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<!DOCTYPE html>
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<h1>Source code for torch.optim.asgd</h1><div class="highlight"><pre>
<span></span><span class="kn">import</span> <span class="nn">math</span>
<span class="kn">from</span> <span class="nn">.optimizer</span> <span class="k">import</span> <span class="n">Optimizer</span>
<div class="viewcode-block" id="ASGD"><a class="viewcode-back" href="../../../optim.html#torch.optim.ASGD">[docs]</a><span class="k">class</span> <span class="nc">ASGD</span><span class="p">(</span><span class="n">Optimizer</span><span class="p">):</span>
<span class="sd">"""Implements Averaged Stochastic Gradient Descent.</span>
<span class="sd"> It has been proposed in `Acceleration of stochastic approximation by averaging`_.</span>
<span class="sd"> Arguments:</span>
<span class="sd"> params (iterable): iterable of parameters to optimize or dicts defining</span>
<span class="sd"> parameter groups</span>
<span class="sd"> lr (float, optional): learning rate (default: 1e-2)</span>
<span class="sd"> lambd (float, optional): decay term (default: 1e-4)</span>
<span class="sd"> alpha (float, optional): power for eta update (default: 0.75)</span>
<span class="sd"> t0 (float, optional): point at which to start averaging (default: 1e6)</span>
<span class="sd"> weight_decay (float, optional): weight decay (L2 penalty) (default: 0)</span>
<span class="sd"> .. _Acceleration of stochastic approximation by averaging:</span>
<span class="sd"> http://dl.acm.org/citation.cfm?id=131098</span>
<span class="sd"> """</span>
<span class="k">def</span> <span class="nf">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">params</span><span class="p">,</span> <span class="n">lr</span><span class="o">=</span><span class="mi">1</span><span class="n">e</span><span class="o">-</span><span class="mi">2</span><span class="p">,</span> <span class="n">lambd</span><span class="o">=</span><span class="mi">1</span><span class="n">e</span><span class="o">-</span><span class="mi">4</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.75</span><span class="p">,</span> <span class="n">t0</span><span class="o">=</span><span class="mi">1</span><span class="n">e6</span><span class="p">,</span> <span class="n">weight_decay</span><span class="o">=</span><span class="mi">0</span><span class="p">):</span>
<span class="n">defaults</span> <span class="o">=</span> <span class="nb">dict</span><span class="p">(</span><span class="n">lr</span><span class="o">=</span><span class="n">lr</span><span class="p">,</span> <span class="n">lambd</span><span class="o">=</span><span class="n">lambd</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="n">alpha</span><span class="p">,</span> <span class="n">t0</span><span class="o">=</span><span class="n">t0</span><span class="p">,</span>
<span class="n">weight_decay</span><span class="o">=</span><span class="n">weight_decay</span><span class="p">)</span>
<span class="nb">super</span><span class="p">(</span><span class="n">ASGD</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="n">__init__</span><span class="p">(</span><span class="n">params</span><span class="p">,</span> <span class="n">defaults</span><span class="p">)</span>
<div class="viewcode-block" id="ASGD.step"><a class="viewcode-back" href="../../../optim.html#torch.optim.ASGD.step">[docs]</a> <span class="k">def</span> <span class="nf">step</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">closure</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
<span class="sd">"""Performs a single optimization step.</span>
<span class="sd"> Arguments:</span>
<span class="sd"> closure (callable, optional): A closure that reevaluates the model</span>
<span class="sd"> and returns the loss.</span>
<span class="sd"> """</span>
<span class="n">loss</span> <span class="o">=</span> <span class="kc">None</span>
<span class="k">if</span> <span class="n">closure</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
<span class="n">loss</span> <span class="o">=</span> <span class="n">closure</span><span class="p">()</span>
<span class="k">for</span> <span class="n">group</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">param_groups</span><span class="p">:</span>
<span class="k">for</span> <span class="n">p</span> <span class="ow">in</span> <span class="n">group</span><span class="p">[</span><span class="s1">'params'</span><span class="p">]:</span>
<span class="k">if</span> <span class="n">p</span><span class="o">.</span><span class="n">grad</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
<span class="k">continue</span>
<span class="n">grad</span> <span class="o">=</span> <span class="n">p</span><span class="o">.</span><span class="n">grad</span><span class="o">.</span><span class="n">data</span>
<span class="n">state</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">state</span><span class="p">[</span><span class="n">p</span><span class="p">]</span>
<span class="c1"># State initialization</span>
<span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">state</span><span class="p">)</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
<span class="n">state</span><span class="p">[</span><span class="s1">'step'</span><span class="p">]</span> <span class="o">=</span> <span class="mi">0</span>
<span class="n">state</span><span class="p">[</span><span class="s1">'eta'</span><span class="p">]</span> <span class="o">=</span> <span class="n">group</span><span class="p">[</span><span class="s1">'lr'</span><span class="p">]</span>
<span class="n">state</span><span class="p">[</span><span class="s1">'mu'</span><span class="p">]</span> <span class="o">=</span> <span class="mi">1</span>
<span class="n">state</span><span class="p">[</span><span class="s1">'ax'</span><span class="p">]</span> <span class="o">=</span> <span class="n">grad</span><span class="o">.</span><span class="n">new</span><span class="p">()</span><span class="o">.</span><span class="n">resize_as_</span><span class="p">(</span><span class="n">grad</span><span class="p">)</span><span class="o">.</span><span class="n">zero_</span><span class="p">()</span>
<span class="n">state</span><span class="p">[</span><span class="s1">'step'</span><span class="p">]</span> <span class="o">+=</span> <span class="mi">1</span>
<span class="k">if</span> <span class="n">group</span><span class="p">[</span><span class="s1">'weight_decay'</span><span class="p">]</span> <span class="o">!=</span> <span class="mi">0</span><span class="p">:</span>
<span class="n">grad</span> <span class="o">=</span> <span class="n">grad</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">group</span><span class="p">[</span><span class="s1">'weight_decay'</span><span class="p">],</span> <span class="n">p</span><span class="o">.</span><span class="n">data</span><span class="p">)</span>
<span class="c1"># decay term</span>
<span class="n">p</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">mul_</span><span class="p">(</span><span class="mi">1</span> <span class="o">-</span> <span class="n">group</span><span class="p">[</span><span class="s1">'lambd'</span><span class="p">]</span> <span class="o">*</span> <span class="n">state</span><span class="p">[</span><span class="s1">'eta'</span><span class="p">])</span>
<span class="c1"># update parameter</span>
<span class="n">p</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">add_</span><span class="p">(</span><span class="o">-</span><span class="n">state</span><span class="p">[</span><span class="s1">'eta'</span><span class="p">],</span> <span class="n">grad</span><span class="p">)</span>
<span class="c1"># averaging</span>
<span class="k">if</span> <span class="n">state</span><span class="p">[</span><span class="s1">'mu'</span><span class="p">]</span> <span class="o">!=</span> <span class="mi">1</span><span class="p">:</span>
<span class="n">state</span><span class="p">[</span><span class="s1">'ax'</span><span class="p">]</span><span class="o">.</span><span class="n">add_</span><span class="p">(</span><span class="n">p</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">sub</span><span class="p">(</span><span class="n">state</span><span class="p">[</span><span class="s1">'ax'</span><span class="p">])</span><span class="o">.</span><span class="n">mul</span><span class="p">(</span><span class="n">state</span><span class="p">[</span><span class="s1">'mu'</span><span class="p">]))</span>
<span class="k">else</span><span class="p">:</span>
<span class="n">state</span><span class="p">[</span><span class="s1">'ax'</span><span class="p">]</span><span class="o">.</span><span class="n">copy_</span><span class="p">(</span><span class="n">p</span><span class="o">.</span><span class="n">data</span><span class="p">)</span>
<span class="c1"># update eta and mu</span>
<span class="n">state</span><span class="p">[</span><span class="s1">'eta'</span><span class="p">]</span> <span class="o">=</span> <span class="p">(</span><span class="n">group</span><span class="p">[</span><span class="s1">'lr'</span><span class="p">]</span> <span class="o">/</span>
<span class="n">math</span><span class="o">.</span><span class="n">pow</span><span class="p">((</span><span class="mi">1</span> <span class="o">+</span> <span class="n">group</span><span class="p">[</span><span class="s1">'lambd'</span><span class="p">]</span> <span class="o">*</span> <span class="n">group</span><span class="p">[</span><span class="s1">'lr'</span><span class="p">]</span> <span class="o">*</span> <span class="n">state</span><span class="p">[</span><span class="s1">'step'</span><span class="p">]),</span> <span class="n">group</span><span class="p">[</span><span class="s1">'alpha'</span><span class="p">]))</span>
<span class="n">state</span><span class="p">[</span><span class="s1">'mu'</span><span class="p">]</span> <span class="o">=</span> <span class="mi">1</span> <span class="o">/</span> <span class="nb">max</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="n">state</span><span class="p">[</span><span class="s1">'step'</span><span class="p">]</span> <span class="o">-</span> <span class="n">group</span><span class="p">[</span><span class="s1">'t0'</span><span class="p">])</span>
<span class="k">return</span> <span class="n">loss</span></div></div>
</pre></div>
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