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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> |
| <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">rnn.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">rnn.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">rnn.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">rnn.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">rnn.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">rnn.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">rnn.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">rnn.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">rnn.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">rnn.Conv3DGRUCell</span></code></a></td> |
| <td>3D Convolutional Gated Rectified Unit (GRU) network cell.</td> |
| </tr> |
| </tbody> |
| </table> |
| </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.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 or tuple of int</em>) – Input convolution kernel sizes.</li> |
| <li><strong>h2h_kernel</strong> (<em>int or tuple of int</em>) – Recurrent convolution kernel sizes. Only odd-numbered sizes are supported.</li> |
| <li><strong>i2h_pad</strong> (<em>int or tuple of int, default (0,)</em>) – Pad for input convolution.</li> |
| <li><strong>i2h_dilate</strong> (<em>int or tuple of int, default (1,)</em>) – Input convolution dilate.</li> |
| <li><strong>h2h_dilate</strong> (<em>int or tuple of int, default (1,)</em>) – Recurrent convolution dilate.</li> |
| <li><strong>i2h_weight_initializer</strong> (<em>str or Initializer</em>) – Initializer for the input weights matrix, used for the input convolutions.</li> |
| <li><strong>h2h_weight_initializer</strong> (<em>str or Initializer</em>) – Initializer for the recurrent weights matrix, used for the input convolutions.</li> |
| <li><strong>i2h_bias_initializer</strong> (<em>str or Initializer, default zeros</em>) – Initializer for the input convolution bias vectors.</li> |
| <li><strong>h2h_bias_initializer</strong> (<em>str or Initializer, default zeros</em>) – Initializer for the recurrent convolution bias vectors.</li> |
| <li><strong>conv_layout</strong> (<em>str, default 'NCW'</em>) – Layout for all convolution inputs, outputs and weights. Options are ‘NCW’ and ‘NWC’.</li> |
| <li><strong>activation</strong> (<em>str or Block, 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="#id3"><span class="problematic" id="id4">conv_rnn_</span></a>‘) – Prefix for name of layers (and name of weight if params is None).</li> |
| <li><strong>params</strong> (<em>RNNParams, 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 or tuple of int</em>) – Input convolution kernel sizes.</li> |
| <li><strong>h2h_kernel</strong> (<em>int or tuple of int</em>) – Recurrent convolution kernel sizes. Only odd-numbered sizes are supported.</li> |
| <li><strong>i2h_pad</strong> (<em>int or tuple of int, default (0, 0)</em>) – Pad for input convolution.</li> |
| <li><strong>i2h_dilate</strong> (<em>int or tuple of int, default (1, 1)</em>) – Input convolution dilate.</li> |
| <li><strong>h2h_dilate</strong> (<em>int or tuple of int, default (1, 1)</em>) – Recurrent convolution dilate.</li> |
| <li><strong>i2h_weight_initializer</strong> (<em>str or Initializer</em>) – Initializer for the input weights matrix, used for the input convolutions.</li> |
| <li><strong>h2h_weight_initializer</strong> (<em>str or Initializer</em>) – Initializer for the recurrent weights matrix, used for the input convolutions.</li> |
| <li><strong>i2h_bias_initializer</strong> (<em>str or Initializer, default zeros</em>) – Initializer for the input convolution bias vectors.</li> |
| <li><strong>h2h_bias_initializer</strong> (<em>str or Initializer, default zeros</em>) – Initializer for the recurrent convolution bias vectors.</li> |
| <li><strong>conv_layout</strong> (<em>str, default 'NCHW'</em>) – Layout for all convolution inputs, outputs and weights. Options are ‘NCHW’ and ‘NHWC’.</li> |
| <li><strong>activation</strong> (<em>str or Block, 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="#id5"><span class="problematic" id="id6">conv_rnn_</span></a>‘) – Prefix for name of layers (and name of weight if params is None).</li> |
| <li><strong>params</strong> (<em>RNNParams, 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 or tuple of int</em>) – Input convolution kernel sizes.</li> |
| <li><strong>h2h_kernel</strong> (<em>int or tuple of int</em>) – Recurrent convolution kernel sizes. Only odd-numbered sizes are supported.</li> |
| <li><strong>i2h_pad</strong> (<em>int or tuple of int, default (0, 0, 0)</em>) – Pad for input convolution.</li> |
| <li><strong>i2h_dilate</strong> (<em>int or tuple of int, default (1, 1, 1)</em>) – Input convolution dilate.</li> |
| <li><strong>h2h_dilate</strong> (<em>int or tuple of int, default (1, 1, 1)</em>) – Recurrent convolution dilate.</li> |
| <li><strong>i2h_weight_initializer</strong> (<em>str or Initializer</em>) – Initializer for the input weights matrix, used for the input convolutions.</li> |
| <li><strong>h2h_weight_initializer</strong> (<em>str or Initializer</em>) – Initializer for the recurrent weights matrix, used for the input convolutions.</li> |
| <li><strong>i2h_bias_initializer</strong> (<em>str or Initializer, default zeros</em>) – Initializer for the input convolution bias vectors.</li> |
| <li><strong>h2h_bias_initializer</strong> (<em>str or Initializer, default zeros</em>) – Initializer for the recurrent convolution bias vectors.</li> |
| <li><strong>conv_layout</strong> (<em>str, default 'NCDHW'</em>) – Layout for all convolution inputs, outputs and weights. Options are ‘NCDHW’ and ‘NDHWC’.</li> |
| <li><strong>activation</strong> (<em>str or Block, 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="#id7"><span class="problematic" id="id8">conv_rnn_</span></a>‘) – Prefix for name of layers (and name of weight if params is None).</li> |
| <li><strong>params</strong> (<em>RNNParams, 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 or tuple of int</em>) – Input convolution kernel sizes.</li> |
| <li><strong>h2h_kernel</strong> (<em>int or tuple of int</em>) – Recurrent convolution kernel sizes. Only odd-numbered sizes are supported.</li> |
| <li><strong>i2h_pad</strong> (<em>int or tuple of int, default (0,)</em>) – Pad for input convolution.</li> |
| <li><strong>i2h_dilate</strong> (<em>int or tuple of int, default (1,)</em>) – Input convolution dilate.</li> |
| <li><strong>h2h_dilate</strong> (<em>int or tuple of int, default (1,)</em>) – Recurrent convolution dilate.</li> |
| <li><strong>i2h_weight_initializer</strong> (<em>str or Initializer</em>) – Initializer for the input weights matrix, used for the input convolutions.</li> |
| <li><strong>h2h_weight_initializer</strong> (<em>str or Initializer</em>) – Initializer for the recurrent weights matrix, used for the input convolutions.</li> |
| <li><strong>i2h_bias_initializer</strong> (<em>str or Initializer, default zeros</em>) – Initializer for the input convolution bias vectors.</li> |
| <li><strong>h2h_bias_initializer</strong> (<em>str or Initializer, default zeros</em>) – Initializer for the recurrent convolution bias vectors.</li> |
| <li><strong>conv_layout</strong> (<em>str, default 'NCW'</em>) – Layout for all convolution inputs, outputs and weights. Options are ‘NCW’ and ‘NWC’.</li> |
| <li><strong>activation</strong> (<em>str or Block, 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="#id9"><span class="problematic" id="id10">conv_lstm_</span></a>‘) – Prefix for name of layers (and name of weight if params is None).</li> |
| <li><strong>params</strong> (<em>RNNParams, 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 or tuple of int</em>) – Input convolution kernel sizes.</li> |
| <li><strong>h2h_kernel</strong> (<em>int or tuple of int</em>) – Recurrent convolution kernel sizes. Only odd-numbered sizes are supported.</li> |
| <li><strong>i2h_pad</strong> (<em>int or tuple of int, default (0, 0)</em>) – Pad for input convolution.</li> |
| <li><strong>i2h_dilate</strong> (<em>int or tuple of int, default (1, 1)</em>) – Input convolution dilate.</li> |
| <li><strong>h2h_dilate</strong> (<em>int or tuple of int, default (1, 1)</em>) – Recurrent convolution dilate.</li> |
| <li><strong>i2h_weight_initializer</strong> (<em>str or Initializer</em>) – Initializer for the input weights matrix, used for the input convolutions.</li> |
| <li><strong>h2h_weight_initializer</strong> (<em>str or Initializer</em>) – Initializer for the recurrent weights matrix, used for the input convolutions.</li> |
| <li><strong>i2h_bias_initializer</strong> (<em>str or Initializer, default zeros</em>) – Initializer for the input convolution bias vectors.</li> |
| <li><strong>h2h_bias_initializer</strong> (<em>str or Initializer, default zeros</em>) – Initializer for the recurrent convolution bias vectors.</li> |
| <li><strong>conv_layout</strong> (<em>str, default 'NCHW'</em>) – Layout for all convolution inputs, outputs and weights. Options are ‘NCHW’ and ‘NHWC’.</li> |
| <li><strong>activation</strong> (<em>str or Block, 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="#id11"><span class="problematic" id="id12">conv_lstm_</span></a>‘) – Prefix for name of layers (and name of weight if params is None).</li> |
| <li><strong>params</strong> (<em>RNNParams, 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 or tuple of int</em>) – Input convolution kernel sizes.</li> |
| <li><strong>h2h_kernel</strong> (<em>int or tuple of int</em>) – Recurrent convolution kernel sizes. Only odd-numbered sizes are supported.</li> |
| <li><strong>i2h_pad</strong> (<em>int or tuple of int, default (0, 0, 0)</em>) – Pad for input convolution.</li> |
| <li><strong>i2h_dilate</strong> (<em>int or tuple of int, default (1, 1, 1)</em>) – Input convolution dilate.</li> |
| <li><strong>h2h_dilate</strong> (<em>int or tuple of int, default (1, 1, 1)</em>) – Recurrent convolution dilate.</li> |
| <li><strong>i2h_weight_initializer</strong> (<em>str or Initializer</em>) – Initializer for the input weights matrix, used for the input convolutions.</li> |
| <li><strong>h2h_weight_initializer</strong> (<em>str or Initializer</em>) – Initializer for the recurrent weights matrix, used for the input convolutions.</li> |
| <li><strong>i2h_bias_initializer</strong> (<em>str or Initializer, default zeros</em>) – Initializer for the input convolution bias vectors.</li> |
| <li><strong>h2h_bias_initializer</strong> (<em>str or Initializer, default zeros</em>) – Initializer for the recurrent convolution bias vectors.</li> |
| <li><strong>conv_layout</strong> (<em>str, default 'NCDHW'</em>) – Layout for all convolution inputs, outputs and weights. Options are ‘NCDHW’ and ‘NDHWC’.</li> |
| <li><strong>activation</strong> (<em>str or Block, 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="#id13"><span class="problematic" id="id14">conv_lstm_</span></a>‘) – Prefix for name of layers (and name of weight if params is None).</li> |
| <li><strong>params</strong> (<em>RNNParams, 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 or tuple of int</em>) – Input convolution kernel sizes.</li> |
| <li><strong>h2h_kernel</strong> (<em>int or tuple of int</em>) – Recurrent convolution kernel sizes. Only odd-numbered sizes are supported.</li> |
| <li><strong>i2h_pad</strong> (<em>int or tuple of int, default (0,)</em>) – Pad for input convolution.</li> |
| <li><strong>i2h_dilate</strong> (<em>int or tuple of int, default (1,)</em>) – Input convolution dilate.</li> |
| <li><strong>h2h_dilate</strong> (<em>int or tuple of int, default (1,)</em>) – Recurrent convolution dilate.</li> |
| <li><strong>i2h_weight_initializer</strong> (<em>str or Initializer</em>) – Initializer for the input weights matrix, used for the input convolutions.</li> |
| <li><strong>h2h_weight_initializer</strong> (<em>str or Initializer</em>) – Initializer for the recurrent weights matrix, used for the input convolutions.</li> |
| <li><strong>i2h_bias_initializer</strong> (<em>str or Initializer, default zeros</em>) – Initializer for the input convolution bias vectors.</li> |
| <li><strong>h2h_bias_initializer</strong> (<em>str or Initializer, default zeros</em>) – Initializer for the recurrent convolution bias vectors.</li> |
| <li><strong>conv_layout</strong> (<em>str, default 'NCW'</em>) – Layout for all convolution inputs, outputs and weights. Options are ‘NCW’ and ‘NWC’.</li> |
| <li><strong>activation</strong> (<em>str or Block, 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="#id15"><span class="problematic" id="id16">conv_gru_</span></a>‘) – Prefix for name of layers (and name of weight if params is None).</li> |
| <li><strong>params</strong> (<em>RNNParams, 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 or tuple of int</em>) – Input convolution kernel sizes.</li> |
| <li><strong>h2h_kernel</strong> (<em>int or tuple of int</em>) – Recurrent convolution kernel sizes. Only odd-numbered sizes are supported.</li> |
| <li><strong>i2h_pad</strong> (<em>int or tuple of int, default (0, 0)</em>) – Pad for input convolution.</li> |
| <li><strong>i2h_dilate</strong> (<em>int or tuple of int, default (1, 1)</em>) – Input convolution dilate.</li> |
| <li><strong>h2h_dilate</strong> (<em>int or tuple of int, default (1, 1)</em>) – Recurrent convolution dilate.</li> |
| <li><strong>i2h_weight_initializer</strong> (<em>str or Initializer</em>) – Initializer for the input weights matrix, used for the input convolutions.</li> |
| <li><strong>h2h_weight_initializer</strong> (<em>str or Initializer</em>) – Initializer for the recurrent weights matrix, used for the input convolutions.</li> |
| <li><strong>i2h_bias_initializer</strong> (<em>str or Initializer, default zeros</em>) – Initializer for the input convolution bias vectors.</li> |
| <li><strong>h2h_bias_initializer</strong> (<em>str or Initializer, default zeros</em>) – Initializer for the recurrent convolution bias vectors.</li> |
| <li><strong>conv_layout</strong> (<em>str, default 'NCHW'</em>) – Layout for all convolution inputs, outputs and weights. Options are ‘NCHW’ and ‘NHWC’.</li> |
| <li><strong>activation</strong> (<em>str or Block, 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="#id17"><span class="problematic" id="id18">conv_gru_</span></a>‘) – Prefix for name of layers (and name of weight if params is None).</li> |
| <li><strong>params</strong> (<em>RNNParams, 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 or tuple of int</em>) – Input convolution kernel sizes.</li> |
| <li><strong>h2h_kernel</strong> (<em>int or tuple of int</em>) – Recurrent convolution kernel sizes. Only odd-numbered sizes are supported.</li> |
| <li><strong>i2h_pad</strong> (<em>int or tuple of int, default (0, 0, 0)</em>) – Pad for input convolution.</li> |
| <li><strong>i2h_dilate</strong> (<em>int or tuple of int, default (1, 1, 1)</em>) – Input convolution dilate.</li> |
| <li><strong>h2h_dilate</strong> (<em>int or tuple of int, default (1, 1, 1)</em>) – Recurrent convolution dilate.</li> |
| <li><strong>i2h_weight_initializer</strong> (<em>str or Initializer</em>) – Initializer for the input weights matrix, used for the input convolutions.</li> |
| <li><strong>h2h_weight_initializer</strong> (<em>str or Initializer</em>) – Initializer for the recurrent weights matrix, used for the input convolutions.</li> |
| <li><strong>i2h_bias_initializer</strong> (<em>str or Initializer, default zeros</em>) – Initializer for the input convolution bias vectors.</li> |
| <li><strong>h2h_bias_initializer</strong> (<em>str or Initializer, default zeros</em>) – Initializer for the recurrent convolution bias vectors.</li> |
| <li><strong>conv_layout</strong> (<em>str, default 'NCDHW'</em>) – Layout for all convolution inputs, outputs and weights. Options are ‘NCDHW’ and ‘NDHWC’.</li> |
| <li><strong>activation</strong> (<em>str or Block, 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="#id19"><span class="problematic" id="id20">conv_gru_</span></a>‘) – Prefix for name of layers (and name of weight if params is None).</li> |
| <li><strong>params</strong> (<em>RNNParams, 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, default 0.</em>) – The dropout rate for inputs. Won’t apply dropout if it equals 0.</li> |
| <li><strong>drop_states</strong> (<em>float, 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, 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><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> (<em>Symbol, list of Symbol, or 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, 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, optional</em>) – <cite>layout</cite> of input symbol. Only used if inputs |
| is a single Symbol.</li> |
| <li><strong>merge_outputs</strong> (<em>bool, 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> |
| </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> |
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