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<h1>Source code for mxnet.gluon.contrib.rnn.rnn_cell</h1><div class="highlight"><pre>
<span></span><span class="c1"># Licensed to the Apache Software Foundation (ASF) under one</span>
<span class="c1"># or more contributor license agreements. See the NOTICE file</span>
<span class="c1"># distributed with this work for additional information</span>
<span class="c1"># regarding copyright ownership. The ASF licenses this file</span>
<span class="c1"># to you under the Apache License, Version 2.0 (the</span>
<span class="c1"># "License"); you may not use this file except in compliance</span>
<span class="c1"># with the License. You may obtain a copy of the License at</span>
<span class="c1">#</span>
<span class="c1"># http://www.apache.org/licenses/LICENSE-2.0</span>
<span class="c1">#</span>
<span class="c1"># Unless required by applicable law or agreed to in writing,</span>
<span class="c1"># software distributed under the License is distributed on an</span>
<span class="c1"># "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY</span>
<span class="c1"># KIND, either express or implied. See the License for the</span>
<span class="c1"># specific language governing permissions and limitations</span>
<span class="c1"># under the License.</span>
<span class="c1"># coding: utf-8</span>
<span class="sd">"""Definition of various recurrent neural network cells."""</span>
<span class="n">__all__</span> <span class="o">=</span> <span class="p">[</span><span class="s1">'VariationalDropoutCell'</span><span class="p">]</span>
<span class="kn">from</span> <span class="nn">...rnn</span> <span class="kn">import</span> <span class="n">BidirectionalCell</span><span class="p">,</span> <span class="n">SequentialRNNCell</span><span class="p">,</span> <span class="n">ModifierCell</span>
<span class="kn">from</span> <span class="nn">...rnn.rnn_cell</span> <span class="kn">import</span> <span class="n">_format_sequence</span><span class="p">,</span> <span class="n">_get_begin_state</span>
<div class="viewcode-block" id="VariationalDropoutCell"><a class="viewcode-back" href="../../../../../api/python/gluon/contrib.html#mxnet.gluon.contrib.rnn.VariationalDropoutCell">[docs]</a><span class="k">class</span> <span class="nc">VariationalDropoutCell</span><span class="p">(</span><span class="n">ModifierCell</span><span class="p">):</span>
<span class="sd">"""</span>
<span class="sd"> Applies Variational Dropout on base cell.</span>
<span class="sd"> (https://arxiv.org/pdf/1512.05287.pdf,</span>
<span class="sd"> https://www.stat.berkeley.edu/~tsmoon/files/Conference/asru2015.pdf).</span>
<span class="sd"> Variational dropout uses the same dropout mask across time-steps. It can be applied to RNN</span>
<span class="sd"> inputs, outputs, and states. The masks for them are not shared.</span>
<span class="sd"> The dropout mask is initialized when stepping forward for the first time and will remain</span>
<span class="sd"> the same until .reset() is called. Thus, if using the cell and stepping manually without calling</span>
<span class="sd"> .unroll(), the .reset() should be called after each sequence.</span>
<span class="sd"> Parameters</span>
<span class="sd"> ----------</span>
<span class="sd"> base_cell : RecurrentCell</span>
<span class="sd"> The cell on which to perform variational dropout.</span>
<span class="sd"> drop_inputs : float, default 0.</span>
<span class="sd"> The dropout rate for inputs. Won't apply dropout if it equals 0.</span>
<span class="sd"> drop_states : float, default 0.</span>
<span class="sd"> The dropout rate for state inputs on the first state channel.</span>
<span class="sd"> Won't apply dropout if it equals 0.</span>
<span class="sd"> drop_outputs : float, default 0.</span>
<span class="sd"> The dropout rate for outputs. Won't apply dropout if it equals 0.</span>
<span class="sd"> """</span>
<span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">base_cell</span><span class="p">,</span> <span class="n">drop_inputs</span><span class="o">=</span><span class="mf">0.</span><span class="p">,</span> <span class="n">drop_states</span><span class="o">=</span><span class="mf">0.</span><span class="p">,</span> <span class="n">drop_outputs</span><span class="o">=</span><span class="mf">0.</span><span class="p">):</span>
<span class="k">assert</span> <span class="ow">not</span> <span class="n">drop_states</span> <span class="ow">or</span> <span class="ow">not</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">base_cell</span><span class="p">,</span> <span class="n">BidirectionalCell</span><span class="p">),</span> \
<span class="s2">"BidirectionalCell doesn't support variational state dropout. "</span> \
<span class="s2">"Please add VariationalDropoutCell to the cells underneath instead."</span>
<span class="k">assert</span> <span class="ow">not</span> <span class="n">drop_states</span> \
<span class="ow">or</span> <span class="ow">not</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">base_cell</span><span class="p">,</span> <span class="n">SequentialRNNCell</span><span class="p">)</span> <span class="ow">or</span> <span class="ow">not</span> <span class="n">base_cell</span><span class="o">.</span><span class="n">_bidirectional</span><span class="p">,</span> \
<span class="s2">"Bidirectional SequentialRNNCell doesn't support variational state dropout. "</span> \
<span class="s2">"Please add VariationalDropoutCell to the cells underneath instead."</span>
<span class="nb">super</span><span class="p">(</span><span class="n">VariationalDropoutCell</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="n">base_cell</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">drop_inputs</span> <span class="o">=</span> <span class="n">drop_inputs</span>
<span class="bp">self</span><span class="o">.</span><span class="n">drop_states</span> <span class="o">=</span> <span class="n">drop_states</span>
<span class="bp">self</span><span class="o">.</span><span class="n">drop_outputs</span> <span class="o">=</span> <span class="n">drop_outputs</span>
<span class="bp">self</span><span class="o">.</span><span class="n">drop_inputs_mask</span> <span class="o">=</span> <span class="bp">None</span>
<span class="bp">self</span><span class="o">.</span><span class="n">drop_states_mask</span> <span class="o">=</span> <span class="bp">None</span>
<span class="bp">self</span><span class="o">.</span><span class="n">drop_outputs_mask</span> <span class="o">=</span> <span class="bp">None</span>
<span class="k">def</span> <span class="nf">_alias</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="k">return</span> <span class="s1">'vardrop'</span>
<span class="k">def</span> <span class="nf">reset</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="nb">super</span><span class="p">(</span><span class="n">VariationalDropoutCell</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="n">reset</span><span class="p">()</span>
<span class="bp">self</span><span class="o">.</span><span class="n">drop_inputs_mask</span> <span class="o">=</span> <span class="bp">None</span>
<span class="bp">self</span><span class="o">.</span><span class="n">drop_states_mask</span> <span class="o">=</span> <span class="bp">None</span>
<span class="bp">self</span><span class="o">.</span><span class="n">drop_outputs_mask</span> <span class="o">=</span> <span class="bp">None</span>
<span class="k">def</span> <span class="nf">_initialize_input_masks</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">F</span><span class="p">,</span> <span class="n">inputs</span><span class="p">,</span> <span class="n">states</span><span class="p">):</span>
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">drop_states</span> <span class="ow">and</span> <span class="bp">self</span><span class="o">.</span><span class="n">drop_states_mask</span> <span class="ow">is</span> <span class="bp">None</span><span class="p">:</span>
<span class="bp">self</span><span class="o">.</span><span class="n">drop_states_mask</span> <span class="o">=</span> <span class="n">F</span><span class="o">.</span><span class="n">Dropout</span><span class="p">(</span><span class="n">F</span><span class="o">.</span><span class="n">ones_like</span><span class="p">(</span><span class="n">states</span><span class="p">[</span><span class="mi">0</span><span class="p">]),</span>
<span class="n">p</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">drop_states</span><span class="p">)</span>
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">drop_inputs</span> <span class="ow">and</span> <span class="bp">self</span><span class="o">.</span><span class="n">drop_inputs_mask</span> <span class="ow">is</span> <span class="bp">None</span><span class="p">:</span>
<span class="bp">self</span><span class="o">.</span><span class="n">drop_inputs_mask</span> <span class="o">=</span> <span class="n">F</span><span class="o">.</span><span class="n">Dropout</span><span class="p">(</span><span class="n">F</span><span class="o">.</span><span class="n">ones_like</span><span class="p">(</span><span class="n">inputs</span><span class="p">),</span>
<span class="n">p</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">drop_inputs</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">_initialize_output_mask</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">F</span><span class="p">,</span> <span class="n">output</span><span class="p">):</span>
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">drop_outputs</span> <span class="ow">and</span> <span class="bp">self</span><span class="o">.</span><span class="n">drop_outputs_mask</span> <span class="ow">is</span> <span class="bp">None</span><span class="p">:</span>
<span class="bp">self</span><span class="o">.</span><span class="n">drop_outputs_mask</span> <span class="o">=</span> <span class="n">F</span><span class="o">.</span><span class="n">Dropout</span><span class="p">(</span><span class="n">F</span><span class="o">.</span><span class="n">ones_like</span><span class="p">(</span><span class="n">output</span><span class="p">),</span>
<span class="n">p</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">drop_outputs</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">hybrid_forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">F</span><span class="p">,</span> <span class="n">inputs</span><span class="p">,</span> <span class="n">states</span><span class="p">):</span>
<span class="n">cell</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">base_cell</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_initialize_input_masks</span><span class="p">(</span><span class="n">F</span><span class="p">,</span> <span class="n">inputs</span><span class="p">,</span> <span class="n">states</span><span class="p">)</span>
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">drop_states</span><span class="p">:</span>
<span class="n">states</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="n">states</span><span class="p">)</span>
<span class="c1"># state dropout only needs to be applied on h, which is always the first state.</span>
<span class="n">states</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">=</span> <span class="n">states</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">drop_states_mask</span>
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">drop_inputs</span><span class="p">:</span>
<span class="n">inputs</span> <span class="o">=</span> <span class="n">inputs</span> <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">drop_inputs_mask</span>
<span class="n">next_output</span><span class="p">,</span> <span class="n">next_states</span> <span class="o">=</span> <span class="n">cell</span><span class="p">(</span><span class="n">inputs</span><span class="p">,</span> <span class="n">states</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_initialize_output_mask</span><span class="p">(</span><span class="n">F</span><span class="p">,</span> <span class="n">next_output</span><span class="p">)</span>
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">drop_outputs</span><span class="p">:</span>
<span class="n">next_output</span> <span class="o">=</span> <span class="n">next_output</span> <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">drop_outputs_mask</span>
<span class="k">return</span> <span class="n">next_output</span><span class="p">,</span> <span class="n">next_states</span>
<span class="k">def</span> <span class="fm">__repr__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="n">s</span> <span class="o">=</span> <span class="s1">'{name}(p_out = {drop_outputs}, p_state = {drop_states})'</span>
<span class="k">return</span> <span class="n">s</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">name</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="vm">__class__</span><span class="o">.</span><span class="vm">__name__</span><span class="p">,</span>
<span class="o">**</span><span class="bp">self</span><span class="o">.</span><span class="vm">__dict__</span><span class="p">)</span>
<div class="viewcode-block" id="VariationalDropoutCell.unroll"><a class="viewcode-back" href="../../../../../api/python/gluon/contrib.html#mxnet.gluon.contrib.rnn.VariationalDropoutCell.unroll">[docs]</a> <span class="k">def</span> <span class="nf">unroll</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">length</span><span class="p">,</span> <span class="n">inputs</span><span class="p">,</span> <span class="n">begin_state</span><span class="o">=</span><span class="bp">None</span><span class="p">,</span> <span class="n">layout</span><span class="o">=</span><span class="s1">'NTC'</span><span class="p">,</span> <span class="n">merge_outputs</span><span class="o">=</span><span class="bp">None</span><span class="p">):</span>
<span class="sd">"""Unrolls an RNN cell across time steps.</span>
<span class="sd"> Parameters</span>
<span class="sd"> ----------</span>
<span class="sd"> length : int</span>
<span class="sd"> Number of steps to unroll.</span>
<span class="sd"> inputs : Symbol, list of Symbol, or None</span>
<span class="sd"> If `inputs` is a single Symbol (usually the output</span>
<span class="sd"> of Embedding symbol), it should have shape</span>
<span class="sd"> (batch_size, length, ...) if `layout` is 'NTC',</span>
<span class="sd"> or (length, batch_size, ...) if `layout` is 'TNC'.</span>
<span class="sd"> If `inputs` is a list of symbols (usually output of</span>
<span class="sd"> previous unroll), they should all have shape</span>
<span class="sd"> (batch_size, ...).</span>
<span class="sd"> begin_state : nested list of Symbol, optional</span>
<span class="sd"> Input states created by `begin_state()`</span>
<span class="sd"> or output state of another cell.</span>
<span class="sd"> Created from `begin_state()` if `None`.</span>
<span class="sd"> layout : str, optional</span>
<span class="sd"> `layout` of input symbol. Only used if inputs</span>
<span class="sd"> is a single Symbol.</span>
<span class="sd"> merge_outputs : bool, optional</span>
<span class="sd"> If `False`, returns outputs as a list of Symbols.</span>
<span class="sd"> If `True`, concatenates output across time steps</span>
<span class="sd"> and returns a single symbol with shape</span>
<span class="sd"> (batch_size, length, ...) if layout is 'NTC',</span>
<span class="sd"> or (length, batch_size, ...) if layout is 'TNC'.</span>
<span class="sd"> If `None`, output whatever is faster.</span>
<span class="sd"> Returns</span>
<span class="sd"> -------</span>
<span class="sd"> outputs : list of Symbol or Symbol</span>
<span class="sd"> Symbol (if `merge_outputs` is True) or list of Symbols</span>
<span class="sd"> (if `merge_outputs` is False) corresponding to the output from</span>
<span class="sd"> the RNN from this unrolling.</span>
<span class="sd"> states : list of Symbol</span>
<span class="sd"> The new state of this RNN after this unrolling.</span>
<span class="sd"> The type of this symbol is same as the output of `begin_state()`.</span>
<span class="sd"> """</span>
<span class="c1"># Dropout on inputs and outputs can be performed on the whole sequence</span>
<span class="c1"># only when state dropout is not present.</span>
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">drop_states</span><span class="p">:</span>
<span class="k">return</span> <span class="nb">super</span><span class="p">(</span><span class="n">VariationalDropoutCell</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="n">unroll</span><span class="p">(</span><span class="n">length</span><span class="p">,</span> <span class="n">inputs</span><span class="p">,</span> <span class="n">begin_state</span><span class="p">,</span>
<span class="n">layout</span><span class="p">,</span> <span class="n">merge_outputs</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">reset</span><span class="p">()</span>
<span class="n">inputs</span><span class="p">,</span> <span class="n">axis</span><span class="p">,</span> <span class="n">F</span><span class="p">,</span> <span class="n">batch_size</span> <span class="o">=</span> <span class="n">_format_sequence</span><span class="p">(</span><span class="n">length</span><span class="p">,</span> <span class="n">inputs</span><span class="p">,</span> <span class="n">layout</span><span class="p">,</span> <span class="bp">True</span><span class="p">)</span>
<span class="n">states</span> <span class="o">=</span> <span class="n">_get_begin_state</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">F</span><span class="p">,</span> <span class="n">begin_state</span><span class="p">,</span> <span class="n">inputs</span><span class="p">,</span> <span class="n">batch_size</span><span class="p">)</span>
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">drop_inputs</span><span class="p">:</span>
<span class="n">first_input</span> <span class="o">=</span> <span class="n">inputs</span><span class="o">.</span><span class="n">slice_axis</span><span class="p">(</span><span class="n">axis</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="n">axis</span><span class="p">,</span> <span class="n">squeeze_axis</span><span class="o">=</span><span class="bp">True</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_initialize_input_masks</span><span class="p">(</span><span class="n">F</span><span class="p">,</span> <span class="n">first_input</span><span class="p">,</span> <span class="n">states</span><span class="p">)</span>
<span class="n">inputs</span> <span class="o">=</span> <span class="n">F</span><span class="o">.</span><span class="n">broadcast_mul</span><span class="p">(</span><span class="n">inputs</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">drop_inputs_mask</span><span class="o">.</span><span class="n">expand_dims</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="n">axis</span><span class="p">))</span>
<span class="n">outputs</span><span class="p">,</span> <span class="n">states</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">base_cell</span><span class="o">.</span><span class="n">unroll</span><span class="p">(</span><span class="n">length</span><span class="p">,</span> <span class="n">inputs</span><span class="p">,</span> <span class="n">states</span><span class="p">,</span> <span class="n">layout</span><span class="p">,</span> <span class="n">merge_outputs</span><span class="o">=</span><span class="bp">True</span><span class="p">)</span>
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">drop_outputs</span><span class="p">:</span>
<span class="n">first_output</span> <span class="o">=</span> <span class="n">outputs</span><span class="o">.</span><span class="n">slice_axis</span><span class="p">(</span><span class="n">axis</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="n">axis</span><span class="p">,</span> <span class="n">squeeze_axis</span><span class="o">=</span><span class="bp">True</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_initialize_output_mask</span><span class="p">(</span><span class="n">F</span><span class="p">,</span> <span class="n">first_output</span><span class="p">)</span>
<span class="n">outputs</span> <span class="o">=</span> <span class="n">F</span><span class="o">.</span><span class="n">broadcast_mul</span><span class="p">(</span><span class="n">outputs</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">drop_outputs_mask</span><span class="o">.</span><span class="n">expand_dims</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="n">axis</span><span class="p">))</span>
<span class="n">outputs</span><span class="p">,</span> <span class="n">_</span><span class="p">,</span> <span class="n">_</span><span class="p">,</span> <span class="n">_</span> <span class="o">=</span> <span class="n">_format_sequence</span><span class="p">(</span><span class="n">length</span><span class="p">,</span> <span class="n">outputs</span><span class="p">,</span> <span class="n">layout</span><span class="p">,</span> <span class="n">merge_outputs</span><span class="p">)</span>
<span class="k">return</span> <span class="n">outputs</span><span class="p">,</span> <span class="n">states</span></div></div>
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