viewcode-back" href="../../../../api/gluon/rnn/index.html#mxnet.gluon.rnn.BidirectionalCell">[docs]</a><span class="k">class</span> <span class="nc">BidirectionalCell</span><span class="p">(</span><span class="n">HybridRecurrentCell</span><span class="p">):</span> |
| <span class="sd">"""Bidirectional RNN cell.</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> l_cell : RecurrentCell</span> |
| <span class="sd"> Cell for forward unrolling</span> |
| <span class="sd"> r_cell : RecurrentCell</span> |
| <span class="sd"> Cell for backward unrolling</span> |
| <span class="sd"> """</span> |
| <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">l_cell</span><span class="p">,</span> <span class="n">r_cell</span><span class="p">,</span> <span class="n">output_prefix</span><span class="o">=</span><span class="s1">'bi_'</span><span class="p">):</span> |
| <span class="nb">super</span><span class="p">(</span><span class="n">BidirectionalCell</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="n">prefix</span><span class="o">=</span><span class="s1">''</span><span class="p">,</span> <span class="n">params</span><span class="o">=</span><span class="kc">None</span><span class="p">)</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">register_child</span><span class="p">(</span><span class="n">l_cell</span><span class="p">,</span> <span class="s1">'l_cell'</span><span class="p">)</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">register_child</span><span class="p">(</span><span class="n">r_cell</span><span class="p">,</span> <span class="s1">'r_cell'</span><span class="p">)</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_output_prefix</span> <span class="o">=</span> <span class="n">output_prefix</span> |
| |
| <span class="k">def</span> <span class="fm">__call__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">inputs</span><span class="p">,</span> <span class="n">states</span><span class="p">):</span> |
| <span class="k">raise</span> <span class="ne">NotImplementedError</span><span class="p">(</span><span class="s2">"Bidirectional cannot be stepped. Please use unroll"</span><span class="p">)</span> |
| |
| <span class="k">def</span> <span class="fm">__repr__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span> |
| <span class="n">s</span> <span class="o">=</span> <span class="s1">'</span><span class="si">{name}</span><span class="s1">(forward=</span><span class="si">{l_cell}</span><span class="s1">, backward=</span><span class="si">{r_cell}</span><span class="s1">)'</span> |
| <span class="k">return</span> <span class="n">s</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">name</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="vm">__class__</span><span class="o">.</span><span class="vm">__name__</span><span class="p">,</span> |
| <span class="n">l_cell</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">_children</span><span class="p">[</span><span class="s1">'l_cell'</span><span class="p">],</span> |
| <span class="n">r_cell</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">_children</span><span class="p">[</span><span class="s1">'r_cell'</span><span class="p">])</span> |
| |
| <div class="viewcode-block" id="BidirectionalCell.state_info"><a class="viewcode-back" href="../../../../api/gluon/rnn/index.html#mxnet.gluon.rnn.BidirectionalCell.state_info">[docs]</a> <span class="k">def</span> <span class="nf">state_info</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">batch_size</span><span class="o">=</span><span class="mi">0</span><span class="p">):</span> |
| <span class="k">return</span> <span class="n">_cells_state_info</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_children</span><span class="o">.</span><span class="n">values</span><span class="p">(),</span> <span class="n">batch_size</span><span class="p">)</span></div> |
| |
| <div class="viewcode-block" id="BidirectionalCell.begin_state"><a class="viewcode-back" href="../../../../api/gluon/rnn/index.html#mxnet.gluon.rnn.BidirectionalCell.begin_state">[docs]</a> <span class="k">def</span> <span class="nf">begin_state</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span> |
| <span class="k">assert</span> <span class="ow">not</span> <span class="bp">self</span><span class="o">.</span><span class="n">_modified</span><span class="p">,</span> \ |
| <span class="s2">"After applying modifier cells (e.g. DropoutCell) the base "</span> \ |
| <span class="s2">"cell cannot be called directly. Call the modifier cell instead."</span> |
| <span class="k">return</span> <span class="n">_cells_begin_state</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_children</span><span class="o">.</span><span class="n">values</span><span class="p">(),</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span></div> |
| |
| <div class="viewcode-block" id="BidirectionalCell.unroll"><a class="viewcode-back" href="../../../../api/gluon/rnn/index.html#mxnet.gluon.rnn.BidirectionalCell.unroll">[docs]</a> <span class="k">def</span> <span class="nf">unroll</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">length</span><span class="p">,</span> <span class="n">inputs</span><span class="p">,</span> <span class="n">begin_state</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">layout</span><span class="o">=</span><span class="s1">'NTC'</span><span class="p">,</span> <span class="n">merge_outputs</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> |
| <span class="n">valid_length</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> |
| <span class="c1"># pylint: disable=too-many-locals</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">reset</span><span class="p">()</span> |
| |
| <span class="n">inputs</span><span class="p">,</span> <span class="n">axis</span><span class="p">,</span> <span class="n">F</span><span class="p">,</span> <span class="n">batch_size</span> <span class="o">=</span> <span class="n">_format_sequence</span><span class="p">(</span><span class="n">length</span><span class="p">,</span> <span class="n">inputs</span><span class="p">,</span> <span class="n">layout</span><span class="p">,</span> <span class="kc">False</span><span class="p">)</span> |
| <span class="n">reversed_inputs</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="n">_reverse_sequences</span><span class="p">(</span><span class="n">inputs</span><span class="p">,</span> <span class="n">length</span><span class="p">,</span> <span class="n">valid_length</span><span class="p">))</span> |
| <span class="n">begin_state</span> <span class="o">=</span> <span class="n">_get_begin_state</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">F</span><span class="p">,</span> <span class="n">begin_state</span><span class="p">,</span> <span class="n">inputs</span><span class="p">,</span> <span class="n">batch_size</span><span class="p">)</span> |
| |
| <span class="n">states</span> <span class="o">=</span> <span class="n">begin_state</span> |
| <span class="n">l_cell</span><span class="p">,</span> <span class="n">r_cell</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_children</span><span class="o">.</span><span class="n">values</span><span class="p">()</span> |
| <span class="n">l_outputs</span><span class="p">,</span> <span class="n">l_states</span> <span class="o">=</span> <span class="n">l_cell</span><span class="o">.</span><span class="n">unroll</span><span class="p">(</span><span class="n">length</span><span class="p">,</span> <span class="n">inputs</span><span class="o">=</span><span class="n">inputs</span><span class="p">,</span> |
| <span class="n">begin_state</span><span class="o">=</span><span class="n">states</span><span class="p">[:</span><span class="nb">len</span><span class="p">(</span><span class="n">l_cell</span><span class="o">.</span><span class="n">state_info</span><span class="p">(</span><span class="n">batch_size</span><span class="p">))],</span> |
| <span class="n">layout</span><span class="o">=</span><span class="n">layout</span><span class="p">,</span> <span class="n">merge_outputs</span><span class="o">=</span><span class="n">merge_outputs</span><span class="p">,</span> |
| <span class="n">valid_length</span><span class="o">=</span><span class="n">valid_length</span><span class="p">)</span> |
| <span class="n">r_outputs</span><span class="p">,</span> <span class="n">r_states</span> <span class="o">=</span> <span class="n">r_cell</span><span class="o">.</span><span class="n">unroll</span><span class="p">(</span><span class="n">length</span><span class="p">,</span> |
| <span class="n">inputs</span><span class="o">=</span><span class="n">reversed_inputs</span><span class="p">,</span> |
| <span class="n">begin_state</span><span class="o">=</span><span class="n">states</span><span class="p">[</span><span class="nb">len</span><span class="p">(</span><span class="n">l_cell</span><span class="o">.</span><span class="n">state_info</span><span class="p">(</span><span class="n">batch_size</span><span class="p">)):],</span> |
| <span class="n">layout</span><span class="o">=</span><span class="n">layout</span><span class="p">,</span> <span class="n">merge_outputs</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> |
| <span class="n">valid_length</span><span class="o">=</span><span class="n">valid_length</span><span class="p">)</span> |
| <span class="n">reversed_r_outputs</span> <span class="o">=</span> <span class="n">_reverse_sequences</span><span class="p">(</span><span class="n">r_outputs</span><span class="p">,</span> <span class="n">length</span><span class="p">,</span> <span class="n">valid_length</span><span class="p">)</span> |
| |
| <span class="k">if</span> <span class="n">merge_outputs</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span> |
| <span class="n">merge_outputs</span> <span class="o">=</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">l_outputs</span><span class="p">,</span> <span class="n">tensor_types</span><span class="p">)</span> |
| <span class="n">l_outputs</span><span class="p">,</span> <span class="n">_</span><span class="p">,</span> <span class="n">_</span><span class="p">,</span> <span class="n">_</span> <span class="o">=</span> <span class="n">_format_sequence</span><span class="p">(</span><span class="kc">None</span><span class="p">,</span> <span class="n">l_outputs</span><span class="p">,</span> <span class="n">layout</span><span class="p">,</span> <span class="n">merge_outputs</span><span class="p">)</span> |
| <span class="n">reversed_r_outputs</span><span class="p">,</span> <span class="n">_</span><span class="p">,</span> <span class="n">_</span><span class="p">,</span> <span class="n">_</span> <span class="o">=</span> <span class="n">_format_sequence</span><span class="p">(</span><span class="kc">None</span><span class="p">,</span> <span class="n">reversed_r_outputs</span><span class="p">,</span> <span class="n">layout</span><span class="p">,</span> |
| <span class="n">merge_outputs</span><span class="p">)</span> |
| |
| <span class="k">if</span> <span class="n">merge_outputs</span><span class="p">:</span> |
| <span class="n">reversed_r_outputs</span> <span class="o">=</span> <span class="n">F</span><span class="o">.</span><span class="n">stack</span><span class="p">(</span><span class="o">*</span><span class="n">reversed_r_outputs</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="n">axis</span><span class="p">)</span> |
| <span class="n">outputs</span> <span class="o">=</span> <span class="n">F</span><span class="o">.</span><span class="n">concat</span><span class="p">(</span><span class="n">l_outputs</span><span class="p">,</span> <span class="n">reversed_r_outputs</span><span class="p">,</span> <span class="n">dim</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> |
| <span class="n">name</span><span class="o">=</span><span class="s1">'</span><span class="si">%s</span><span class="s1">out'</span><span class="o">%</span><span class="bp">self</span><span class="o">.</span><span class="n">_output_prefix</span><span class="p">)</span> |
| |
| <span class="k">else</span><span class="p">:</span> |
| <span class="n">outputs</span> <span class="o">=</span> <span class="p">[</span><span class="n">F</span><span class="o">.</span><span class="n">concat</span><span class="p">(</span><span class="n">l_o</span><span class="p">,</span> <span class="n">r_o</span><span class="p">,</span> <span class="n">dim</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">name</span><span class="o">=</span><span class="s1">'</span><span class="si">%s</span><span class="s1">t</span><span class="si">%d</span><span class="s1">'</span><span class="o">%</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_output_prefix</span><span class="p">,</span> <span class="n">i</span><span class="p">))</span> |
| <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="p">(</span><span class="n">l_o</span><span class="p">,</span> <span class="n">r_o</span><span class="p">)</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="nb">zip</span><span class="p">(</span><span class="n">l_outputs</span><span class="p">,</span> <span class="n">reversed_r_outputs</span><span class="p">))]</span> |
| <span class="k">if</span> <span class="n">valid_length</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span> |
| <span class="n">outputs</span> <span class="o">=</span> <span class="n">_mask_sequence_variable_length</span><span class="p">(</span><span class="n">F</span><span class="p">,</span> <span class="n">outputs</span><span class="p">,</span> <span class="n">length</span><span class="p">,</span> <span class="n">valid_length</span><span class="p">,</span> <span class="n">axis</span><span class="p">,</span> |
| <span class="n">merge_outputs</span><span class="p">)</span> |
| <span class="n">states</span> <span class="o">=</span> <span class="n">l_states</span> <span class="o">+</span> <span class="n">r_states</span> |
| <span class="k">return</span> <span class="n">outputs</span><span class="p">,</span> <span class="n">states</span></div></div> |
| </pre></div> |
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