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<h1>torch.nn.utils.prune.random_unstructured<a class="headerlink" href="#torch-nn-utils-prune-random-unstructured" title="Permalink to this headline">¶</a></h1>
<dl class="function">
<dt id="torch.nn.utils.prune.random_unstructured">
<code class="sig-prename descclassname">torch.nn.utils.prune.</code><code class="sig-name descname">random_unstructured</code><span class="sig-paren">(</span><em class="sig-param">module</em>, <em class="sig-param">name</em>, <em class="sig-param">amount</em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/utils/prune.html#random_unstructured"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torch.nn.utils.prune.random_unstructured" title="Permalink to this definition">¶</a></dt>
<dd><p>Prunes tensor corresponding to parameter called <code class="docutils literal notranslate"><span class="pre">name</span></code> in <code class="docutils literal notranslate"><span class="pre">module</span></code>
by removing the specified <code class="docutils literal notranslate"><span class="pre">amount</span></code> of (currently unpruned) units
selected at random.
Modifies module in place (and also return the modified module) by:
1) adding a named buffer called <code class="docutils literal notranslate"><span class="pre">name+'_mask'</span></code> corresponding to the
binary mask applied to the parameter <cite>name</cite> by the pruning method.
2) replacing the parameter <code class="docutils literal notranslate"><span class="pre">name</span></code> by its pruned version, while the
original (unpruned) parameter is stored in a new parameter named
<code class="docutils literal notranslate"><span class="pre">name+'_orig'</span></code>.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>module</strong> (<a class="reference internal" href="torch.nn.Module.html#torch.nn.Module" title="torch.nn.Module"><em>nn.Module</em></a>) – module containing the tensor to prune</p></li>
<li><p><strong>name</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.9)"><em>str</em></a>) – parameter name within <code class="docutils literal notranslate"><span class="pre">module</span></code> on which pruning
will act.</p></li>
<li><p><strong>amount</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.9)"><em>int</em></a><em> or </em><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.9)"><em>float</em></a>) – quantity of parameters to prune.
If <code class="docutils literal notranslate"><span class="pre">float</span></code>, should be between 0.0 and 1.0 and represent the
fraction of parameters to prune. If <code class="docutils literal notranslate"><span class="pre">int</span></code>, it represents the
absolute number of parameters to prune.</p></li>
</ul>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p>modified (i.e. pruned) version of the input module</p>
</dd>
<dt class="field-odd">Return type</dt>
<dd class="field-odd"><p>module (<a class="reference internal" href="torch.nn.Module.html#torch.nn.Module" title="torch.nn.Module">nn.Module</a>)</p>
</dd>
</dl>
<p class="rubric">Examples</p>
<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">m</span> <span class="o">=</span> <span class="n">prune</span><span class="o">.</span><span class="n">random_unstructured</span><span class="p">(</span><span class="n">nn</span><span class="o">.</span><span class="n">Linear</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">),</span> <span class="s1">'weight'</span><span class="p">,</span> <span class="n">amount</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">torch</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">m</span><span class="o">.</span><span class="n">weight_mask</span> <span class="o">==</span> <span class="mi">0</span><span class="p">)</span>
<span class="go">tensor(1)</span>
</pre></div>
</div>
</dd></dl>
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