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<h1>Source code for torch.distributions.bernoulli</h1><div class="highlight"><pre>
<span></span><span class="kn">from</span> <span class="nn">numbers</span> <span class="kn">import</span> <span class="n">Number</span>
<span class="kn">import</span> <span class="nn">torch</span>
<span class="kn">from</span> <span class="nn">torch</span> <span class="kn">import</span> <span class="n">nan</span>
<span class="kn">from</span> <span class="nn">torch.distributions</span> <span class="kn">import</span> <span class="n">constraints</span>
<span class="kn">from</span> <span class="nn">torch.distributions.exp_family</span> <span class="kn">import</span> <span class="n">ExponentialFamily</span>
<span class="kn">from</span> <span class="nn">torch.distributions.utils</span> <span class="kn">import</span> <span class="n">broadcast_all</span><span class="p">,</span> <span class="n">probs_to_logits</span><span class="p">,</span> <span class="n">logits_to_probs</span><span class="p">,</span> <span class="n">lazy_property</span>
<span class="kn">from</span> <span class="nn">torch.nn.functional</span> <span class="kn">import</span> <span class="n">binary_cross_entropy_with_logits</span>
<span class="n">__all__</span> <span class="o">=</span> <span class="p">[</span><span class="s1">'Bernoulli'</span><span class="p">]</span>
<div class="viewcode-block" id="Bernoulli"><a class="viewcode-back" href="../../../distributions.html#torch.distributions.bernoulli.Bernoulli">[docs]</a><span class="k">class</span> <span class="nc">Bernoulli</span><span class="p">(</span><span class="n">ExponentialFamily</span><span class="p">):</span>
<span class="sa">r</span><span class="sd">"""</span>
<span class="sd"> Creates a Bernoulli distribution parameterized by :attr:`probs`</span>
<span class="sd"> or :attr:`logits` (but not both).</span>
<span class="sd"> Samples are binary (0 or 1). They take the value `1` with probability `p`</span>
<span class="sd"> and `0` with probability `1 - p`.</span>
<span class="sd"> Example::</span>
<span class="sd"> >>> # xdoctest: +IGNORE_WANT("non-deterinistic")</span>
<span class="sd"> >>> m = Bernoulli(torch.tensor([0.3]))</span>
<span class="sd"> >>> m.sample() # 30% chance 1; 70% chance 0</span>
<span class="sd"> tensor([ 0.])</span>
<span class="sd"> Args:</span>
<span class="sd"> probs (Number, Tensor): the probability of sampling `1`</span>
<span class="sd"> logits (Number, Tensor): the log-odds of sampling `1`</span>
<span class="sd"> """</span>
<span class="n">arg_constraints</span> <span class="o">=</span> <span class="p">{</span><span class="s1">'probs'</span><span class="p">:</span> <span class="n">constraints</span><span class="o">.</span><span class="n">unit_interval</span><span class="p">,</span>
<span class="s1">'logits'</span><span class="p">:</span> <span class="n">constraints</span><span class="o">.</span><span class="n">real</span><span class="p">}</span>
<span class="n">support</span> <span class="o">=</span> <span class="n">constraints</span><span class="o">.</span><span class="n">boolean</span>
<span class="n">has_enumerate_support</span> <span class="o">=</span> <span class="kc">True</span>
<span class="n">_mean_carrier_measure</span> <span class="o">=</span> <span class="mi">0</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">probs</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">logits</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">validate_args</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
<span class="k">if</span> <span class="p">(</span><span class="n">probs</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">)</span> <span class="o">==</span> <span class="p">(</span><span class="n">logits</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">):</span>
<span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">"Either `probs` or `logits` must be specified, but not both."</span><span class="p">)</span>
<span class="k">if</span> <span class="n">probs</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
<span class="n">is_scalar</span> <span class="o">=</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">probs</span><span class="p">,</span> <span class="n">Number</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">probs</span><span class="p">,</span> <span class="o">=</span> <span class="n">broadcast_all</span><span class="p">(</span><span class="n">probs</span><span class="p">)</span>
<span class="k">else</span><span class="p">:</span>
<span class="n">is_scalar</span> <span class="o">=</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">logits</span><span class="p">,</span> <span class="n">Number</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">logits</span><span class="p">,</span> <span class="o">=</span> <span class="n">broadcast_all</span><span class="p">(</span><span class="n">logits</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_param</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">probs</span> <span class="k">if</span> <span class="n">probs</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="k">else</span> <span class="bp">self</span><span class="o">.</span><span class="n">logits</span>
<span class="k">if</span> <span class="n">is_scalar</span><span class="p">:</span>
<span class="n">batch_shape</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">Size</span><span class="p">()</span>
<span class="k">else</span><span class="p">:</span>
<span class="n">batch_shape</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_param</span><span class="o">.</span><span class="n">size</span><span class="p">()</span>
<span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="n">batch_shape</span><span class="p">,</span> <span class="n">validate_args</span><span class="o">=</span><span class="n">validate_args</span><span class="p">)</span>
<div class="viewcode-block" id="Bernoulli.expand"><a class="viewcode-back" href="../../../distributions.html#torch.distributions.bernoulli.Bernoulli.expand">[docs]</a> <span class="k">def</span> <span class="nf">expand</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">batch_shape</span><span class="p">,</span> <span class="n">_instance</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
<span class="n">new</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_checked_instance</span><span class="p">(</span><span class="n">Bernoulli</span><span class="p">,</span> <span class="n">_instance</span><span class="p">)</span>
<span class="n">batch_shape</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">Size</span><span class="p">(</span><span class="n">batch_shape</span><span class="p">)</span>
<span class="k">if</span> <span class="s1">'probs'</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="vm">__dict__</span><span class="p">:</span>
<span class="n">new</span><span class="o">.</span><span class="n">probs</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">probs</span><span class="o">.</span><span class="n">expand</span><span class="p">(</span><span class="n">batch_shape</span><span class="p">)</span>
<span class="n">new</span><span class="o">.</span><span class="n">_param</span> <span class="o">=</span> <span class="n">new</span><span class="o">.</span><span class="n">probs</span>
<span class="k">if</span> <span class="s1">'logits'</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="vm">__dict__</span><span class="p">:</span>
<span class="n">new</span><span class="o">.</span><span class="n">logits</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">logits</span><span class="o">.</span><span class="n">expand</span><span class="p">(</span><span class="n">batch_shape</span><span class="p">)</span>
<span class="n">new</span><span class="o">.</span><span class="n">_param</span> <span class="o">=</span> <span class="n">new</span><span class="o">.</span><span class="n">logits</span>
<span class="nb">super</span><span class="p">(</span><span class="n">Bernoulli</span><span class="p">,</span> <span class="n">new</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="n">batch_shape</span><span class="p">,</span> <span class="n">validate_args</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
<span class="n">new</span><span class="o">.</span><span class="n">_validate_args</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_validate_args</span>
<span class="k">return</span> <span class="n">new</span></div>
<span class="k">def</span> <span class="nf">_new</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_param</span><span class="o">.</span><span class="n">new</span><span class="p">(</span><span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
<span class="nd">@property</span>
<span class="k">def</span> <span class="nf">mean</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">probs</span>
<span class="nd">@property</span>
<span class="k">def</span> <span class="nf">mode</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="n">mode</span> <span class="o">=</span> <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">probs</span> <span class="o">>=</span> <span class="mf">0.5</span><span class="p">)</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">probs</span><span class="p">)</span>
<span class="n">mode</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">probs</span> <span class="o">==</span> <span class="mf">0.5</span><span class="p">]</span> <span class="o">=</span> <span class="n">nan</span>
<span class="k">return</span> <span class="n">mode</span>
<span class="nd">@property</span>
<span class="k">def</span> <span class="nf">variance</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">probs</span> <span class="o">*</span> <span class="p">(</span><span class="mi">1</span> <span class="o">-</span> <span class="bp">self</span><span class="o">.</span><span class="n">probs</span><span class="p">)</span>
<span class="nd">@lazy_property</span>
<span class="k">def</span> <span class="nf">logits</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="k">return</span> <span class="n">probs_to_logits</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">probs</span><span class="p">,</span> <span class="n">is_binary</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
<span class="nd">@lazy_property</span>
<span class="k">def</span> <span class="nf">probs</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="k">return</span> <span class="n">logits_to_probs</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">logits</span><span class="p">,</span> <span class="n">is_binary</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
<span class="nd">@property</span>
<span class="k">def</span> <span class="nf">param_shape</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_param</span><span class="o">.</span><span class="n">size</span><span class="p">()</span>
<div class="viewcode-block" id="Bernoulli.sample"><a class="viewcode-back" href="../../../distributions.html#torch.distributions.bernoulli.Bernoulli.sample">[docs]</a> <span class="k">def</span> <span class="nf">sample</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">sample_shape</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">Size</span><span class="p">()):</span>
<span class="n">shape</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_extended_shape</span><span class="p">(</span><span class="n">sample_shape</span><span class="p">)</span>
<span class="k">with</span> <span class="n">torch</span><span class="o">.</span><span class="n">no_grad</span><span class="p">():</span>
<span class="k">return</span> <span class="n">torch</span><span class="o">.</span><span class="n">bernoulli</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">probs</span><span class="o">.</span><span class="n">expand</span><span class="p">(</span><span class="n">shape</span><span class="p">))</span></div>
<div class="viewcode-block" id="Bernoulli.log_prob"><a class="viewcode-back" href="../../../distributions.html#torch.distributions.bernoulli.Bernoulli.log_prob">[docs]</a> <span class="k">def</span> <span class="nf">log_prob</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">value</span><span class="p">):</span>
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_validate_args</span><span class="p">:</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_validate_sample</span><span class="p">(</span><span class="n">value</span><span class="p">)</span>
<span class="n">logits</span><span class="p">,</span> <span class="n">value</span> <span class="o">=</span> <span class="n">broadcast_all</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">logits</span><span class="p">,</span> <span class="n">value</span><span class="p">)</span>
<span class="k">return</span> <span class="o">-</span><span class="n">binary_cross_entropy_with_logits</span><span class="p">(</span><span class="n">logits</span><span class="p">,</span> <span class="n">value</span><span class="p">,</span> <span class="n">reduction</span><span class="o">=</span><span class="s1">'none'</span><span class="p">)</span></div>
<div class="viewcode-block" id="Bernoulli.entropy"><a class="viewcode-back" href="../../../distributions.html#torch.distributions.bernoulli.Bernoulli.entropy">[docs]</a> <span class="k">def</span> <span class="nf">entropy</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="k">return</span> <span class="n">binary_cross_entropy_with_logits</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">logits</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">probs</span><span class="p">,</span> <span class="n">reduction</span><span class="o">=</span><span class="s1">'none'</span><span class="p">)</span></div>
<div class="viewcode-block" id="Bernoulli.enumerate_support"><a class="viewcode-back" href="../../../distributions.html#torch.distributions.bernoulli.Bernoulli.enumerate_support">[docs]</a> <span class="k">def</span> <span class="nf">enumerate_support</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">expand</span><span class="o">=</span><span class="kc">True</span><span class="p">):</span>
<span class="n">values</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">_param</span><span class="o">.</span><span class="n">dtype</span><span class="p">,</span> <span class="n">device</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">_param</span><span class="o">.</span><span class="n">device</span><span class="p">)</span>
<span class="n">values</span> <span class="o">=</span> <span class="n">values</span><span class="o">.</span><span class="n">view</span><span class="p">((</span><span class="o">-</span><span class="mi">1</span><span class="p">,)</span> <span class="o">+</span> <span class="p">(</span><span class="mi">1</span><span class="p">,)</span> <span class="o">*</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_batch_shape</span><span class="p">))</span>
<span class="k">if</span> <span class="n">expand</span><span class="p">:</span>
<span class="n">values</span> <span class="o">=</span> <span class="n">values</span><span class="o">.</span><span class="n">expand</span><span class="p">((</span><span class="o">-</span><span class="mi">1</span><span class="p">,)</span> <span class="o">+</span> <span class="bp">self</span><span class="o">.</span><span class="n">_batch_shape</span><span class="p">)</span>
<span class="k">return</span> <span class="n">values</span></div>
<span class="nd">@property</span>
<span class="k">def</span> <span class="nf">_natural_params</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="k">return</span> <span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">logit</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">probs</span><span class="p">),</span> <span class="p">)</span>
<span class="k">def</span> <span class="nf">_log_normalizer</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">):</span>
<span class="k">return</span> <span class="n">torch</span><span class="o">.</span><span class="n">log1p</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">exp</span><span class="p">(</span><span class="n">x</span><span class="p">))</span></div>
</pre></div>
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