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<div class="section" id="torch-argmax">
<h1>torch.argmax<a class="headerlink" href="#torch-argmax" title="Permalink to this headline">¶</a></h1>
<dl class="function">
<dt id="torch.argmax">
<code class="sig-prename descclassname">torch.</code><code class="sig-name descname">argmax</code><span class="sig-paren">(</span><em class="sig-param">input</em><span class="sig-paren">)</span> → LongTensor<a class="headerlink" href="#torch.argmax" title="Permalink to this definition">¶</a></dt>
<dd><p>Returns the indices of the maximum value of all elements in the <code class="xref py py-attr docutils literal notranslate"><span class="pre">input</span></code> tensor.</p>
<p>This is the second value returned by <a class="reference internal" href="torch.max.html#torch.max" title="torch.max"><code class="xref py py-meth docutils literal notranslate"><span class="pre">torch.max()</span></code></a>. See its
documentation for the exact semantics of this method.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><p><strong>input</strong> (<a class="reference internal" href="../tensors.html#torch.Tensor" title="torch.Tensor"><em>Tensor</em></a>) – the input tensor.</p>
</dd>
</dl>
<p>Example:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">a</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">4</span><span class="p">,</span> <span class="mi">4</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">a</span>
<span class="go">tensor([[ 1.3398, 0.2663, -0.2686, 0.2450],</span>
<span class="go"> [-0.7401, -0.8805, -0.3402, -1.1936],</span>
<span class="go"> [ 0.4907, -1.3948, -1.0691, -0.3132],</span>
<span class="go"> [-1.6092, 0.5419, -0.2993, 0.3195]])</span>
<span class="gp">>>> </span><span class="n">torch</span><span class="o">.</span><span class="n">argmax</span><span class="p">(</span><span class="n">a</span><span class="p">)</span>
<span class="go">tensor(0)</span>
</pre></div>
</div>
<dl class="function">
<dt>
<code class="sig-prename descclassname">torch.</code><code class="sig-name descname">argmax</code><span class="sig-paren">(</span><em class="sig-param">input</em>, <em class="sig-param">dim</em>, <em class="sig-param">keepdim=False</em><span class="sig-paren">)</span> → LongTensor</dt>
<dd></dd></dl>
<p>Returns the indices of the maximum values of a tensor across a dimension.</p>
<p>This is the second value returned by <a class="reference internal" href="torch.max.html#torch.max" title="torch.max"><code class="xref py py-meth docutils literal notranslate"><span class="pre">torch.max()</span></code></a>. See its
documentation for the exact semantics of this method.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>input</strong> (<a class="reference internal" href="../tensors.html#torch.Tensor" title="torch.Tensor"><em>Tensor</em></a>) – the input tensor.</p></li>
<li><p><strong>dim</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.8)"><em>int</em></a>) – the dimension to reduce. If <code class="docutils literal notranslate"><span class="pre">None</span></code>, the argmax of the flattened input is returned.</p></li>
<li><p><strong>keepdim</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.8)"><em>bool</em></a>) – whether the output tensor has <code class="xref py py-attr docutils literal notranslate"><span class="pre">dim</span></code> retained or not. Ignored if <code class="docutils literal notranslate"><span class="pre">dim=None</span></code>.</p></li>
</ul>
</dd>
</dl>
<p>Example:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">a</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">4</span><span class="p">,</span> <span class="mi">4</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">a</span>
<span class="go">tensor([[ 1.3398, 0.2663, -0.2686, 0.2450],</span>
<span class="go"> [-0.7401, -0.8805, -0.3402, -1.1936],</span>
<span class="go"> [ 0.4907, -1.3948, -1.0691, -0.3132],</span>
<span class="go"> [-1.6092, 0.5419, -0.2993, 0.3195]])</span>
<span class="gp">>>> </span><span class="n">torch</span><span class="o">.</span><span class="n">argmax</span><span class="p">(</span><span class="n">a</span><span class="p">,</span> <span class="n">dim</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
<span class="go">tensor([ 0, 2, 0, 1])</span>
</pre></div>
</div>
</dd></dl>
</div>
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