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<!DOCTYPE html>
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<title>torch.autograd — PyTorch 0.1.12_2 documentation</title>
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<p class="caption"><span class="caption-text">Notes</span></p>
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<li class="toctree-l1"><a class="reference internal" href="../../notes/autograd.html">Autograd mechanics</a><ul>
<li class="toctree-l2"><a class="reference internal" href="../../notes/autograd.html#excluding-subgraphs-from-backward">Excluding subgraphs from backward</a><ul>
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<p class="caption"><span class="caption-text">Package Reference</span></p>
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<li class="toctree-l2"><a class="reference internal" href="../../nn.html#loss-functions">Loss functions</a><ul>
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<li class="toctree-l3"><a class="reference internal" href="../../nn.html#crossentropyloss"><span class="hidden-section">CrossEntropyLoss</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="../../nn.html#nllloss"><span class="hidden-section">NLLLoss</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="../../nn.html#nllloss2d"><span class="hidden-section">NLLLoss2d</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="../../nn.html#kldivloss"><span class="hidden-section">KLDivLoss</span></a></li>
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<li class="toctree-l3"><a class="reference internal" href="../../nn.html#marginrankingloss"><span class="hidden-section">MarginRankingLoss</span></a></li>
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<li class="toctree-l3"><a class="reference internal" href="../../nn.html#multilabelsoftmarginloss"><span class="hidden-section">MultiLabelSoftMarginLoss</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="../../nn.html#cosineembeddingloss"><span class="hidden-section">CosineEmbeddingLoss</span></a></li>
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<li class="toctree-l3"><a class="reference internal" href="../../nn.html#id25"><span class="hidden-section">softplus</span></a></li>
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<li class="toctree-l3"><a class="reference internal" href="../../nn.html#id27"><span class="hidden-section">softmax</span></a></li>
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<h1>Source code for torch.autograd</h1><div class="highlight"><pre>
<span></span><span class="sd">"""</span>
<span class="sd">torch.autograd provides classes and functions implementing automatic</span>
<span class="sd">differentiation of arbitrary scalar valued functions. It requires minimal</span>
<span class="sd">changes to the existing code - you only need to wrap all tensors in</span>
<span class="sd">:class:`.Variable` objects.</span>
<span class="sd">"""</span>
<span class="kn">import</span> <span class="nn">torch</span>
<span class="kn">from</span> <span class="nn">.variable</span> <span class="k">import</span> <span class="n">Variable</span>
<span class="kn">from</span> <span class="nn">.function</span> <span class="k">import</span> <span class="n">Function</span><span class="p">,</span> <span class="n">NestedIOFunction</span>
<span class="kn">from</span> <span class="nn">.stochastic_function</span> <span class="k">import</span> <span class="n">StochasticFunction</span>
<span class="kn">from</span> <span class="nn">.gradcheck</span> <span class="k">import</span> <span class="n">gradcheck</span>
<span class="n">__all__</span> <span class="o">=</span> <span class="p">[</span><span class="s1">'Variable'</span><span class="p">,</span> <span class="s1">'Function'</span><span class="p">,</span> <span class="s1">'StochasticFunction'</span><span class="p">,</span> <span class="s1">'backward'</span><span class="p">]</span>
<div class="viewcode-block" id="backward"><a class="viewcode-back" href="../../autograd.html#torch.autograd.backward">[docs]</a><span class="k">def</span> <span class="nf">backward</span><span class="p">(</span><span class="n">variables</span><span class="p">,</span> <span class="n">grad_variables</span><span class="p">,</span> <span class="n">retain_variables</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
<span class="sd">"""Computes the sum of gradients of given variables w.r.t. graph leaves.</span>
<span class="sd"> The graph is differentiated using the chain rule. If any of ``variables``</span>
<span class="sd"> are non-scalar (i.e. their data has more than one element) and require</span>
<span class="sd"> gradient, the function additionaly requires specifying ``grad_variables``.</span>
<span class="sd"> It should be a sequence of matching length, that containins gradient of</span>
<span class="sd"> the differentiated function w.r.t. corresponding variables (``None`` is an</span>
<span class="sd"> acceptable value for all variables that don't need gradient tensors).</span>
<span class="sd"> This function accumulates gradients in the leaves - you might need to zero</span>
<span class="sd"> them before calling it.</span>
<span class="sd"> Arguments:</span>
<span class="sd"> variables (sequence of Variable): Variables of which the derivative will be</span>
<span class="sd"> computed.</span>
<span class="sd"> grad_variables (sequence of Tensor): Gradients w.r.t. each element of</span>
<span class="sd"> corresponding variables. Required only for non-scalar variables that</span>
<span class="sd"> require gradient.</span>
<span class="sd"> retain_variables (bool): If ``True``, buffers necessary for computing</span>
<span class="sd"> gradients won't be freed after use. It is only necessary to</span>
<span class="sd"> specify ``True`` if you want to differentiate some subgraph multiple</span>
<span class="sd"> times.</span>
<span class="sd"> """</span>
<span class="n">Variable</span><span class="o">.</span><span class="n">_execution_engine</span><span class="o">.</span><span class="n">run_backward</span><span class="p">(</span>
<span class="nb">tuple</span><span class="p">(</span><span class="n">variables</span><span class="p">),</span> <span class="nb">tuple</span><span class="p">(</span><span class="n">grad_variables</span><span class="p">),</span> <span class="n">retain_variables</span><span class="p">)</span></div>
<span class="k">assert</span> <span class="n">torch</span><span class="o">.</span><span class="n">_C</span><span class="o">.</span><span class="n">_autograd_init</span><span class="p">()</span>
</pre></div>
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