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| <div class="section" id="gluon-contrib-api"> |
| <span id="gluon-contrib-api"></span><h1>Gluon Contrib API<a class="headerlink" href="#gluon-contrib-api" title="Permalink to this headline">¶</a></h1> |
| <div class="section" id="overview"> |
| <span id="overview"></span><h2>Overview<a class="headerlink" href="#overview" title="Permalink to this headline">¶</a></h2> |
| <p>This document lists the contrib APIs in Gluon:</p> |
| <table border="1" class="longtable docutils"> |
| <colgroup> |
| <col width="10%"/> |
| <col width="90%"/> |
| </colgroup> |
| <tbody valign="top"> |
| <tr class="row-odd"><td><a class="reference internal" href="#module-mxnet.gluon.contrib" title="mxnet.gluon.contrib"><code class="xref py py-obj docutils literal"><span class="pre">mxnet.gluon.contrib</span></code></a></td> |
| <td>Contrib neural network module.</td> |
| </tr> |
| </tbody> |
| </table> |
| <p>The <code class="docutils literal"><span class="pre">Gluon</span> <span class="pre">Contrib</span></code> API, defined in the <code class="docutils literal"><span class="pre">gluon.contrib</span></code> package, provides |
| many useful experimental APIs for new features. |
| This is a place for the community to try out the new features, |
| so that feature contributors can receive feedback.</p> |
| <div class="admonition warning"> |
| <p class="first admonition-title">Warning</p> |
| <p class="last">This package contains experimental APIs and may change in the near future.</p> |
| </div> |
| <p>In the rest of this document, we list routines provided by the <code class="docutils literal"><span class="pre">gluon.contrib</span></code> package.</p> |
| </div> |
| <div class="section" id="contrib"> |
| <span id="contrib"></span><h2>Contrib<a class="headerlink" href="#contrib" title="Permalink to this headline">¶</a></h2> |
| <div class="section" id="neural-network"> |
| <span id="neural-network"></span><h3>Neural network<a class="headerlink" href="#neural-network" title="Permalink to this headline">¶</a></h3> |
| <table border="1" class="longtable docutils"> |
| <colgroup> |
| <col width="10%"/> |
| <col width="90%"/> |
| </colgroup> |
| <tbody valign="top"> |
| <tr class="row-odd"><td><a class="reference internal" href="#mxnet.gluon.contrib.nn.Concurrent" title="mxnet.gluon.contrib.nn.Concurrent"><code class="xref py py-obj docutils literal"><span class="pre">Concurrent</span></code></a></td> |
| <td>Lays <a href="#id1"><span class="problematic" id="id2">`</span></a>Block`s concurrently.</td> |
| </tr> |
| <tr class="row-even"><td><a class="reference internal" href="#mxnet.gluon.contrib.nn.HybridConcurrent" title="mxnet.gluon.contrib.nn.HybridConcurrent"><code class="xref py py-obj docutils literal"><span class="pre">HybridConcurrent</span></code></a></td> |
| <td>Lays <a href="#id3"><span class="problematic" id="id4">`</span></a>HybridBlock`s concurrently.</td> |
| </tr> |
| <tr class="row-odd"><td><a class="reference internal" href="#mxnet.gluon.contrib.nn.Identity" title="mxnet.gluon.contrib.nn.Identity"><code class="xref py py-obj docutils literal"><span class="pre">Identity</span></code></a></td> |
| <td>Block that passes through the input directly.</td> |
| </tr> |
| <tr class="row-even"><td><a class="reference internal" href="#mxnet.gluon.contrib.nn.SparseEmbedding" title="mxnet.gluon.contrib.nn.SparseEmbedding"><code class="xref py py-obj docutils literal"><span class="pre">SparseEmbedding</span></code></a></td> |
| <td>Turns non-negative integers (indexes/tokens) into dense vectors of fixed size.</td> |
| </tr> |
| <tr class="row-odd"><td><a class="reference internal" href="#mxnet.gluon.contrib.nn.SyncBatchNorm" title="mxnet.gluon.contrib.nn.SyncBatchNorm"><code class="xref py py-obj docutils literal"><span class="pre">SyncBatchNorm</span></code></a></td> |
| <td>Cross-GPU Synchronized Batch normalization (SyncBN)</td> |
| </tr> |
| </tbody> |
| </table> |
| </div> |
| <div class="section" id="recurrent-neural-network"> |
| <span id="recurrent-neural-network"></span><h3>Recurrent neural network<a class="headerlink" href="#recurrent-neural-network" title="Permalink to this headline">¶</a></h3> |
| <table border="1" class="longtable docutils"> |
| <colgroup> |
| <col width="10%"/> |
| <col width="90%"/> |
| </colgroup> |
| <tbody valign="top"> |
| <tr class="row-odd"><td><a class="reference internal" href="#mxnet.gluon.contrib.rnn.VariationalDropoutCell" title="mxnet.gluon.contrib.rnn.VariationalDropoutCell"><code class="xref py py-obj docutils literal"><span class="pre">VariationalDropoutCell</span></code></a></td> |
| <td>Applies Variational Dropout on base cell.</td> |
| </tr> |
| <tr class="row-even"><td><a class="reference internal" href="#mxnet.gluon.contrib.rnn.Conv1DRNNCell" title="mxnet.gluon.contrib.rnn.Conv1DRNNCell"><code class="xref py py-obj docutils literal"><span class="pre">Conv1DRNNCell</span></code></a></td> |
| <td>1D Convolutional RNN cell.</td> |
| </tr> |
| <tr class="row-odd"><td><a class="reference internal" href="#mxnet.gluon.contrib.rnn.Conv2DRNNCell" title="mxnet.gluon.contrib.rnn.Conv2DRNNCell"><code class="xref py py-obj docutils literal"><span class="pre">Conv2DRNNCell</span></code></a></td> |
| <td>2D Convolutional RNN cell.</td> |
| </tr> |
| <tr class="row-even"><td><a class="reference internal" href="#mxnet.gluon.contrib.rnn.Conv3DRNNCell" title="mxnet.gluon.contrib.rnn.Conv3DRNNCell"><code class="xref py py-obj docutils literal"><span class="pre">Conv3DRNNCell</span></code></a></td> |
| <td>3D Convolutional RNN cells</td> |
| </tr> |
| <tr class="row-odd"><td><a class="reference internal" href="#mxnet.gluon.contrib.rnn.Conv1DLSTMCell" title="mxnet.gluon.contrib.rnn.Conv1DLSTMCell"><code class="xref py py-obj docutils literal"><span class="pre">Conv1DLSTMCell</span></code></a></td> |
| <td>1D Convolutional LSTM network cell.</td> |
| </tr> |
| <tr class="row-even"><td><a class="reference internal" href="#mxnet.gluon.contrib.rnn.Conv2DLSTMCell" title="mxnet.gluon.contrib.rnn.Conv2DLSTMCell"><code class="xref py py-obj docutils literal"><span class="pre">Conv2DLSTMCell</span></code></a></td> |
| <td>2D Convolutional LSTM network cell.</td> |
| </tr> |
| <tr class="row-odd"><td><a class="reference internal" href="#mxnet.gluon.contrib.rnn.Conv3DLSTMCell" title="mxnet.gluon.contrib.rnn.Conv3DLSTMCell"><code class="xref py py-obj docutils literal"><span class="pre">Conv3DLSTMCell</span></code></a></td> |
| <td>3D Convolutional LSTM network cell.</td> |
| </tr> |
| <tr class="row-even"><td><a class="reference internal" href="#mxnet.gluon.contrib.rnn.Conv1DGRUCell" title="mxnet.gluon.contrib.rnn.Conv1DGRUCell"><code class="xref py py-obj docutils literal"><span class="pre">Conv1DGRUCell</span></code></a></td> |
| <td>1D Convolutional Gated Rectified Unit (GRU) network cell.</td> |
| </tr> |
| <tr class="row-odd"><td><a class="reference internal" href="#mxnet.gluon.contrib.rnn.Conv2DGRUCell" title="mxnet.gluon.contrib.rnn.Conv2DGRUCell"><code class="xref py py-obj docutils literal"><span class="pre">Conv2DGRUCell</span></code></a></td> |
| <td>2D Convolutional Gated Rectified Unit (GRU) network cell.</td> |
| </tr> |
| <tr class="row-even"><td><a class="reference internal" href="#mxnet.gluon.contrib.rnn.Conv3DGRUCell" title="mxnet.gluon.contrib.rnn.Conv3DGRUCell"><code class="xref py py-obj docutils literal"><span class="pre">Conv3DGRUCell</span></code></a></td> |
| <td>3D Convolutional Gated Rectified Unit (GRU) network cell.</td> |
| </tr> |
| <tr class="row-odd"><td><a class="reference internal" href="#mxnet.gluon.contrib.rnn.LSTMPCell" title="mxnet.gluon.contrib.rnn.LSTMPCell"><code class="xref py py-obj docutils literal"><span class="pre">LSTMPCell</span></code></a></td> |
| <td>Long-Short Term Memory Projected (LSTMP) network cell.</td> |
| </tr> |
| </tbody> |
| </table> |
| </div> |
| <div class="section" id="data"> |
| <span id="data"></span><h3>Data<a class="headerlink" href="#data" title="Permalink to this headline">¶</a></h3> |
| <table border="1" class="longtable docutils"> |
| <colgroup> |
| <col width="10%"/> |
| <col width="90%"/> |
| </colgroup> |
| <tbody valign="top"> |
| <tr class="row-odd"><td><a class="reference internal" href="#mxnet.gluon.contrib.data.IntervalSampler" title="mxnet.gluon.contrib.data.IntervalSampler"><code class="xref py py-obj docutils literal"><span class="pre">IntervalSampler</span></code></a></td> |
| <td>Samples elements from [0, length) at fixed intervals.</td> |
| </tr> |
| </tbody> |
| </table> |
| <div class="section" id="text-dataset"> |
| <span id="text-dataset"></span><h4>Text dataset<a class="headerlink" href="#text-dataset" title="Permalink to this headline">¶</a></h4> |
| <table border="1" class="longtable docutils"> |
| <colgroup> |
| <col width="10%"/> |
| <col width="90%"/> |
| </colgroup> |
| <tbody valign="top"> |
| <tr class="row-odd"><td><a class="reference internal" href="#mxnet.gluon.contrib.data.text.WikiText2" title="mxnet.gluon.contrib.data.text.WikiText2"><code class="xref py py-obj docutils literal"><span class="pre">WikiText2</span></code></a></td> |
| <td>WikiText-2 word-level dataset for language modeling, from Salesforce research.</td> |
| </tr> |
| <tr class="row-even"><td><a class="reference internal" href="#mxnet.gluon.contrib.data.text.WikiText103" title="mxnet.gluon.contrib.data.text.WikiText103"><code class="xref py py-obj docutils literal"><span class="pre">WikiText103</span></code></a></td> |
| <td>WikiText-103 word-level dataset for language modeling, from Salesforce research.</td> |
| </tr> |
| </tbody> |
| </table> |
| </div> |
| </div> |
| </div> |
| <div class="section" id="api-reference"> |
| <span id="api-reference"></span><h2>API Reference<a class="headerlink" href="#api-reference" title="Permalink to this headline">¶</a></h2> |
| <script src="../../../_static/js/auto_module_index.js" type="text/javascript"></script><span class="target" id="module-mxnet.gluon.contrib"></span><p>Contrib neural network module.</p> |
| <span class="target" id="module-mxnet.gluon.contrib.nn"></span><p>Contrib recurrent neural network module.</p> |
| <dl class="class"> |
| <dt id="mxnet.gluon.contrib.nn.Concurrent"> |
| <em class="property">class </em><code class="descclassname">mxnet.gluon.contrib.nn.</code><code class="descname">Concurrent</code><span class="sig-paren">(</span><em>axis=-1</em>, <em>prefix=None</em>, <em>params=None</em><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/mxnet/gluon/contrib/nn/basic_layers.html#Concurrent"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.gluon.contrib.nn.Concurrent" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Lays <a href="#id5"><span class="problematic" id="id6">`</span></a>Block`s concurrently.</p> |
| <p>This block feeds its input to all children blocks, and |
| produce the output by concatenating all the children blocks’ outputs |
| on the specified axis.</p> |
| <p>Example:</p> |
| <div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">net</span> <span class="o">=</span> <span class="n">Concurrent</span><span class="p">()</span> |
| <span class="c1"># use net's name_scope to give children blocks appropriate names.</span> |
| <span class="k">with</span> <span class="n">net</span><span class="o">.</span><span class="n">name_scope</span><span class="p">():</span> |
| <span class="n">net</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">nn</span><span class="o">.</span><span class="n">Dense</span><span class="p">(</span><span class="mi">10</span><span class="p">,</span> <span class="n">activation</span><span class="o">=</span><span class="s1">'relu'</span><span class="p">))</span> |
| <span class="n">net</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">nn</span><span class="o">.</span><span class="n">Dense</span><span class="p">(</span><span class="mi">20</span><span class="p">))</span> |
| <span class="n">net</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">Identity</span><span class="p">())</span> |
| </pre></div> |
| </div> |
| <table class="docutils field-list" frame="void" rules="none"> |
| <col class="field-name"/> |
| <col class="field-body"/> |
| <tbody valign="top"> |
| <tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><strong>axis</strong> (<em>int</em><em>, </em><em>default -1</em>) – The axis on which to concatenate the outputs.</td> |
| </tr> |
| </tbody> |
| </table> |
| </dd></dl> |
| <dl class="class"> |
| <dt id="mxnet.gluon.contrib.nn.HybridConcurrent"> |
| <em class="property">class </em><code class="descclassname">mxnet.gluon.contrib.nn.</code><code class="descname">HybridConcurrent</code><span class="sig-paren">(</span><em>axis=-1</em>, <em>prefix=None</em>, <em>params=None</em><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/mxnet/gluon/contrib/nn/basic_layers.html#HybridConcurrent"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.gluon.contrib.nn.HybridConcurrent" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Lays <a href="#id7"><span class="problematic" id="id8">`</span></a>HybridBlock`s concurrently.</p> |
| <p>This block feeds its input to all children blocks, and |
| produce the output by concatenating all the children blocks’ outputs |
| on the specified axis.</p> |
| <p>Example:</p> |
| <div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">net</span> <span class="o">=</span> <span class="n">HybridConcurrent</span><span class="p">()</span> |
| <span class="c1"># use net's name_scope to give children blocks appropriate names.</span> |
| <span class="k">with</span> <span class="n">net</span><span class="o">.</span><span class="n">name_scope</span><span class="p">():</span> |
| <span class="n">net</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">nn</span><span class="o">.</span><span class="n">Dense</span><span class="p">(</span><span class="mi">10</span><span class="p">,</span> <span class="n">activation</span><span class="o">=</span><span class="s1">'relu'</span><span class="p">))</span> |
| <span class="n">net</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">nn</span><span class="o">.</span><span class="n">Dense</span><span class="p">(</span><span class="mi">20</span><span class="p">))</span> |
| <span class="n">net</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">Identity</span><span class="p">())</span> |
| </pre></div> |
| </div> |
| <table class="docutils field-list" frame="void" rules="none"> |
| <col class="field-name"/> |
| <col class="field-body"/> |
| <tbody valign="top"> |
| <tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><strong>axis</strong> (<em>int</em><em>, </em><em>default -1</em>) – The axis on which to concatenate the outputs.</td> |
| </tr> |
| </tbody> |
| </table> |
| </dd></dl> |
| <dl class="class"> |
| <dt id="mxnet.gluon.contrib.nn.Identity"> |
| <em class="property">class </em><code class="descclassname">mxnet.gluon.contrib.nn.</code><code class="descname">Identity</code><span class="sig-paren">(</span><em>prefix=None</em>, <em>params=None</em><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/mxnet/gluon/contrib/nn/basic_layers.html#Identity"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.gluon.contrib.nn.Identity" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Block that passes through the input directly.</p> |
| <p>This block can be used in conjunction with HybridConcurrent |
| block for residual connection.</p> |
| <p>Example:</p> |
| <div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">net</span> <span class="o">=</span> <span class="n">HybridConcurrent</span><span class="p">()</span> |
| <span class="c1"># use net's name_scope to give child Blocks appropriate names.</span> |
| <span class="k">with</span> <span class="n">net</span><span class="o">.</span><span class="n">name_scope</span><span class="p">():</span> |
| <span class="n">net</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">nn</span><span class="o">.</span><span class="n">Dense</span><span class="p">(</span><span class="mi">10</span><span class="p">,</span> <span class="n">activation</span><span class="o">=</span><span class="s1">'relu'</span><span class="p">))</span> |
| <span class="n">net</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">nn</span><span class="o">.</span><span class="n">Dense</span><span class="p">(</span><span class="mi">20</span><span class="p">))</span> |
| <span class="n">net</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">Identity</span><span class="p">())</span> |
| </pre></div> |
| </div> |
| </dd></dl> |
| <dl class="class"> |
| <dt id="mxnet.gluon.contrib.nn.SparseEmbedding"> |
| <em class="property">class </em><code class="descclassname">mxnet.gluon.contrib.nn.</code><code class="descname">SparseEmbedding</code><span class="sig-paren">(</span><em>input_dim</em>, <em>output_dim</em>, <em>dtype='float32'</em>, <em>weight_initializer=None</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/mxnet/gluon/contrib/nn/basic_layers.html#SparseEmbedding"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.gluon.contrib.nn.SparseEmbedding" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Turns non-negative integers (indexes/tokens) into dense vectors |
| of fixed size. eg. [4, 20] -> [[0.25, 0.1], [0.6, -0.2]]</p> |
| <p>This SparseBlock is designed for distributed training with extremely large |
| input dimension. Both weight and gradient w.r.t. weight are <cite>RowSparseNDArray</cite>.</p> |
| <p>Note: if <cite>sparse_grad</cite> is set to True, the gradient w.r.t weight will be |
| sparse. Only a subset of optimizers support sparse gradients, including SGD, AdaGrad |
| and Adam. By default lazy updates is turned on, which may perform differently |
| from standard updates. For more details, please check the Optimization API at: |
| <a class="reference external" href="/api/python/optimization/optimization.html">/api/python/optimization/optimization.html</a></p> |
| <table class="docutils field-list" frame="void" rules="none"> |
| <col class="field-name"/> |
| <col class="field-body"/> |
| <tbody valign="top"> |
| <tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple"> |
| <li><strong>input_dim</strong> (<em>int</em>) – Size of the vocabulary, i.e. maximum integer index + 1.</li> |
| <li><strong>output_dim</strong> (<em>int</em>) – Dimension of the dense embedding.</li> |
| <li><strong>dtype</strong> (<em>str</em><em> or </em><em>np.dtype</em><em>, </em><em>default 'float32'</em>) – Data type of output embeddings.</li> |
| <li><strong>weight_initializer</strong> (<a class="reference internal" href="../optimization/optimization.html#mxnet.initializer.Initializer" title="mxnet.initializer.Initializer"><em>Initializer</em></a>) – Initializer for the <cite>embeddings</cite> matrix.</li> |
| <li><strong>Inputs</strong> – <ul> |
| <li><strong>data</strong>: (N-1)-D tensor with shape: <cite>(x1, x2, ..., xN-1)</cite>.</li> |
| </ul> |
| </li> |
| <li><strong>Output</strong> – <ul> |
| <li><strong>out</strong>: N-D tensor with shape: <cite>(x1, x2, ..., xN-1, output_dim)</cite>.</li> |
| </ul> |
| </li> |
| </ul> |
| </td> |
| </tr> |
| </tbody> |
| </table> |
| </dd></dl> |
| <dl class="class"> |
| <dt id="mxnet.gluon.contrib.nn.SyncBatchNorm"> |
| <em class="property">class </em><code class="descclassname">mxnet.gluon.contrib.nn.</code><code class="descname">SyncBatchNorm</code><span class="sig-paren">(</span><em>in_channels=0</em>, <em>num_devices=None</em>, <em>momentum=0.9</em>, <em>epsilon=1e-05</em>, <em>center=True</em>, <em>scale=True</em>, <em>use_global_stats=False</em>, <em>beta_initializer='zeros'</em>, <em>gamma_initializer='ones'</em>, <em>running_mean_initializer='zeros'</em>, <em>running_variance_initializer='ones'</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/mxnet/gluon/contrib/nn/basic_layers.html#SyncBatchNorm"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.gluon.contrib.nn.SyncBatchNorm" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Cross-GPU Synchronized Batch normalization (SyncBN)</p> |
| <p>Standard BN <a class="footnote-reference" href="#id11" id="id9">[1]</a> implementation only normalize the data within each device. |
| SyncBN normalizes the input within the whole mini-batch. |
| We follow the sync-onece implmentation described in the paper <a class="footnote-reference" href="#id12" id="id10">[2]</a>.</p> |
| <table class="docutils field-list" frame="void" rules="none"> |
| <col class="field-name"/> |
| <col class="field-body"/> |
| <tbody valign="top"> |
| <tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple"> |
| <li><strong>in_channels</strong> (<em>int</em><em>, </em><em>default 0</em>) – Number of channels (feature maps) in input data. If not specified, |
| initialization will be deferred to the first time <cite>forward</cite> is called |
| and <cite>in_channels</cite> will be inferred from the shape of input data.</li> |
| <li><strong>num_devices</strong> (<em>int</em><em>, </em><em>default number of visible GPUs</em>) – </li> |
| <li><strong>momentum</strong> (<em>float</em><em>, </em><em>default 0.9</em>) – Momentum for the moving average.</li> |
| <li><strong>epsilon</strong> (<em>float</em><em>, </em><em>default 1e-5</em>) – Small float added to variance to avoid dividing by zero.</li> |
| <li><strong>center</strong> (<em>bool</em><em>, </em><em>default True</em>) – If True, add offset of <cite>beta</cite> to normalized tensor. |
| If False, <cite>beta</cite> is ignored.</li> |
| <li><strong>scale</strong> (<em>bool</em><em>, </em><em>default True</em>) – If True, multiply by <cite>gamma</cite>. If False, <cite>gamma</cite> is not used. |
| When the next layer is linear (also e.g. <cite>nn.relu</cite>), |
| this can be disabled since the scaling |
| will be done by the next layer.</li> |
| <li><strong>use_global_stats</strong> (<em>bool</em><em>, </em><em>default False</em>) – If True, use global moving statistics instead of local batch-norm. This will force |
| change batch-norm into a scale shift operator. |
| If False, use local batch-norm.</li> |
| <li><strong>beta_initializer</strong> (str or <cite>Initializer</cite>, default ‘zeros’) – Initializer for the beta weight.</li> |
| <li><strong>gamma_initializer</strong> (str or <cite>Initializer</cite>, default ‘ones’) – Initializer for the gamma weight.</li> |
| <li><strong>moving_mean_initializer</strong> (str or <cite>Initializer</cite>, default ‘zeros’) – Initializer for the moving mean.</li> |
| <li><strong>moving_variance_initializer</strong> (str or <cite>Initializer</cite>, default ‘ones’) – Initializer for the moving variance.</li> |
| </ul> |
| </td> |
| </tr> |
| </tbody> |
| </table> |
| <dl class="docutils"> |
| <dt>Inputs:</dt> |
| <dd><ul class="first last simple"> |
| <li><strong>data</strong>: input tensor with arbitrary shape.</li> |
| </ul> |
| </dd> |
| <dt>Outputs:</dt> |
| <dd><ul class="first last simple"> |
| <li><strong>out</strong>: output tensor with the same shape as <cite>data</cite>.</li> |
| </ul> |
| </dd> |
| <dt>Reference:</dt> |
| <dd><table class="first docutils footnote" frame="void" id="id11" rules="none"> |
| <colgroup><col class="label"/><col/></colgroup> |
| <tbody valign="top"> |
| <tr><td class="label"><a class="fn-backref" href="#id9">[1]</a></td><td>Ioffe, Sergey, and Christian Szegedy. “Batch normalization: Accelerating deep network training by reducing internal covariate shift.” <em>ICML 2015</em></td></tr> |
| </tbody> |
| </table> |
| <table class="last docutils footnote" frame="void" id="id12" rules="none"> |
| <colgroup><col class="label"/><col/></colgroup> |
| <tbody valign="top"> |
| <tr><td class="label"><a class="fn-backref" href="#id10">[2]</a></td><td>Hang Zhang, Kristin Dana, Jianping Shi, Zhongyue Zhang, Xiaogang Wang, Ambrish Tyagi, and Amit Agrawal. “Context Encoding for Semantic Segmentation.” <em>CVPR 2018</em></td></tr> |
| </tbody> |
| </table> |
| </dd> |
| </dl> |
| </dd></dl> |
| <span class="target" id="module-mxnet.gluon.contrib.rnn"></span><p>Contrib recurrent neural network module.</p> |
| <dl class="class"> |
| <dt id="mxnet.gluon.contrib.rnn.Conv1DRNNCell"> |
| <em class="property">class </em><code class="descclassname">mxnet.gluon.contrib.rnn.</code><code class="descname">Conv1DRNNCell</code><span class="sig-paren">(</span><em>input_shape</em>, <em>hidden_channels</em>, <em>i2h_kernel</em>, <em>h2h_kernel</em>, <em>i2h_pad=(0</em>, <em>)</em>, <em>i2h_dilate=(1</em>, <em>)</em>, <em>h2h_dilate=(1</em>, <em>)</em>, <em>i2h_weight_initializer=None</em>, <em>h2h_weight_initializer=None</em>, <em>i2h_bias_initializer='zeros'</em>, <em>h2h_bias_initializer='zeros'</em>, <em>conv_layout='NCW'</em>, <em>activation='tanh'</em>, <em>prefix=None</em>, <em>params=None</em><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/mxnet/gluon/contrib/rnn/conv_rnn_cell.html#Conv1DRNNCell"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.gluon.contrib.rnn.Conv1DRNNCell" title="Permalink to this definition">¶</a></dt> |
| <dd><p>1D Convolutional RNN cell.</p> |
| <div class="math"> |
| \[h_t = tanh(W_i \ast x_t + R_i \ast h_{t-1} + b_i)\]</div> |
| <table class="docutils field-list" frame="void" rules="none"> |
| <col class="field-name"/> |
| <col class="field-body"/> |
| <tbody valign="top"> |
| <tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple"> |
| <li><strong>input_shape</strong> (<em>tuple of int</em>) – Input tensor shape at each time step for each sample, excluding dimension of the batch size |
| and sequence length. Must be consistent with <cite>conv_layout</cite>. |
| For example, for layout ‘NCW’ the shape should be (C, W).</li> |
| <li><strong>hidden_channels</strong> (<em>int</em>) – Number of output channels.</li> |
| <li><strong>i2h_kernel</strong> (<em>int</em><em> or </em><em>tuple of int</em>) – Input convolution kernel sizes.</li> |
| <li><strong>h2h_kernel</strong> (<em>int</em><em> or </em><em>tuple of int</em>) – Recurrent convolution kernel sizes. Only odd-numbered sizes are supported.</li> |
| <li><strong>i2h_pad</strong> (<em>int</em><em> or </em><em>tuple of int</em><em>, </em><em>default</em><em> (</em><em>0</em><em>,</em><em>)</em>) – Pad for input convolution.</li> |
| <li><strong>i2h_dilate</strong> (<em>int</em><em> or </em><em>tuple of int</em><em>, </em><em>default</em><em> (</em><em>1</em><em>,</em><em>)</em>) – Input convolution dilate.</li> |
| <li><strong>h2h_dilate</strong> (<em>int</em><em> or </em><em>tuple of int</em><em>, </em><em>default</em><em> (</em><em>1</em><em>,</em><em>)</em>) – Recurrent convolution dilate.</li> |
| <li><strong>i2h_weight_initializer</strong> (<em>str</em><em> or </em><a class="reference internal" href="../optimization/optimization.html#mxnet.initializer.Initializer" title="mxnet.initializer.Initializer"><em>Initializer</em></a>) – Initializer for the input weights matrix, used for the input convolutions.</li> |
| <li><strong>h2h_weight_initializer</strong> (<em>str</em><em> or </em><a class="reference internal" href="../optimization/optimization.html#mxnet.initializer.Initializer" title="mxnet.initializer.Initializer"><em>Initializer</em></a>) – Initializer for the recurrent weights matrix, used for the input convolutions.</li> |
| <li><strong>i2h_bias_initializer</strong> (<em>str</em><em> or </em><a class="reference internal" href="../optimization/optimization.html#mxnet.initializer.Initializer" title="mxnet.initializer.Initializer"><em>Initializer</em></a><em>, </em><em>default zeros</em>) – Initializer for the input convolution bias vectors.</li> |
| <li><strong>h2h_bias_initializer</strong> (<em>str</em><em> or </em><a class="reference internal" href="../optimization/optimization.html#mxnet.initializer.Initializer" title="mxnet.initializer.Initializer"><em>Initializer</em></a><em>, </em><em>default zeros</em>) – Initializer for the recurrent convolution bias vectors.</li> |
| <li><strong>conv_layout</strong> (<em>str</em><em>, </em><em>default 'NCW'</em>) – Layout for all convolution inputs, outputs and weights. Options are ‘NCW’ and ‘NWC’.</li> |
| <li><strong>activation</strong> (<em>str</em><em> or </em><a class="reference internal" href="gluon.html#mxnet.gluon.Block" title="mxnet.gluon.Block"><em>Block</em></a><em>, </em><em>default 'tanh'</em>) – Type of activation function. |
| If argument type is string, it’s equivalent to nn.Activation(act_type=str). See |
| <a class="reference internal" href="../ndarray/ndarray.html#mxnet.ndarray.Activation" title="mxnet.ndarray.Activation"><code class="xref py py-func docutils literal"><span class="pre">Activation()</span></code></a> for available choices. |
| Alternatively, other activation blocks such as nn.LeakyReLU can be used.</li> |
| <li><strong>prefix</strong> (str, default ‘<a href="#id15"><span class="problematic" id="id16">conv_rnn_</span></a>‘) – Prefix for name of layers (and name of weight if params is None).</li> |
| <li><strong>params</strong> (<a class="reference internal" href="../symbol/rnn.html#mxnet.rnn.RNNParams" title="mxnet.rnn.RNNParams"><em>RNNParams</em></a><em>, </em><em>default None</em>) – Container for weight sharing between cells. Created if None.</li> |
| </ul> |
| </td> |
| </tr> |
| </tbody> |
| </table> |
| </dd></dl> |
| <dl class="class"> |
| <dt id="mxnet.gluon.contrib.rnn.Conv2DRNNCell"> |
| <em class="property">class </em><code class="descclassname">mxnet.gluon.contrib.rnn.</code><code class="descname">Conv2DRNNCell</code><span class="sig-paren">(</span><em>input_shape</em>, <em>hidden_channels</em>, <em>i2h_kernel</em>, <em>h2h_kernel</em>, <em>i2h_pad=(0</em>, <em>0)</em>, <em>i2h_dilate=(1</em>, <em>1)</em>, <em>h2h_dilate=(1</em>, <em>1)</em>, <em>i2h_weight_initializer=None</em>, <em>h2h_weight_initializer=None</em>, <em>i2h_bias_initializer='zeros'</em>, <em>h2h_bias_initializer='zeros'</em>, <em>conv_layout='NCHW'</em>, <em>activation='tanh'</em>, <em>prefix=None</em>, <em>params=None</em><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/mxnet/gluon/contrib/rnn/conv_rnn_cell.html#Conv2DRNNCell"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.gluon.contrib.rnn.Conv2DRNNCell" title="Permalink to this definition">¶</a></dt> |
| <dd><p>2D Convolutional RNN cell.</p> |
| <div class="math"> |
| \[h_t = tanh(W_i \ast x_t + R_i \ast h_{t-1} + b_i)\]</div> |
| <table class="docutils field-list" frame="void" rules="none"> |
| <col class="field-name"/> |
| <col class="field-body"/> |
| <tbody valign="top"> |
| <tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple"> |
| <li><strong>input_shape</strong> (<em>tuple of int</em>) – Input tensor shape at each time step for each sample, excluding dimension of the batch size |
| and sequence length. Must be consistent with <cite>conv_layout</cite>. |
| For example, for layout ‘NCHW’ the shape should be (C, H, W).</li> |
| <li><strong>hidden_channels</strong> (<em>int</em>) – Number of output channels.</li> |
| <li><strong>i2h_kernel</strong> (<em>int</em><em> or </em><em>tuple of int</em>) – Input convolution kernel sizes.</li> |
| <li><strong>h2h_kernel</strong> (<em>int</em><em> or </em><em>tuple of int</em>) – Recurrent convolution kernel sizes. Only odd-numbered sizes are supported.</li> |
| <li><strong>i2h_pad</strong> (<em>int</em><em> or </em><em>tuple of int</em><em>, </em><em>default</em><em> (</em><em>0</em><em>, </em><em>0</em><em>)</em>) – Pad for input convolution.</li> |
| <li><strong>i2h_dilate</strong> (<em>int</em><em> or </em><em>tuple of int</em><em>, </em><em>default</em><em> (</em><em>1</em><em>, </em><em>1</em><em>)</em>) – Input convolution dilate.</li> |
| <li><strong>h2h_dilate</strong> (<em>int</em><em> or </em><em>tuple of int</em><em>, </em><em>default</em><em> (</em><em>1</em><em>, </em><em>1</em><em>)</em>) – Recurrent convolution dilate.</li> |
| <li><strong>i2h_weight_initializer</strong> (<em>str</em><em> or </em><a class="reference internal" href="../optimization/optimization.html#mxnet.initializer.Initializer" title="mxnet.initializer.Initializer"><em>Initializer</em></a>) – Initializer for the input weights matrix, used for the input convolutions.</li> |
| <li><strong>h2h_weight_initializer</strong> (<em>str</em><em> or </em><a class="reference internal" href="../optimization/optimization.html#mxnet.initializer.Initializer" title="mxnet.initializer.Initializer"><em>Initializer</em></a>) – Initializer for the recurrent weights matrix, used for the input convolutions.</li> |
| <li><strong>i2h_bias_initializer</strong> (<em>str</em><em> or </em><a class="reference internal" href="../optimization/optimization.html#mxnet.initializer.Initializer" title="mxnet.initializer.Initializer"><em>Initializer</em></a><em>, </em><em>default zeros</em>) – Initializer for the input convolution bias vectors.</li> |
| <li><strong>h2h_bias_initializer</strong> (<em>str</em><em> or </em><a class="reference internal" href="../optimization/optimization.html#mxnet.initializer.Initializer" title="mxnet.initializer.Initializer"><em>Initializer</em></a><em>, </em><em>default zeros</em>) – Initializer for the recurrent convolution bias vectors.</li> |
| <li><strong>conv_layout</strong> (<em>str</em><em>, </em><em>default 'NCHW'</em>) – Layout for all convolution inputs, outputs and weights. Options are ‘NCHW’ and ‘NHWC’.</li> |
| <li><strong>activation</strong> (<em>str</em><em> or </em><a class="reference internal" href="gluon.html#mxnet.gluon.Block" title="mxnet.gluon.Block"><em>Block</em></a><em>, </em><em>default 'tanh'</em>) – Type of activation function. |
| If argument type is string, it’s equivalent to nn.Activation(act_type=str). See |
| <a class="reference internal" href="../ndarray/ndarray.html#mxnet.ndarray.Activation" title="mxnet.ndarray.Activation"><code class="xref py py-func docutils literal"><span class="pre">Activation()</span></code></a> for available choices. |
| Alternatively, other activation blocks such as nn.LeakyReLU can be used.</li> |
| <li><strong>prefix</strong> (str, default ‘<a href="#id17"><span class="problematic" id="id18">conv_rnn_</span></a>‘) – Prefix for name of layers (and name of weight if params is None).</li> |
| <li><strong>params</strong> (<a class="reference internal" href="../symbol/rnn.html#mxnet.rnn.RNNParams" title="mxnet.rnn.RNNParams"><em>RNNParams</em></a><em>, </em><em>default None</em>) – Container for weight sharing between cells. Created if None.</li> |
| </ul> |
| </td> |
| </tr> |
| </tbody> |
| </table> |
| </dd></dl> |
| <dl class="class"> |
| <dt id="mxnet.gluon.contrib.rnn.Conv3DRNNCell"> |
| <em class="property">class </em><code class="descclassname">mxnet.gluon.contrib.rnn.</code><code class="descname">Conv3DRNNCell</code><span class="sig-paren">(</span><em>input_shape</em>, <em>hidden_channels</em>, <em>i2h_kernel</em>, <em>h2h_kernel</em>, <em>i2h_pad=(0</em>, <em>0</em>, <em>0)</em>, <em>i2h_dilate=(1</em>, <em>1</em>, <em>1)</em>, <em>h2h_dilate=(1</em>, <em>1</em>, <em>1)</em>, <em>i2h_weight_initializer=None</em>, <em>h2h_weight_initializer=None</em>, <em>i2h_bias_initializer='zeros'</em>, <em>h2h_bias_initializer='zeros'</em>, <em>conv_layout='NCDHW'</em>, <em>activation='tanh'</em>, <em>prefix=None</em>, <em>params=None</em><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/mxnet/gluon/contrib/rnn/conv_rnn_cell.html#Conv3DRNNCell"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.gluon.contrib.rnn.Conv3DRNNCell" title="Permalink to this definition">¶</a></dt> |
| <dd><p>3D Convolutional RNN cells</p> |
| <div class="math"> |
| \[h_t = tanh(W_i \ast x_t + R_i \ast h_{t-1} + b_i)\]</div> |
| <table class="docutils field-list" frame="void" rules="none"> |
| <col class="field-name"/> |
| <col class="field-body"/> |
| <tbody valign="top"> |
| <tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple"> |
| <li><strong>input_shape</strong> (<em>tuple of int</em>) – Input tensor shape at each time step for each sample, excluding dimension of the batch size |
| and sequence length. Must be consistent with <cite>conv_layout</cite>. |
| For example, for layout ‘NCDHW’ the shape should be (C, D, H, W).</li> |
| <li><strong>hidden_channels</strong> (<em>int</em>) – Number of output channels.</li> |
| <li><strong>i2h_kernel</strong> (<em>int</em><em> or </em><em>tuple of int</em>) – Input convolution kernel sizes.</li> |
| <li><strong>h2h_kernel</strong> (<em>int</em><em> or </em><em>tuple of int</em>) – Recurrent convolution kernel sizes. Only odd-numbered sizes are supported.</li> |
| <li><strong>i2h_pad</strong> (<em>int</em><em> or </em><em>tuple of int</em><em>, </em><em>default</em><em> (</em><em>0</em><em>, </em><em>0</em><em>, </em><em>0</em><em>)</em>) – Pad for input convolution.</li> |
| <li><strong>i2h_dilate</strong> (<em>int</em><em> or </em><em>tuple of int</em><em>, </em><em>default</em><em> (</em><em>1</em><em>, </em><em>1</em><em>, </em><em>1</em><em>)</em>) – Input convolution dilate.</li> |
| <li><strong>h2h_dilate</strong> (<em>int</em><em> or </em><em>tuple of int</em><em>, </em><em>default</em><em> (</em><em>1</em><em>, </em><em>1</em><em>, </em><em>1</em><em>)</em>) – Recurrent convolution dilate.</li> |
| <li><strong>i2h_weight_initializer</strong> (<em>str</em><em> or </em><a class="reference internal" href="../optimization/optimization.html#mxnet.initializer.Initializer" title="mxnet.initializer.Initializer"><em>Initializer</em></a>) – Initializer for the input weights matrix, used for the input convolutions.</li> |
| <li><strong>h2h_weight_initializer</strong> (<em>str</em><em> or </em><a class="reference internal" href="../optimization/optimization.html#mxnet.initializer.Initializer" title="mxnet.initializer.Initializer"><em>Initializer</em></a>) – Initializer for the recurrent weights matrix, used for the input convolutions.</li> |
| <li><strong>i2h_bias_initializer</strong> (<em>str</em><em> or </em><a class="reference internal" href="../optimization/optimization.html#mxnet.initializer.Initializer" title="mxnet.initializer.Initializer"><em>Initializer</em></a><em>, </em><em>default zeros</em>) – Initializer for the input convolution bias vectors.</li> |
| <li><strong>h2h_bias_initializer</strong> (<em>str</em><em> or </em><a class="reference internal" href="../optimization/optimization.html#mxnet.initializer.Initializer" title="mxnet.initializer.Initializer"><em>Initializer</em></a><em>, </em><em>default zeros</em>) – Initializer for the recurrent convolution bias vectors.</li> |
| <li><strong>conv_layout</strong> (<em>str</em><em>, </em><em>default 'NCDHW'</em>) – Layout for all convolution inputs, outputs and weights. Options are ‘NCDHW’ and ‘NDHWC’.</li> |
| <li><strong>activation</strong> (<em>str</em><em> or </em><a class="reference internal" href="gluon.html#mxnet.gluon.Block" title="mxnet.gluon.Block"><em>Block</em></a><em>, </em><em>default 'tanh'</em>) – Type of activation function. |
| If argument type is string, it’s equivalent to nn.Activation(act_type=str). See |
| <a class="reference internal" href="../ndarray/ndarray.html#mxnet.ndarray.Activation" title="mxnet.ndarray.Activation"><code class="xref py py-func docutils literal"><span class="pre">Activation()</span></code></a> for available choices. |
| Alternatively, other activation blocks such as nn.LeakyReLU can be used.</li> |
| <li><strong>prefix</strong> (str, default ‘<a href="#id19"><span class="problematic" id="id20">conv_rnn_</span></a>‘) – Prefix for name of layers (and name of weight if params is None).</li> |
| <li><strong>params</strong> (<a class="reference internal" href="../symbol/rnn.html#mxnet.rnn.RNNParams" title="mxnet.rnn.RNNParams"><em>RNNParams</em></a><em>, </em><em>default None</em>) – Container for weight sharing between cells. Created if None.</li> |
| </ul> |
| </td> |
| </tr> |
| </tbody> |
| </table> |
| </dd></dl> |
| <dl class="class"> |
| <dt id="mxnet.gluon.contrib.rnn.Conv1DLSTMCell"> |
| <em class="property">class </em><code class="descclassname">mxnet.gluon.contrib.rnn.</code><code class="descname">Conv1DLSTMCell</code><span class="sig-paren">(</span><em>input_shape</em>, <em>hidden_channels</em>, <em>i2h_kernel</em>, <em>h2h_kernel</em>, <em>i2h_pad=(0</em>, <em>)</em>, <em>i2h_dilate=(1</em>, <em>)</em>, <em>h2h_dilate=(1</em>, <em>)</em>, <em>i2h_weight_initializer=None</em>, <em>h2h_weight_initializer=None</em>, <em>i2h_bias_initializer='zeros'</em>, <em>h2h_bias_initializer='zeros'</em>, <em>conv_layout='NCW'</em>, <em>activation='tanh'</em>, <em>prefix=None</em>, <em>params=None</em><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/mxnet/gluon/contrib/rnn/conv_rnn_cell.html#Conv1DLSTMCell"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.gluon.contrib.rnn.Conv1DLSTMCell" title="Permalink to this definition">¶</a></dt> |
| <dd><p>1D Convolutional LSTM network cell.</p> |
| <p><a class="reference external" href="https://arxiv.org/abs/1506.04214">“Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting”</a> paper. Xingjian et al. NIPS2015</p> |
| <div class="math"> |
| \[\begin{split}\begin{array}{ll} |
| i_t = \sigma(W_i \ast x_t + R_i \ast h_{t-1} + b_i) \\ |
| f_t = \sigma(W_f \ast x_t + R_f \ast h_{t-1} + b_f) \\ |
| o_t = \sigma(W_o \ast x_t + R_o \ast h_{t-1} + b_o) \\ |
| c^\prime_t = tanh(W_c \ast x_t + R_c \ast h_{t-1} + b_c) \\ |
| c_t = f_t \circ c_{t-1} + i_t \circ c^\prime_t \\ |
| h_t = o_t \circ tanh(c_t) \\ |
| \end{array}\end{split}\]</div> |
| <table class="docutils field-list" frame="void" rules="none"> |
| <col class="field-name"/> |
| <col class="field-body"/> |
| <tbody valign="top"> |
| <tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple"> |
| <li><strong>input_shape</strong> (<em>tuple of int</em>) – Input tensor shape at each time step for each sample, excluding dimension of the batch size |
| and sequence length. Must be consistent with <cite>conv_layout</cite>. |
| For example, for layout ‘NCW’ the shape should be (C, W).</li> |
| <li><strong>hidden_channels</strong> (<em>int</em>) – Number of output channels.</li> |
| <li><strong>i2h_kernel</strong> (<em>int</em><em> or </em><em>tuple of int</em>) – Input convolution kernel sizes.</li> |
| <li><strong>h2h_kernel</strong> (<em>int</em><em> or </em><em>tuple of int</em>) – Recurrent convolution kernel sizes. Only odd-numbered sizes are supported.</li> |
| <li><strong>i2h_pad</strong> (<em>int</em><em> or </em><em>tuple of int</em><em>, </em><em>default</em><em> (</em><em>0</em><em>,</em><em>)</em>) – Pad for input convolution.</li> |
| <li><strong>i2h_dilate</strong> (<em>int</em><em> or </em><em>tuple of int</em><em>, </em><em>default</em><em> (</em><em>1</em><em>,</em><em>)</em>) – Input convolution dilate.</li> |
| <li><strong>h2h_dilate</strong> (<em>int</em><em> or </em><em>tuple of int</em><em>, </em><em>default</em><em> (</em><em>1</em><em>,</em><em>)</em>) – Recurrent convolution dilate.</li> |
| <li><strong>i2h_weight_initializer</strong> (<em>str</em><em> or </em><a class="reference internal" href="../optimization/optimization.html#mxnet.initializer.Initializer" title="mxnet.initializer.Initializer"><em>Initializer</em></a>) – Initializer for the input weights matrix, used for the input convolutions.</li> |
| <li><strong>h2h_weight_initializer</strong> (<em>str</em><em> or </em><a class="reference internal" href="../optimization/optimization.html#mxnet.initializer.Initializer" title="mxnet.initializer.Initializer"><em>Initializer</em></a>) – Initializer for the recurrent weights matrix, used for the input convolutions.</li> |
| <li><strong>i2h_bias_initializer</strong> (<em>str</em><em> or </em><a class="reference internal" href="../optimization/optimization.html#mxnet.initializer.Initializer" title="mxnet.initializer.Initializer"><em>Initializer</em></a><em>, </em><em>default zeros</em>) – Initializer for the input convolution bias vectors.</li> |
| <li><strong>h2h_bias_initializer</strong> (<em>str</em><em> or </em><a class="reference internal" href="../optimization/optimization.html#mxnet.initializer.Initializer" title="mxnet.initializer.Initializer"><em>Initializer</em></a><em>, </em><em>default zeros</em>) – Initializer for the recurrent convolution bias vectors.</li> |
| <li><strong>conv_layout</strong> (<em>str</em><em>, </em><em>default 'NCW'</em>) – Layout for all convolution inputs, outputs and weights. Options are ‘NCW’ and ‘NWC’.</li> |
| <li><strong>activation</strong> (<em>str</em><em> or </em><a class="reference internal" href="gluon.html#mxnet.gluon.Block" title="mxnet.gluon.Block"><em>Block</em></a><em>, </em><em>default 'tanh'</em>) – Type of activation function used in c^prime_t. |
| If argument type is string, it’s equivalent to nn.Activation(act_type=str). See |
| <a class="reference internal" href="../ndarray/ndarray.html#mxnet.ndarray.Activation" title="mxnet.ndarray.Activation"><code class="xref py py-func docutils literal"><span class="pre">Activation()</span></code></a> for available choices. |
| Alternatively, other activation blocks such as nn.LeakyReLU can be used.</li> |
| <li><strong>prefix</strong> (str, default ‘<a href="#id21"><span class="problematic" id="id22">conv_lstm_</span></a>‘) – Prefix for name of layers (and name of weight if params is None).</li> |
| <li><strong>params</strong> (<a class="reference internal" href="../symbol/rnn.html#mxnet.rnn.RNNParams" title="mxnet.rnn.RNNParams"><em>RNNParams</em></a><em>, </em><em>default None</em>) – Container for weight sharing between cells. Created if None.</li> |
| </ul> |
| </td> |
| </tr> |
| </tbody> |
| </table> |
| </dd></dl> |
| <dl class="class"> |
| <dt id="mxnet.gluon.contrib.rnn.Conv2DLSTMCell"> |
| <em class="property">class </em><code class="descclassname">mxnet.gluon.contrib.rnn.</code><code class="descname">Conv2DLSTMCell</code><span class="sig-paren">(</span><em>input_shape</em>, <em>hidden_channels</em>, <em>i2h_kernel</em>, <em>h2h_kernel</em>, <em>i2h_pad=(0</em>, <em>0)</em>, <em>i2h_dilate=(1</em>, <em>1)</em>, <em>h2h_dilate=(1</em>, <em>1)</em>, <em>i2h_weight_initializer=None</em>, <em>h2h_weight_initializer=None</em>, <em>i2h_bias_initializer='zeros'</em>, <em>h2h_bias_initializer='zeros'</em>, <em>conv_layout='NCHW'</em>, <em>activation='tanh'</em>, <em>prefix=None</em>, <em>params=None</em><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/mxnet/gluon/contrib/rnn/conv_rnn_cell.html#Conv2DLSTMCell"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.gluon.contrib.rnn.Conv2DLSTMCell" title="Permalink to this definition">¶</a></dt> |
| <dd><p>2D Convolutional LSTM network cell.</p> |
| <p><a class="reference external" href="https://arxiv.org/abs/1506.04214">“Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting”</a> paper. Xingjian et al. NIPS2015</p> |
| <div class="math"> |
| \[\begin{split}\begin{array}{ll} |
| i_t = \sigma(W_i \ast x_t + R_i \ast h_{t-1} + b_i) \\ |
| f_t = \sigma(W_f \ast x_t + R_f \ast h_{t-1} + b_f) \\ |
| o_t = \sigma(W_o \ast x_t + R_o \ast h_{t-1} + b_o) \\ |
| c^\prime_t = tanh(W_c \ast x_t + R_c \ast h_{t-1} + b_c) \\ |
| c_t = f_t \circ c_{t-1} + i_t \circ c^\prime_t \\ |
| h_t = o_t \circ tanh(c_t) \\ |
| \end{array}\end{split}\]</div> |
| <table class="docutils field-list" frame="void" rules="none"> |
| <col class="field-name"/> |
| <col class="field-body"/> |
| <tbody valign="top"> |
| <tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple"> |
| <li><strong>input_shape</strong> (<em>tuple of int</em>) – Input tensor shape at each time step for each sample, excluding dimension of the batch size |
| and sequence length. Must be consistent with <cite>conv_layout</cite>. |
| For example, for layout ‘NCHW’ the shape should be (C, H, W).</li> |
| <li><strong>hidden_channels</strong> (<em>int</em>) – Number of output channels.</li> |
| <li><strong>i2h_kernel</strong> (<em>int</em><em> or </em><em>tuple of int</em>) – Input convolution kernel sizes.</li> |
| <li><strong>h2h_kernel</strong> (<em>int</em><em> or </em><em>tuple of int</em>) – Recurrent convolution kernel sizes. Only odd-numbered sizes are supported.</li> |
| <li><strong>i2h_pad</strong> (<em>int</em><em> or </em><em>tuple of int</em><em>, </em><em>default</em><em> (</em><em>0</em><em>, </em><em>0</em><em>)</em>) – Pad for input convolution.</li> |
| <li><strong>i2h_dilate</strong> (<em>int</em><em> or </em><em>tuple of int</em><em>, </em><em>default</em><em> (</em><em>1</em><em>, </em><em>1</em><em>)</em>) – Input convolution dilate.</li> |
| <li><strong>h2h_dilate</strong> (<em>int</em><em> or </em><em>tuple of int</em><em>, </em><em>default</em><em> (</em><em>1</em><em>, </em><em>1</em><em>)</em>) – Recurrent convolution dilate.</li> |
| <li><strong>i2h_weight_initializer</strong> (<em>str</em><em> or </em><a class="reference internal" href="../optimization/optimization.html#mxnet.initializer.Initializer" title="mxnet.initializer.Initializer"><em>Initializer</em></a>) – Initializer for the input weights matrix, used for the input convolutions.</li> |
| <li><strong>h2h_weight_initializer</strong> (<em>str</em><em> or </em><a class="reference internal" href="../optimization/optimization.html#mxnet.initializer.Initializer" title="mxnet.initializer.Initializer"><em>Initializer</em></a>) – Initializer for the recurrent weights matrix, used for the input convolutions.</li> |
| <li><strong>i2h_bias_initializer</strong> (<em>str</em><em> or </em><a class="reference internal" href="../optimization/optimization.html#mxnet.initializer.Initializer" title="mxnet.initializer.Initializer"><em>Initializer</em></a><em>, </em><em>default zeros</em>) – Initializer for the input convolution bias vectors.</li> |
| <li><strong>h2h_bias_initializer</strong> (<em>str</em><em> or </em><a class="reference internal" href="../optimization/optimization.html#mxnet.initializer.Initializer" title="mxnet.initializer.Initializer"><em>Initializer</em></a><em>, </em><em>default zeros</em>) – Initializer for the recurrent convolution bias vectors.</li> |
| <li><strong>conv_layout</strong> (<em>str</em><em>, </em><em>default 'NCHW'</em>) – Layout for all convolution inputs, outputs and weights. Options are ‘NCHW’ and ‘NHWC’.</li> |
| <li><strong>activation</strong> (<em>str</em><em> or </em><a class="reference internal" href="gluon.html#mxnet.gluon.Block" title="mxnet.gluon.Block"><em>Block</em></a><em>, </em><em>default 'tanh'</em>) – Type of activation function used in c^prime_t. |
| If argument type is string, it’s equivalent to nn.Activation(act_type=str). See |
| <a class="reference internal" href="../ndarray/ndarray.html#mxnet.ndarray.Activation" title="mxnet.ndarray.Activation"><code class="xref py py-func docutils literal"><span class="pre">Activation()</span></code></a> for available choices. |
| Alternatively, other activation blocks such as nn.LeakyReLU can be used.</li> |
| <li><strong>prefix</strong> (str, default ‘<a href="#id23"><span class="problematic" id="id24">conv_lstm_</span></a>‘) – Prefix for name of layers (and name of weight if params is None).</li> |
| <li><strong>params</strong> (<a class="reference internal" href="../symbol/rnn.html#mxnet.rnn.RNNParams" title="mxnet.rnn.RNNParams"><em>RNNParams</em></a><em>, </em><em>default None</em>) – Container for weight sharing between cells. Created if None.</li> |
| </ul> |
| </td> |
| </tr> |
| </tbody> |
| </table> |
| </dd></dl> |
| <dl class="class"> |
| <dt id="mxnet.gluon.contrib.rnn.Conv3DLSTMCell"> |
| <em class="property">class </em><code class="descclassname">mxnet.gluon.contrib.rnn.</code><code class="descname">Conv3DLSTMCell</code><span class="sig-paren">(</span><em>input_shape</em>, <em>hidden_channels</em>, <em>i2h_kernel</em>, <em>h2h_kernel</em>, <em>i2h_pad=(0</em>, <em>0</em>, <em>0)</em>, <em>i2h_dilate=(1</em>, <em>1</em>, <em>1)</em>, <em>h2h_dilate=(1</em>, <em>1</em>, <em>1)</em>, <em>i2h_weight_initializer=None</em>, <em>h2h_weight_initializer=None</em>, <em>i2h_bias_initializer='zeros'</em>, <em>h2h_bias_initializer='zeros'</em>, <em>conv_layout='NCDHW'</em>, <em>activation='tanh'</em>, <em>prefix=None</em>, <em>params=None</em><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/mxnet/gluon/contrib/rnn/conv_rnn_cell.html#Conv3DLSTMCell"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.gluon.contrib.rnn.Conv3DLSTMCell" title="Permalink to this definition">¶</a></dt> |
| <dd><p>3D Convolutional LSTM network cell.</p> |
| <p><a class="reference external" href="https://arxiv.org/abs/1506.04214">“Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting”</a> paper. Xingjian et al. NIPS2015</p> |
| <div class="math"> |
| \[\begin{split}\begin{array}{ll} |
| i_t = \sigma(W_i \ast x_t + R_i \ast h_{t-1} + b_i) \\ |
| f_t = \sigma(W_f \ast x_t + R_f \ast h_{t-1} + b_f) \\ |
| o_t = \sigma(W_o \ast x_t + R_o \ast h_{t-1} + b_o) \\ |
| c^\prime_t = tanh(W_c \ast x_t + R_c \ast h_{t-1} + b_c) \\ |
| c_t = f_t \circ c_{t-1} + i_t \circ c^\prime_t \\ |
| h_t = o_t \circ tanh(c_t) \\ |
| \end{array}\end{split}\]</div> |
| <table class="docutils field-list" frame="void" rules="none"> |
| <col class="field-name"/> |
| <col class="field-body"/> |
| <tbody valign="top"> |
| <tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple"> |
| <li><strong>input_shape</strong> (<em>tuple of int</em>) – Input tensor shape at each time step for each sample, excluding dimension of the batch size |
| and sequence length. Must be consistent with <cite>conv_layout</cite>. |
| For example, for layout ‘NCDHW’ the shape should be (C, D, H, W).</li> |
| <li><strong>hidden_channels</strong> (<em>int</em>) – Number of output channels.</li> |
| <li><strong>i2h_kernel</strong> (<em>int</em><em> or </em><em>tuple of int</em>) – Input convolution kernel sizes.</li> |
| <li><strong>h2h_kernel</strong> (<em>int</em><em> or </em><em>tuple of int</em>) – Recurrent convolution kernel sizes. Only odd-numbered sizes are supported.</li> |
| <li><strong>i2h_pad</strong> (<em>int</em><em> or </em><em>tuple of int</em><em>, </em><em>default</em><em> (</em><em>0</em><em>, </em><em>0</em><em>, </em><em>0</em><em>)</em>) – Pad for input convolution.</li> |
| <li><strong>i2h_dilate</strong> (<em>int</em><em> or </em><em>tuple of int</em><em>, </em><em>default</em><em> (</em><em>1</em><em>, </em><em>1</em><em>, </em><em>1</em><em>)</em>) – Input convolution dilate.</li> |
| <li><strong>h2h_dilate</strong> (<em>int</em><em> or </em><em>tuple of int</em><em>, </em><em>default</em><em> (</em><em>1</em><em>, </em><em>1</em><em>, </em><em>1</em><em>)</em>) – Recurrent convolution dilate.</li> |
| <li><strong>i2h_weight_initializer</strong> (<em>str</em><em> or </em><a class="reference internal" href="../optimization/optimization.html#mxnet.initializer.Initializer" title="mxnet.initializer.Initializer"><em>Initializer</em></a>) – Initializer for the input weights matrix, used for the input convolutions.</li> |
| <li><strong>h2h_weight_initializer</strong> (<em>str</em><em> or </em><a class="reference internal" href="../optimization/optimization.html#mxnet.initializer.Initializer" title="mxnet.initializer.Initializer"><em>Initializer</em></a>) – Initializer for the recurrent weights matrix, used for the input convolutions.</li> |
| <li><strong>i2h_bias_initializer</strong> (<em>str</em><em> or </em><a class="reference internal" href="../optimization/optimization.html#mxnet.initializer.Initializer" title="mxnet.initializer.Initializer"><em>Initializer</em></a><em>, </em><em>default zeros</em>) – Initializer for the input convolution bias vectors.</li> |
| <li><strong>h2h_bias_initializer</strong> (<em>str</em><em> or </em><a class="reference internal" href="../optimization/optimization.html#mxnet.initializer.Initializer" title="mxnet.initializer.Initializer"><em>Initializer</em></a><em>, </em><em>default zeros</em>) – Initializer for the recurrent convolution bias vectors.</li> |
| <li><strong>conv_layout</strong> (<em>str</em><em>, </em><em>default 'NCDHW'</em>) – Layout for all convolution inputs, outputs and weights. Options are ‘NCDHW’ and ‘NDHWC’.</li> |
| <li><strong>activation</strong> (<em>str</em><em> or </em><a class="reference internal" href="gluon.html#mxnet.gluon.Block" title="mxnet.gluon.Block"><em>Block</em></a><em>, </em><em>default 'tanh'</em>) – Type of activation function used in c^prime_t. |
| If argument type is string, it’s equivalent to nn.Activation(act_type=str). See |
| <a class="reference internal" href="../ndarray/ndarray.html#mxnet.ndarray.Activation" title="mxnet.ndarray.Activation"><code class="xref py py-func docutils literal"><span class="pre">Activation()</span></code></a> for available choices. |
| Alternatively, other activation blocks such as nn.LeakyReLU can be used.</li> |
| <li><strong>prefix</strong> (str, default ‘<a href="#id25"><span class="problematic" id="id26">conv_lstm_</span></a>‘) – Prefix for name of layers (and name of weight if params is None).</li> |
| <li><strong>params</strong> (<a class="reference internal" href="../symbol/rnn.html#mxnet.rnn.RNNParams" title="mxnet.rnn.RNNParams"><em>RNNParams</em></a><em>, </em><em>default None</em>) – Container for weight sharing between cells. Created if None.</li> |
| </ul> |
| </td> |
| </tr> |
| </tbody> |
| </table> |
| </dd></dl> |
| <dl class="class"> |
| <dt id="mxnet.gluon.contrib.rnn.Conv1DGRUCell"> |
| <em class="property">class </em><code class="descclassname">mxnet.gluon.contrib.rnn.</code><code class="descname">Conv1DGRUCell</code><span class="sig-paren">(</span><em>input_shape</em>, <em>hidden_channels</em>, <em>i2h_kernel</em>, <em>h2h_kernel</em>, <em>i2h_pad=(0</em>, <em>)</em>, <em>i2h_dilate=(1</em>, <em>)</em>, <em>h2h_dilate=(1</em>, <em>)</em>, <em>i2h_weight_initializer=None</em>, <em>h2h_weight_initializer=None</em>, <em>i2h_bias_initializer='zeros'</em>, <em>h2h_bias_initializer='zeros'</em>, <em>conv_layout='NCW'</em>, <em>activation='tanh'</em>, <em>prefix=None</em>, <em>params=None</em><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/mxnet/gluon/contrib/rnn/conv_rnn_cell.html#Conv1DGRUCell"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.gluon.contrib.rnn.Conv1DGRUCell" title="Permalink to this definition">¶</a></dt> |
| <dd><p>1D Convolutional Gated Rectified Unit (GRU) network cell.</p> |
| <div class="math"> |
| \[\begin{split}\begin{array}{ll} |
| r_t = \sigma(W_r \ast x_t + R_r \ast h_{t-1} + b_r) \\ |
| z_t = \sigma(W_z \ast x_t + R_z \ast h_{t-1} + b_z) \\ |
| n_t = tanh(W_i \ast x_t + b_i + r_t \circ (R_n \ast h_{t-1} + b_n)) \\ |
| h^\prime_t = (1 - z_t) \circ n_t + z_t \circ h \\ |
| \end{array}\end{split}\]</div> |
| <table class="docutils field-list" frame="void" rules="none"> |
| <col class="field-name"/> |
| <col class="field-body"/> |
| <tbody valign="top"> |
| <tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple"> |
| <li><strong>input_shape</strong> (<em>tuple of int</em>) – Input tensor shape at each time step for each sample, excluding dimension of the batch size |
| and sequence length. Must be consistent with <cite>conv_layout</cite>. |
| For example, for layout ‘NCW’ the shape should be (C, W).</li> |
| <li><strong>hidden_channels</strong> (<em>int</em>) – Number of output channels.</li> |
| <li><strong>i2h_kernel</strong> (<em>int</em><em> or </em><em>tuple of int</em>) – Input convolution kernel sizes.</li> |
| <li><strong>h2h_kernel</strong> (<em>int</em><em> or </em><em>tuple of int</em>) – Recurrent convolution kernel sizes. Only odd-numbered sizes are supported.</li> |
| <li><strong>i2h_pad</strong> (<em>int</em><em> or </em><em>tuple of int</em><em>, </em><em>default</em><em> (</em><em>0</em><em>,</em><em>)</em>) – Pad for input convolution.</li> |
| <li><strong>i2h_dilate</strong> (<em>int</em><em> or </em><em>tuple of int</em><em>, </em><em>default</em><em> (</em><em>1</em><em>,</em><em>)</em>) – Input convolution dilate.</li> |
| <li><strong>h2h_dilate</strong> (<em>int</em><em> or </em><em>tuple of int</em><em>, </em><em>default</em><em> (</em><em>1</em><em>,</em><em>)</em>) – Recurrent convolution dilate.</li> |
| <li><strong>i2h_weight_initializer</strong> (<em>str</em><em> or </em><a class="reference internal" href="../optimization/optimization.html#mxnet.initializer.Initializer" title="mxnet.initializer.Initializer"><em>Initializer</em></a>) – Initializer for the input weights matrix, used for the input convolutions.</li> |
| <li><strong>h2h_weight_initializer</strong> (<em>str</em><em> or </em><a class="reference internal" href="../optimization/optimization.html#mxnet.initializer.Initializer" title="mxnet.initializer.Initializer"><em>Initializer</em></a>) – Initializer for the recurrent weights matrix, used for the input convolutions.</li> |
| <li><strong>i2h_bias_initializer</strong> (<em>str</em><em> or </em><a class="reference internal" href="../optimization/optimization.html#mxnet.initializer.Initializer" title="mxnet.initializer.Initializer"><em>Initializer</em></a><em>, </em><em>default zeros</em>) – Initializer for the input convolution bias vectors.</li> |
| <li><strong>h2h_bias_initializer</strong> (<em>str</em><em> or </em><a class="reference internal" href="../optimization/optimization.html#mxnet.initializer.Initializer" title="mxnet.initializer.Initializer"><em>Initializer</em></a><em>, </em><em>default zeros</em>) – Initializer for the recurrent convolution bias vectors.</li> |
| <li><strong>conv_layout</strong> (<em>str</em><em>, </em><em>default 'NCW'</em>) – Layout for all convolution inputs, outputs and weights. Options are ‘NCW’ and ‘NWC’.</li> |
| <li><strong>activation</strong> (<em>str</em><em> or </em><a class="reference internal" href="gluon.html#mxnet.gluon.Block" title="mxnet.gluon.Block"><em>Block</em></a><em>, </em><em>default 'tanh'</em>) – Type of activation function used in n_t. |
| If argument type is string, it’s equivalent to nn.Activation(act_type=str). See |
| <a class="reference internal" href="../ndarray/ndarray.html#mxnet.ndarray.Activation" title="mxnet.ndarray.Activation"><code class="xref py py-func docutils literal"><span class="pre">Activation()</span></code></a> for available choices. |
| Alternatively, other activation blocks such as nn.LeakyReLU can be used.</li> |
| <li><strong>prefix</strong> (str, default ‘<a href="#id27"><span class="problematic" id="id28">conv_gru_</span></a>‘) – Prefix for name of layers (and name of weight if params is None).</li> |
| <li><strong>params</strong> (<a class="reference internal" href="../symbol/rnn.html#mxnet.rnn.RNNParams" title="mxnet.rnn.RNNParams"><em>RNNParams</em></a><em>, </em><em>default None</em>) – Container for weight sharing between cells. Created if None.</li> |
| </ul> |
| </td> |
| </tr> |
| </tbody> |
| </table> |
| </dd></dl> |
| <dl class="class"> |
| <dt id="mxnet.gluon.contrib.rnn.Conv2DGRUCell"> |
| <em class="property">class </em><code class="descclassname">mxnet.gluon.contrib.rnn.</code><code class="descname">Conv2DGRUCell</code><span class="sig-paren">(</span><em>input_shape</em>, <em>hidden_channels</em>, <em>i2h_kernel</em>, <em>h2h_kernel</em>, <em>i2h_pad=(0</em>, <em>0)</em>, <em>i2h_dilate=(1</em>, <em>1)</em>, <em>h2h_dilate=(1</em>, <em>1)</em>, <em>i2h_weight_initializer=None</em>, <em>h2h_weight_initializer=None</em>, <em>i2h_bias_initializer='zeros'</em>, <em>h2h_bias_initializer='zeros'</em>, <em>conv_layout='NCHW'</em>, <em>activation='tanh'</em>, <em>prefix=None</em>, <em>params=None</em><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/mxnet/gluon/contrib/rnn/conv_rnn_cell.html#Conv2DGRUCell"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.gluon.contrib.rnn.Conv2DGRUCell" title="Permalink to this definition">¶</a></dt> |
| <dd><p>2D Convolutional Gated Rectified Unit (GRU) network cell.</p> |
| <div class="math"> |
| \[\begin{split}\begin{array}{ll} |
| r_t = \sigma(W_r \ast x_t + R_r \ast h_{t-1} + b_r) \\ |
| z_t = \sigma(W_z \ast x_t + R_z \ast h_{t-1} + b_z) \\ |
| n_t = tanh(W_i \ast x_t + b_i + r_t \circ (R_n \ast h_{t-1} + b_n)) \\ |
| h^\prime_t = (1 - z_t) \circ n_t + z_t \circ h \\ |
| \end{array}\end{split}\]</div> |
| <table class="docutils field-list" frame="void" rules="none"> |
| <col class="field-name"/> |
| <col class="field-body"/> |
| <tbody valign="top"> |
| <tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple"> |
| <li><strong>input_shape</strong> (<em>tuple of int</em>) – Input tensor shape at each time step for each sample, excluding dimension of the batch size |
| and sequence length. Must be consistent with <cite>conv_layout</cite>. |
| For example, for layout ‘NCHW’ the shape should be (C, H, W).</li> |
| <li><strong>hidden_channels</strong> (<em>int</em>) – Number of output channels.</li> |
| <li><strong>i2h_kernel</strong> (<em>int</em><em> or </em><em>tuple of int</em>) – Input convolution kernel sizes.</li> |
| <li><strong>h2h_kernel</strong> (<em>int</em><em> or </em><em>tuple of int</em>) – Recurrent convolution kernel sizes. Only odd-numbered sizes are supported.</li> |
| <li><strong>i2h_pad</strong> (<em>int</em><em> or </em><em>tuple of int</em><em>, </em><em>default</em><em> (</em><em>0</em><em>, </em><em>0</em><em>)</em>) – Pad for input convolution.</li> |
| <li><strong>i2h_dilate</strong> (<em>int</em><em> or </em><em>tuple of int</em><em>, </em><em>default</em><em> (</em><em>1</em><em>, </em><em>1</em><em>)</em>) – Input convolution dilate.</li> |
| <li><strong>h2h_dilate</strong> (<em>int</em><em> or </em><em>tuple of int</em><em>, </em><em>default</em><em> (</em><em>1</em><em>, </em><em>1</em><em>)</em>) – Recurrent convolution dilate.</li> |
| <li><strong>i2h_weight_initializer</strong> (<em>str</em><em> or </em><a class="reference internal" href="../optimization/optimization.html#mxnet.initializer.Initializer" title="mxnet.initializer.Initializer"><em>Initializer</em></a>) – Initializer for the input weights matrix, used for the input convolutions.</li> |
| <li><strong>h2h_weight_initializer</strong> (<em>str</em><em> or </em><a class="reference internal" href="../optimization/optimization.html#mxnet.initializer.Initializer" title="mxnet.initializer.Initializer"><em>Initializer</em></a>) – Initializer for the recurrent weights matrix, used for the input convolutions.</li> |
| <li><strong>i2h_bias_initializer</strong> (<em>str</em><em> or </em><a class="reference internal" href="../optimization/optimization.html#mxnet.initializer.Initializer" title="mxnet.initializer.Initializer"><em>Initializer</em></a><em>, </em><em>default zeros</em>) – Initializer for the input convolution bias vectors.</li> |
| <li><strong>h2h_bias_initializer</strong> (<em>str</em><em> or </em><a class="reference internal" href="../optimization/optimization.html#mxnet.initializer.Initializer" title="mxnet.initializer.Initializer"><em>Initializer</em></a><em>, </em><em>default zeros</em>) – Initializer for the recurrent convolution bias vectors.</li> |
| <li><strong>conv_layout</strong> (<em>str</em><em>, </em><em>default 'NCHW'</em>) – Layout for all convolution inputs, outputs and weights. Options are ‘NCHW’ and ‘NHWC’.</li> |
| <li><strong>activation</strong> (<em>str</em><em> or </em><a class="reference internal" href="gluon.html#mxnet.gluon.Block" title="mxnet.gluon.Block"><em>Block</em></a><em>, </em><em>default 'tanh'</em>) – Type of activation function used in n_t. |
| If argument type is string, it’s equivalent to nn.Activation(act_type=str). See |
| <a class="reference internal" href="../ndarray/ndarray.html#mxnet.ndarray.Activation" title="mxnet.ndarray.Activation"><code class="xref py py-func docutils literal"><span class="pre">Activation()</span></code></a> for available choices. |
| Alternatively, other activation blocks such as nn.LeakyReLU can be used.</li> |
| <li><strong>prefix</strong> (str, default ‘<a href="#id29"><span class="problematic" id="id30">conv_gru_</span></a>‘) – Prefix for name of layers (and name of weight if params is None).</li> |
| <li><strong>params</strong> (<a class="reference internal" href="../symbol/rnn.html#mxnet.rnn.RNNParams" title="mxnet.rnn.RNNParams"><em>RNNParams</em></a><em>, </em><em>default None</em>) – Container for weight sharing between cells. Created if None.</li> |
| </ul> |
| </td> |
| </tr> |
| </tbody> |
| </table> |
| </dd></dl> |
| <dl class="class"> |
| <dt id="mxnet.gluon.contrib.rnn.Conv3DGRUCell"> |
| <em class="property">class </em><code class="descclassname">mxnet.gluon.contrib.rnn.</code><code class="descname">Conv3DGRUCell</code><span class="sig-paren">(</span><em>input_shape</em>, <em>hidden_channels</em>, <em>i2h_kernel</em>, <em>h2h_kernel</em>, <em>i2h_pad=(0</em>, <em>0</em>, <em>0)</em>, <em>i2h_dilate=(1</em>, <em>1</em>, <em>1)</em>, <em>h2h_dilate=(1</em>, <em>1</em>, <em>1)</em>, <em>i2h_weight_initializer=None</em>, <em>h2h_weight_initializer=None</em>, <em>i2h_bias_initializer='zeros'</em>, <em>h2h_bias_initializer='zeros'</em>, <em>conv_layout='NCDHW'</em>, <em>activation='tanh'</em>, <em>prefix=None</em>, <em>params=None</em><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/mxnet/gluon/contrib/rnn/conv_rnn_cell.html#Conv3DGRUCell"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.gluon.contrib.rnn.Conv3DGRUCell" title="Permalink to this definition">¶</a></dt> |
| <dd><p>3D Convolutional Gated Rectified Unit (GRU) network cell.</p> |
| <div class="math"> |
| \[\begin{split}\begin{array}{ll} |
| r_t = \sigma(W_r \ast x_t + R_r \ast h_{t-1} + b_r) \\ |
| z_t = \sigma(W_z \ast x_t + R_z \ast h_{t-1} + b_z) \\ |
| n_t = tanh(W_i \ast x_t + b_i + r_t \circ (R_n \ast h_{t-1} + b_n)) \\ |
| h^\prime_t = (1 - z_t) \circ n_t + z_t \circ h \\ |
| \end{array}\end{split}\]</div> |
| <table class="docutils field-list" frame="void" rules="none"> |
| <col class="field-name"/> |
| <col class="field-body"/> |
| <tbody valign="top"> |
| <tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple"> |
| <li><strong>input_shape</strong> (<em>tuple of int</em>) – Input tensor shape at each time step for each sample, excluding dimension of the batch size |
| and sequence length. Must be consistent with <cite>conv_layout</cite>. |
| For example, for layout ‘NCDHW’ the shape should be (C, D, H, W).</li> |
| <li><strong>hidden_channels</strong> (<em>int</em>) – Number of output channels.</li> |
| <li><strong>i2h_kernel</strong> (<em>int</em><em> or </em><em>tuple of int</em>) – Input convolution kernel sizes.</li> |
| <li><strong>h2h_kernel</strong> (<em>int</em><em> or </em><em>tuple of int</em>) – Recurrent convolution kernel sizes. Only odd-numbered sizes are supported.</li> |
| <li><strong>i2h_pad</strong> (<em>int</em><em> or </em><em>tuple of int</em><em>, </em><em>default</em><em> (</em><em>0</em><em>, </em><em>0</em><em>, </em><em>0</em><em>)</em>) – Pad for input convolution.</li> |
| <li><strong>i2h_dilate</strong> (<em>int</em><em> or </em><em>tuple of int</em><em>, </em><em>default</em><em> (</em><em>1</em><em>, </em><em>1</em><em>, </em><em>1</em><em>)</em>) – Input convolution dilate.</li> |
| <li><strong>h2h_dilate</strong> (<em>int</em><em> or </em><em>tuple of int</em><em>, </em><em>default</em><em> (</em><em>1</em><em>, </em><em>1</em><em>, </em><em>1</em><em>)</em>) – Recurrent convolution dilate.</li> |
| <li><strong>i2h_weight_initializer</strong> (<em>str</em><em> or </em><a class="reference internal" href="../optimization/optimization.html#mxnet.initializer.Initializer" title="mxnet.initializer.Initializer"><em>Initializer</em></a>) – Initializer for the input weights matrix, used for the input convolutions.</li> |
| <li><strong>h2h_weight_initializer</strong> (<em>str</em><em> or </em><a class="reference internal" href="../optimization/optimization.html#mxnet.initializer.Initializer" title="mxnet.initializer.Initializer"><em>Initializer</em></a>) – Initializer for the recurrent weights matrix, used for the input convolutions.</li> |
| <li><strong>i2h_bias_initializer</strong> (<em>str</em><em> or </em><a class="reference internal" href="../optimization/optimization.html#mxnet.initializer.Initializer" title="mxnet.initializer.Initializer"><em>Initializer</em></a><em>, </em><em>default zeros</em>) – Initializer for the input convolution bias vectors.</li> |
| <li><strong>h2h_bias_initializer</strong> (<em>str</em><em> or </em><a class="reference internal" href="../optimization/optimization.html#mxnet.initializer.Initializer" title="mxnet.initializer.Initializer"><em>Initializer</em></a><em>, </em><em>default zeros</em>) – Initializer for the recurrent convolution bias vectors.</li> |
| <li><strong>conv_layout</strong> (<em>str</em><em>, </em><em>default 'NCDHW'</em>) – Layout for all convolution inputs, outputs and weights. Options are ‘NCDHW’ and ‘NDHWC’.</li> |
| <li><strong>activation</strong> (<em>str</em><em> or </em><a class="reference internal" href="gluon.html#mxnet.gluon.Block" title="mxnet.gluon.Block"><em>Block</em></a><em>, </em><em>default 'tanh'</em>) – Type of activation function used in n_t. |
| If argument type is string, it’s equivalent to nn.Activation(act_type=str). See |
| <a class="reference internal" href="../ndarray/ndarray.html#mxnet.ndarray.Activation" title="mxnet.ndarray.Activation"><code class="xref py py-func docutils literal"><span class="pre">Activation()</span></code></a> for available choices. |
| Alternatively, other activation blocks such as nn.LeakyReLU can be used.</li> |
| <li><strong>prefix</strong> (str, default ‘<a href="#id31"><span class="problematic" id="id32">conv_gru_</span></a>‘) – Prefix for name of layers (and name of weight if params is None).</li> |
| <li><strong>params</strong> (<a class="reference internal" href="../symbol/rnn.html#mxnet.rnn.RNNParams" title="mxnet.rnn.RNNParams"><em>RNNParams</em></a><em>, </em><em>default None</em>) – Container for weight sharing between cells. Created if None.</li> |
| </ul> |
| </td> |
| </tr> |
| </tbody> |
| </table> |
| </dd></dl> |
| <dl class="class"> |
| <dt id="mxnet.gluon.contrib.rnn.VariationalDropoutCell"> |
| <em class="property">class </em><code class="descclassname">mxnet.gluon.contrib.rnn.</code><code class="descname">VariationalDropoutCell</code><span class="sig-paren">(</span><em>base_cell</em>, <em>drop_inputs=0.0</em>, <em>drop_states=0.0</em>, <em>drop_outputs=0.0</em><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/mxnet/gluon/contrib/rnn/rnn_cell.html#VariationalDropoutCell"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.gluon.contrib.rnn.VariationalDropoutCell" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Applies Variational Dropout on base cell. |
| (<a class="reference external" href="https://arxiv.org/pdf/1512.05287.pdf">https://arxiv.org/pdf/1512.05287.pdf</a>,</p> |
| <blockquote> |
| <div><a class="reference external" href="https://www.stat.berkeley.edu/~tsmoon/files/Conference/asru2015.pdf">https://www.stat.berkeley.edu/~tsmoon/files/Conference/asru2015.pdf</a>).</div></blockquote> |
| <p>Variational dropout uses the same dropout mask across time-steps. It can be applied to RNN |
| inputs, outputs, and states. The masks for them are not shared.</p> |
| <p>The dropout mask is initialized when stepping forward for the first time and will remain |
| the same until .reset() is called. Thus, if using the cell and stepping manually without calling |
| .unroll(), the .reset() should be called after each sequence.</p> |
| <table class="docutils field-list" frame="void" rules="none"> |
| <col class="field-name"/> |
| <col class="field-body"/> |
| <tbody valign="top"> |
| <tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple"> |
| <li><strong>base_cell</strong> (<a class="reference internal" href="rnn.html#mxnet.gluon.rnn.RecurrentCell" title="mxnet.gluon.rnn.RecurrentCell"><em>RecurrentCell</em></a>) – The cell on which to perform variational dropout.</li> |
| <li><strong>drop_inputs</strong> (<em>float</em><em>, </em><em>default 0.</em>) – The dropout rate for inputs. Won’t apply dropout if it equals 0.</li> |
| <li><strong>drop_states</strong> (<em>float</em><em>, </em><em>default 0.</em>) – The dropout rate for state inputs on the first state channel. |
| Won’t apply dropout if it equals 0.</li> |
| <li><strong>drop_outputs</strong> (<em>float</em><em>, </em><em>default 0.</em>) – The dropout rate for outputs. Won’t apply dropout if it equals 0.</li> |
| </ul> |
| </td> |
| </tr> |
| </tbody> |
| </table> |
| <dl class="method"> |
| <dt id="mxnet.gluon.contrib.rnn.VariationalDropoutCell.unroll"> |
| <code class="descname">unroll</code><span class="sig-paren">(</span><em>length</em>, <em>inputs</em>, <em>begin_state=None</em>, <em>layout='NTC'</em>, <em>merge_outputs=None</em>, <em>valid_length=None</em><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/mxnet/gluon/contrib/rnn/rnn_cell.html#VariationalDropoutCell.unroll"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.gluon.contrib.rnn.VariationalDropoutCell.unroll" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Unrolls an RNN cell across time steps.</p> |
| <table class="docutils field-list" frame="void" rules="none"> |
| <col class="field-name"/> |
| <col class="field-body"/> |
| <tbody valign="top"> |
| <tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple"> |
| <li><strong>length</strong> (<em>int</em>) – Number of steps to unroll.</li> |
| <li><strong>inputs</strong> (<a class="reference internal" href="../symbol/symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a><em>, </em><em>list of Symbol</em><em>, or </em><em>None</em>) – <p>If <cite>inputs</cite> is a single Symbol (usually the output |
| of Embedding symbol), it should have shape |
| (batch_size, length, ...) if <cite>layout</cite> is ‘NTC’, |
| or (length, batch_size, ...) if <cite>layout</cite> is ‘TNC’.</p> |
| <p>If <cite>inputs</cite> is a list of symbols (usually output of |
| previous unroll), they should all have shape |
| (batch_size, ...).</p> |
| </li> |
| <li><strong>begin_state</strong> (<em>nested list of Symbol</em><em>, </em><em>optional</em>) – Input states created by <cite>begin_state()</cite> |
| or output state of another cell. |
| Created from <cite>begin_state()</cite> if <cite>None</cite>.</li> |
| <li><strong>layout</strong> (<em>str</em><em>, </em><em>optional</em>) – <cite>layout</cite> of input symbol. Only used if inputs |
| is a single Symbol.</li> |
| <li><strong>merge_outputs</strong> (<em>bool</em><em>, </em><em>optional</em>) – If <cite>False</cite>, returns outputs as a list of Symbols. |
| If <cite>True</cite>, concatenates output across time steps |
| and returns a single symbol with shape |
| (batch_size, length, ...) if layout is ‘NTC’, |
| or (length, batch_size, ...) if layout is ‘TNC’. |
| If <cite>None</cite>, output whatever is faster.</li> |
| <li><strong>valid_length</strong> (<a class="reference internal" href="../symbol/symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a><em>, </em><a class="reference internal" href="../ndarray/ndarray.html#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a><em> or </em><em>None</em>) – <cite>valid_length</cite> specifies the length of the sequences in the batch without padding. |
| This option is especially useful for building sequence-to-sequence models where |
| the input and output sequences would potentially be padded. |
| If <cite>valid_length</cite> is None, all sequences are assumed to have the same length. |
| If <cite>valid_length</cite> is a Symbol or NDArray, it should have shape (batch_size,). |
| The ith element will be the length of the ith sequence in the batch. |
| The last valid state will be return and the padded outputs will be masked with 0. |
| Note that <cite>valid_length</cite> must be smaller or equal to <cite>length</cite>.</li> |
| </ul> |
| </td> |
| </tr> |
| <tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first last"><ul class="simple"> |
| <li><strong>outputs</strong> (<em>list of Symbol or Symbol</em>) – Symbol (if <cite>merge_outputs</cite> is True) or list of Symbols |
| (if <cite>merge_outputs</cite> is False) corresponding to the output from |
| the RNN from this unrolling.</li> |
| <li><strong>states</strong> (<em>list of Symbol</em>) – The new state of this RNN after this unrolling. |
| The type of this symbol is same as the output of <cite>begin_state()</cite>.</li> |
| </ul> |
| </p> |
| </td> |
| </tr> |
| </tbody> |
| </table> |
| </dd></dl> |
| </dd></dl> |
| <dl class="class"> |
| <dt id="mxnet.gluon.contrib.rnn.LSTMPCell"> |
| <em class="property">class </em><code class="descclassname">mxnet.gluon.contrib.rnn.</code><code class="descname">LSTMPCell</code><span class="sig-paren">(</span><em>hidden_size</em>, <em>projection_size</em>, <em>i2h_weight_initializer=None</em>, <em>h2h_weight_initializer=None</em>, <em>h2r_weight_initializer=None</em>, <em>i2h_bias_initializer='zeros'</em>, <em>h2h_bias_initializer='zeros'</em>, <em>input_size=0</em>, <em>prefix=None</em>, <em>params=None</em><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/mxnet/gluon/contrib/rnn/rnn_cell.html#LSTMPCell"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.gluon.contrib.rnn.LSTMPCell" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Long-Short Term Memory Projected (LSTMP) network cell. |
| (<a class="reference external" href="https://arxiv.org/abs/1402.1128">https://arxiv.org/abs/1402.1128</a>) |
| Each call computes the following function: |
| .. math:</p> |
| <div class="highlight-default"><div class="highlight"><pre><span></span>\<span class="n">begin</span><span class="p">{</span><span class="n">array</span><span class="p">}{</span><span class="n">ll</span><span class="p">}</span> |
| <span class="n">i_t</span> <span class="o">=</span> <span class="n">sigmoid</span><span class="p">(</span><span class="n">W_</span><span class="p">{</span><span class="n">ii</span><span class="p">}</span> <span class="n">x_t</span> <span class="o">+</span> <span class="n">b_</span><span class="p">{</span><span class="n">ii</span><span class="p">}</span> <span class="o">+</span> <span class="n">W_</span><span class="p">{</span><span class="n">ri</span><span class="p">}</span> <span class="n">r_</span><span class="p">{(</span><span class="n">t</span><span class="o">-</span><span class="mi">1</span><span class="p">)}</span> <span class="o">+</span> <span class="n">b_</span><span class="p">{</span><span class="n">ri</span><span class="p">})</span> \\ |
| <span class="n">f_t</span> <span class="o">=</span> <span class="n">sigmoid</span><span class="p">(</span><span class="n">W_</span><span class="p">{</span><span class="k">if</span><span class="p">}</span> <span class="n">x_t</span> <span class="o">+</span> <span class="n">b_</span><span class="p">{</span><span class="k">if</span><span class="p">}</span> <span class="o">+</span> <span class="n">W_</span><span class="p">{</span><span class="n">rf</span><span class="p">}</span> <span class="n">r_</span><span class="p">{(</span><span class="n">t</span><span class="o">-</span><span class="mi">1</span><span class="p">)}</span> <span class="o">+</span> <span class="n">b_</span><span class="p">{</span><span class="n">rf</span><span class="p">})</span> \\ |
| <span class="n">g_t</span> <span class="o">=</span> \<span class="n">tanh</span><span class="p">(</span><span class="n">W_</span><span class="p">{</span><span class="n">ig</span><span class="p">}</span> <span class="n">x_t</span> <span class="o">+</span> <span class="n">b_</span><span class="p">{</span><span class="n">ig</span><span class="p">}</span> <span class="o">+</span> <span class="n">W_</span><span class="p">{</span><span class="n">rc</span><span class="p">}</span> <span class="n">r_</span><span class="p">{(</span><span class="n">t</span><span class="o">-</span><span class="mi">1</span><span class="p">)}</span> <span class="o">+</span> <span class="n">b_</span><span class="p">{</span><span class="n">rg</span><span class="p">}})</span> \\ |
| <span class="n">o_t</span> <span class="o">=</span> <span class="n">sigmoid</span><span class="p">(</span><span class="n">W_</span><span class="p">{</span><span class="n">io</span><span class="p">}</span> <span class="n">x_t</span> <span class="o">+</span> <span class="n">b_</span><span class="p">{</span><span class="n">io</span><span class="p">}</span> <span class="o">+</span> <span class="n">W_</span><span class="p">{</span><span class="n">ro</span><span class="p">}</span> <span class="n">r_</span><span class="p">{(</span><span class="n">t</span><span class="o">-</span><span class="mi">1</span><span class="p">)}</span> <span class="o">+</span> <span class="n">b_</span><span class="p">{</span><span class="n">ro</span><span class="p">})</span> \\ |
| <span class="n">c_t</span> <span class="o">=</span> <span class="n">f_t</span> <span class="o">*</span> <span class="n">c_</span><span class="p">{(</span><span class="n">t</span><span class="o">-</span><span class="mi">1</span><span class="p">)}</span> <span class="o">+</span> <span class="n">i_t</span> <span class="o">*</span> <span class="n">g_t</span> \\ |
| <span class="n">h_t</span> <span class="o">=</span> <span class="n">o_t</span> <span class="o">*</span> \<span class="n">tanh</span><span class="p">(</span><span class="n">c_t</span><span class="p">)</span> \\ |
| <span class="n">r_t</span> <span class="o">=</span> <span class="n">W_</span><span class="p">{</span><span class="n">hr</span><span class="p">}</span> <span class="n">h_t</span> |
| \<span class="n">end</span><span class="p">{</span><span class="n">array</span><span class="p">}</span> |
| </pre></div> |
| </div> |
| <p>where <span class="math">\(r_t\)</span> is the projected recurrent activation at time <cite>t</cite>, |
| math:<cite>h_t</cite> is the hidden state at time <cite>t</cite>, <span class="math">\(c_t\)</span> is the |
| cell state at time <cite>t</cite>, <span class="math">\(x_t\)</span> is the input at time <cite>t</cite>, and <span class="math">\(i_t\)</span>, |
| <span class="math">\(f_t\)</span>, <span class="math">\(g_t\)</span>, <span class="math">\(o_t\)</span> are the input, forget, cell, and |
| out gates, respectively. |
| :param hidden_size: Number of units in cell state symbol. |
| :type hidden_size: int |
| :param projection_size: Number of units in output symbol. |
| :type projection_size: int |
| :param i2h_weight_initializer: Initializer for the input weights matrix, used for the linear</p> |
| <blockquote> |
| <div>transformation of the inputs.</div></blockquote> |
| <table class="docutils field-list" frame="void" rules="none"> |
| <col class="field-name"/> |
| <col class="field-body"/> |
| <tbody valign="top"> |
| <tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple"> |
| <li><strong>h2h_weight_initializer</strong> (<em>str</em><em> or </em><a class="reference internal" href="../optimization/optimization.html#mxnet.initializer.Initializer" title="mxnet.initializer.Initializer"><em>Initializer</em></a>) – Initializer for the recurrent weights matrix, used for the linear |
| transformation of the hidden state.</li> |
| <li><strong>h2r_weight_initializer</strong> (<em>str</em><em> or </em><a class="reference internal" href="../optimization/optimization.html#mxnet.initializer.Initializer" title="mxnet.initializer.Initializer"><em>Initializer</em></a>) – Initializer for the projection weights matrix, used for the linear |
| transformation of the recurrent state.</li> |
| <li><strong>i2h_bias_initializer</strong> (<em>str</em><em> or </em><a class="reference internal" href="../optimization/optimization.html#mxnet.initializer.Initializer" title="mxnet.initializer.Initializer"><em>Initializer</em></a><em>, </em><em>default 'lstmbias'</em>) – Initializer for the bias vector. By default, bias for the forget |
| gate is initialized to 1 while all other biases are initialized |
| to zero.</li> |
| <li><strong>h2h_bias_initializer</strong> (<em>str</em><em> or </em><a class="reference internal" href="../optimization/optimization.html#mxnet.initializer.Initializer" title="mxnet.initializer.Initializer"><em>Initializer</em></a>) – Initializer for the bias vector.</li> |
| <li><strong>prefix</strong> (str, default ‘<a href="#id33"><span class="problematic" id="id34">lstmp_</span></a>‘) – Prefix for name of <cite>Block`s |
| (and name of weight if params is `None</cite>).</li> |
| <li><strong>params</strong> (<a class="reference internal" href="gluon.html#mxnet.gluon.Parameter" title="mxnet.gluon.Parameter"><em>Parameter</em></a><em> or </em><em>None</em>) – Container for weight sharing between cells. |
| Created if <cite>None</cite>.</li> |
| <li><strong>Inputs</strong> – <ul> |
| <li><strong>data</strong>: input tensor with shape <cite>(batch_size, input_size)</cite>.</li> |
| <li><strong>states</strong>: a list of two initial recurrent state tensors, with shape |
| <cite>(batch_size, projection_size)</cite> and <cite>(batch_size, hidden_size)</cite> respectively.</li> |
| </ul> |
| </li> |
| <li><strong>Outputs</strong> – <ul> |
| <li><strong>out</strong>: output tensor with shape <cite>(batch_size, num_hidden)</cite>.</li> |
| <li><strong>next_states</strong>: a list of two output recurrent state tensors. Each has |
| the same shape as <cite>states</cite>.</li> |
| </ul> |
| </li> |
| </ul> |
| </td> |
| </tr> |
| </tbody> |
| </table> |
| </dd></dl> |
| <span class="target" id="module-mxnet.gluon.contrib.data"></span><p>Contrib datasets.</p> |
| <dl class="class"> |
| <dt id="mxnet.gluon.contrib.data.IntervalSampler"> |
| <em class="property">class </em><code class="descclassname">mxnet.gluon.contrib.data.</code><code class="descname">IntervalSampler</code><span class="sig-paren">(</span><em>length</em>, <em>interval</em>, <em>rollover=True</em><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/mxnet/gluon/contrib/data/sampler.html#IntervalSampler"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.gluon.contrib.data.IntervalSampler" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Samples elements from [0, length) at fixed intervals.</p> |
| <table class="docutils field-list" frame="void" rules="none"> |
| <col class="field-name"/> |
| <col class="field-body"/> |
| <tbody valign="top"> |
| <tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple"> |
| <li><strong>length</strong> (<em>int</em>) – Length of the sequence.</li> |
| <li><strong>interval</strong> (<em>int</em>) – The number of items to skip between two samples.</li> |
| <li><strong>rollover</strong> (<em>bool</em><em>, </em><em>default True</em>) – Whether to start again from the first skipped item after reaching the end. |
| If true, this sampler would start again from the first skipped item until all items |
| are visited. |
| Otherwise, iteration stops when end is reached and skipped items are ignored.</li> |
| </ul> |
| </td> |
| </tr> |
| </tbody> |
| </table> |
| <p class="rubric">Examples</p> |
| <div class="highlight-default"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">sampler</span> <span class="o">=</span> <span class="n">contrib</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">IntervalSampler</span><span class="p">(</span><span class="mi">13</span><span class="p">,</span> <span class="n">interval</span><span class="o">=</span><span class="mi">3</span><span class="p">)</span> |
| <span class="gp">>>> </span><span class="nb">list</span><span class="p">(</span><span class="n">sampler</span><span class="p">)</span> |
| <span class="go">[0, 3, 6, 9, 12, 1, 4, 7, 10, 2, 5, 8, 11]</span> |
| <span class="gp">>>> </span><span class="n">sampler</span> <span class="o">=</span> <span class="n">contrib</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">IntervalSampler</span><span class="p">(</span><span class="mi">13</span><span class="p">,</span> <span class="n">interval</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span> <span class="n">rollover</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span> |
| <span class="gp">>>> </span><span class="nb">list</span><span class="p">(</span><span class="n">sampler</span><span class="p">)</span> |
| <span class="go">[0, 3, 6, 9, 12]</span> |
| </pre></div> |
| </div> |
| </dd></dl> |
| <span class="target" id="module-mxnet.gluon.contrib.data.text"></span><p>Text datasets.</p> |
| <dl class="class"> |
| <dt id="mxnet.gluon.contrib.data.text.WikiText2"> |
| <em class="property">class </em><code class="descclassname">mxnet.gluon.contrib.data.text.</code><code class="descname">WikiText2</code><span class="sig-paren">(</span><em>root='/work/mxnet/datasets/wikitext-2'</em>, <em>segment='train'</em>, <em>vocab=None</em>, <em>seq_len=35</em><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/mxnet/gluon/contrib/data/text.html#WikiText2"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.gluon.contrib.data.text.WikiText2" title="Permalink to this definition">¶</a></dt> |
| <dd><p>WikiText-2 word-level dataset for language modeling, from Salesforce research.</p> |
| <p>From |
| <a class="reference external" href="https://einstein.ai/research/the-wikitext-long-term-dependency-language-modeling-dataset">https://einstein.ai/research/the-wikitext-long-term-dependency-language-modeling-dataset</a></p> |
| <p>License: Creative Commons Attribution-ShareAlike</p> |
| <p>Each sample is a vector of length equal to the specified sequence length. |
| At the end of each sentence, an end-of-sentence token ‘<eos>’ is added.</p> |
| <table class="docutils field-list" frame="void" rules="none"> |
| <col class="field-name"/> |
| <col class="field-body"/> |
| <tbody valign="top"> |
| <tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple"> |
| <li><strong>root</strong> (<em>str</em><em>, </em><em>default $MXNET_HOME/datasets/wikitext-2</em>) – Path to temp folder for storing data.</li> |
| <li><strong>segment</strong> (<em>str</em><em>, </em><em>default 'train'</em>) – Dataset segment. Options are ‘train’, ‘validation’, ‘test’.</li> |
| <li><strong>vocab</strong> (<a class="reference internal" href="../contrib/text.html#mxnet.contrib.text.vocab.Vocabulary" title="mxnet.contrib.text.vocab.Vocabulary"><code class="xref py py-class docutils literal"><span class="pre">Vocabulary</span></code></a>, default None) – The vocabulary to use for indexing the text dataset. |
| If None, a default vocabulary is created.</li> |
| <li><strong>seq_len</strong> (<em>int</em><em>, </em><em>default 35</em>) – The sequence length of each sample, regardless of the sentence boundary.</li> |
| </ul> |
| </td> |
| </tr> |
| </tbody> |
| </table> |
| </dd></dl> |
| <dl class="class"> |
| <dt id="mxnet.gluon.contrib.data.text.WikiText103"> |
| <em class="property">class </em><code class="descclassname">mxnet.gluon.contrib.data.text.</code><code class="descname">WikiText103</code><span class="sig-paren">(</span><em>root='/work/mxnet/datasets/wikitext-103'</em>, <em>segment='train'</em>, <em>vocab=None</em>, <em>seq_len=35</em><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/mxnet/gluon/contrib/data/text.html#WikiText103"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.gluon.contrib.data.text.WikiText103" title="Permalink to this definition">¶</a></dt> |
| <dd><p>WikiText-103 word-level dataset for language modeling, from Salesforce research.</p> |
| <p>From |
| <a class="reference external" href="https://einstein.ai/research/the-wikitext-long-term-dependency-language-modeling-dataset">https://einstein.ai/research/the-wikitext-long-term-dependency-language-modeling-dataset</a></p> |
| <p>License: Creative Commons Attribution-ShareAlike</p> |
| <p>Each sample is a vector of length equal to the specified sequence length. |
| At the end of each sentence, an end-of-sentence token ‘<eos>’ is added.</p> |
| <table class="docutils field-list" frame="void" rules="none"> |
| <col class="field-name"/> |
| <col class="field-body"/> |
| <tbody valign="top"> |
| <tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple"> |
| <li><strong>root</strong> (<em>str</em><em>, </em><em>default $MXNET_HOME/datasets/wikitext-103</em>) – Path to temp folder for storing data.</li> |
| <li><strong>segment</strong> (<em>str</em><em>, </em><em>default 'train'</em>) – Dataset segment. Options are ‘train’, ‘validation’, ‘test’.</li> |
| <li><strong>vocab</strong> (<a class="reference internal" href="../contrib/text.html#mxnet.contrib.text.vocab.Vocabulary" title="mxnet.contrib.text.vocab.Vocabulary"><code class="xref py py-class docutils literal"><span class="pre">Vocabulary</span></code></a>, default None) – The vocabulary to use for indexing the text dataset. |
| If None, a default vocabulary is created.</li> |
| <li><strong>seq_len</strong> (<em>int</em><em>, </em><em>default 35</em>) – The sequence length of each sample, regardless of the sentence boundary.</li> |
| </ul> |
| </td> |
| </tr> |
| </tbody> |
| </table> |
| </dd></dl> |
| <script>auto_index("api-reference");</script></div> |
| </div> |
| </div> |
| </div> |
| <div aria-label="main navigation" class="sphinxsidebar rightsidebar" role="navigation"> |
| <div class="sphinxsidebarwrapper"> |
| <h3><a href="../../../index.html">Table Of Contents</a></h3> |
| <ul> |
| <li><a class="reference internal" href="#">Gluon Contrib API</a><ul> |
| <li><a class="reference internal" href="#overview">Overview</a></li> |
| <li><a class="reference internal" href="#contrib">Contrib</a><ul> |
| <li><a class="reference internal" href="#neural-network">Neural network</a></li> |
| <li><a class="reference internal" href="#recurrent-neural-network">Recurrent neural network</a></li> |
| <li><a class="reference internal" href="#data">Data</a><ul> |
| <li><a class="reference internal" href="#text-dataset">Text dataset</a></li> |
| </ul> |
| </li> |
| </ul> |
| </li> |
| <li><a class="reference internal" href="#api-reference">API Reference</a></li> |
| </ul> |
| </li> |
| </ul> |
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| <img height="60" src="https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/image/apache_incubator_logo.png"/> |
| <p> |
| Apache MXNet is an effort undergoing incubation at The Apache Software Foundation (ASF), <strong>sponsored by the <i>Apache Incubator</i></strong>. Incubation is required of all newly accepted projects until a further review indicates that the infrastructure, communications, and decision making process have stabilized in a manner consistent with other successful ASF projects. While incubation status is not necessarily a reflection of the completeness or stability of the code, it does indicate that the project has yet to be fully endorsed by the ASF. |
| </p> |
| <p> |
| "Copyright © 2017-2018, The Apache Software Foundation |
| Apache MXNet, MXNet, Apache, the Apache feather, and the Apache MXNet project logo are either registered trademarks or trademarks of the Apache Software Foundation." |
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