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<!DOCTYPE html>
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<h1>Source code for torch.optim.adam</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="Adam"><a class="viewcode-back" href="../../../optim.html#torch.optim.Adam">[docs]</a><span class="k">class</span> <span class="nc">Adam</span><span class="p">(</span><span class="n">Optimizer</span><span class="p">):</span>
<span class="sd">"""Implements Adam algorithm.</span>
<span class="sd"> It has been proposed in `Adam: A Method for Stochastic Optimization`_.</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-3)</span>
<span class="sd"> betas (Tuple[float, float], optional): coefficients used for computing</span>
<span class="sd"> running averages of gradient and its square (default: (0.9, 0.999))</span>
<span class="sd"> eps (float, optional): term added to the denominator to improve</span>
<span class="sd"> numerical stability (default: 1e-8)</span>
<span class="sd"> weight_decay (float, optional): weight decay (L2 penalty) (default: 0)</span>
<span class="sd"> .. _Adam\: A Method for Stochastic Optimization:</span>
<span class="sd"> https://arxiv.org/abs/1412.6980</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">3</span><span class="p">,</span> <span class="n">betas</span><span class="o">=</span><span class="p">(</span><span class="mf">0.9</span><span class="p">,</span> <span class="mf">0.999</span><span class="p">),</span> <span class="n">eps</span><span class="o">=</span><span class="mi">1</span><span class="n">e</span><span class="o">-</span><span class="mi">8</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">betas</span><span class="o">=</span><span class="n">betas</span><span class="p">,</span> <span class="n">eps</span><span class="o">=</span><span class="n">eps</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">Adam</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="Adam.step"><a class="viewcode-back" href="../../../optim.html#torch.optim.Adam.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="c1"># Exponential moving average of gradient values</span>
<span class="n">state</span><span class="p">[</span><span class="s1">'exp_avg'</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="c1"># Exponential moving average of squared gradient values</span>
<span class="n">state</span><span class="p">[</span><span class="s1">'exp_avg_sq'</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">exp_avg</span><span class="p">,</span> <span class="n">exp_avg_sq</span> <span class="o">=</span> <span class="n">state</span><span class="p">[</span><span class="s1">'exp_avg'</span><span class="p">],</span> <span class="n">state</span><span class="p">[</span><span class="s1">'exp_avg_sq'</span><span class="p">]</span>
<span class="n">beta1</span><span class="p">,</span> <span class="n">beta2</span> <span class="o">=</span> <span class="n">group</span><span class="p">[</span><span class="s1">'betas'</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 the first and second moment running average coefficient</span>
<span class="n">exp_avg</span><span class="o">.</span><span class="n">mul_</span><span class="p">(</span><span class="n">beta1</span><span class="p">)</span><span class="o">.</span><span class="n">add_</span><span class="p">(</span><span class="mi">1</span> <span class="o">-</span> <span class="n">beta1</span><span class="p">,</span> <span class="n">grad</span><span class="p">)</span>
<span class="n">exp_avg_sq</span><span class="o">.</span><span class="n">mul_</span><span class="p">(</span><span class="n">beta2</span><span class="p">)</span><span class="o">.</span><span class="n">addcmul_</span><span class="p">(</span><span class="mi">1</span> <span class="o">-</span> <span class="n">beta2</span><span class="p">,</span> <span class="n">grad</span><span class="p">,</span> <span class="n">grad</span><span class="p">)</span>
<span class="n">denom</span> <span class="o">=</span> <span class="n">exp_avg_sq</span><span class="o">.</span><span class="n">sqrt</span><span class="p">()</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">'eps'</span><span class="p">])</span>
<span class="n">bias_correction1</span> <span class="o">=</span> <span class="mi">1</span> <span class="o">-</span> <span class="n">beta1</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">bias_correction2</span> <span class="o">=</span> <span class="mi">1</span> <span class="o">-</span> <span class="n">beta2</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">step_size</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">math</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="n">bias_correction2</span><span class="p">)</span> <span class="o">/</span> <span class="n">bias_correction1</span>
<span class="n">p</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">addcdiv_</span><span class="p">(</span><span class="o">-</span><span class="n">step_size</span><span class="p">,</span> <span class="n">exp_avg</span><span class="p">,</span> <span class="n">denom</span><span class="p">)</span>
<span class="k">return</span> <span class="n">loss</span></div></div>
</pre></div>
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