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<!DOCTYPE html>
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<p class="caption"><span class="caption-text">Notes</span></p>
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<li class="toctree-l3"><a class="reference internal" href="../../../nn.html#batchnorm1d"><span class="hidden-section">BatchNorm1d</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="../../../nn.html#batchnorm2d"><span class="hidden-section">BatchNorm2d</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="../../../nn.html#batchnorm3d"><span class="hidden-section">BatchNorm3d</span></a></li>
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<li class="toctree-l3"><a class="reference internal" href="../../../nn.html#rnncell"><span class="hidden-section">RNNCell</span></a></li>
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<li class="toctree-l3"><a class="reference internal" href="../../../nn.html#grucell"><span class="hidden-section">GRUCell</span></a></li>
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<li class="toctree-l3"><a class="reference internal" href="../../../nn.html#dropout"><span class="hidden-section">Dropout</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="../../../nn.html#dropout2d"><span class="hidden-section">Dropout2d</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="../../../nn.html#dropout3d"><span class="hidden-section">Dropout3d</span></a></li>
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<li class="toctree-l2"><a class="reference internal" href="../../../nn.html#loss-functions">Loss functions</a><ul>
<li class="toctree-l3"><a class="reference internal" href="../../../nn.html#l1loss"><span class="hidden-section">L1Loss</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="../../../nn.html#mseloss"><span class="hidden-section">MSELoss</span></a></li>
<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>
<li class="toctree-l3"><a class="reference internal" href="../../../nn.html#hingeembeddingloss"><span class="hidden-section">HingeEmbeddingLoss</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="../../../nn.html#multilabelmarginloss"><span class="hidden-section">MultiLabelMarginLoss</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="../../../nn.html#smoothl1loss"><span class="hidden-section">SmoothL1Loss</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="../../../nn.html#softmarginloss"><span class="hidden-section">SoftMarginLoss</span></a></li>
<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>
<li class="toctree-l3"><a class="reference internal" href="../../../nn.html#multimarginloss"><span class="hidden-section">MultiMarginLoss</span></a></li>
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<li class="toctree-l3"><a class="reference internal" href="../../../nn.html#upsamplingbilinear2d"><span class="hidden-section">UpsamplingBilinear2d</span></a></li>
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<li class="toctree-l3"><a class="reference internal" href="../../../nn.html#pack-padded-sequence"><span class="hidden-section">pack_padded_sequence</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="../../../nn.html#pad-packed-sequence"><span class="hidden-section">pad_packed_sequence</span></a></li>
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<li class="toctree-l1"><a class="reference internal" href="../../../nn.html#torch-nn-functional">torch.nn.functional</a><ul>
<li class="toctree-l2"><a class="reference internal" href="../../../nn.html#convolution-functions">Convolution functions</a><ul>
<li class="toctree-l3"><a class="reference internal" href="../../../nn.html#id13"><span class="hidden-section">conv1d</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="../../../nn.html#id14"><span class="hidden-section">conv2d</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="../../../nn.html#id15"><span class="hidden-section">conv3d</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="../../../nn.html#conv-transpose1d"><span class="hidden-section">conv_transpose1d</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="../../../nn.html#conv-transpose2d"><span class="hidden-section">conv_transpose2d</span></a></li>
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<li class="toctree-l3"><a class="reference internal" href="../../../nn.html#avg-pool2d"><span class="hidden-section">avg_pool2d</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="../../../nn.html#avg-pool3d"><span class="hidden-section">avg_pool3d</span></a></li>
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<li class="toctree-l3"><a class="reference internal" href="../../../nn.html#max-unpool2d"><span class="hidden-section">max_unpool2d</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="../../../nn.html#max-unpool3d"><span class="hidden-section">max_unpool3d</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="../../../nn.html#lp-pool2d"><span class="hidden-section">lp_pool2d</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="../../../nn.html#adaptive-max-pool1d"><span class="hidden-section">adaptive_max_pool1d</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="../../../nn.html#adaptive-max-pool2d"><span class="hidden-section">adaptive_max_pool2d</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="../../../nn.html#adaptive-avg-pool1d"><span class="hidden-section">adaptive_avg_pool1d</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="../../../nn.html#adaptive-avg-pool2d"><span class="hidden-section">adaptive_avg_pool2d</span></a></li>
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<li class="toctree-l2"><a class="reference internal" href="../../../nn.html#non-linear-activation-functions">Non-linear activation functions</a><ul>
<li class="toctree-l3"><a class="reference internal" href="../../../nn.html#id16"><span class="hidden-section">threshold</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="../../../nn.html#id17"><span class="hidden-section">relu</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="../../../nn.html#id18"><span class="hidden-section">hardtanh</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="../../../nn.html#id19"><span class="hidden-section">relu6</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="../../../nn.html#id20"><span class="hidden-section">elu</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="../../../nn.html#leaky-relu"><span class="hidden-section">leaky_relu</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="../../../nn.html#id21"><span class="hidden-section">prelu</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="../../../nn.html#rrelu"><span class="hidden-section">rrelu</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="../../../nn.html#id22"><span class="hidden-section">logsigmoid</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="../../../nn.html#hardshrink"><span class="hidden-section">hardshrink</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="../../../nn.html#id23"><span class="hidden-section">tanhshrink</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="../../../nn.html#id24"><span class="hidden-section">softsign</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="../../../nn.html#id25"><span class="hidden-section">softplus</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="../../../nn.html#id26"><span class="hidden-section">softmin</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="../../../nn.html#id27"><span class="hidden-section">softmax</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="../../../nn.html#id28"><span class="hidden-section">softshrink</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="../../../nn.html#log-softmax"><span class="hidden-section">log_softmax</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="../../../nn.html#id29"><span class="hidden-section">tanh</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="../../../nn.html#id30"><span class="hidden-section">sigmoid</span></a></li>
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<li class="toctree-l3"><a class="reference internal" href="../../../nn.html#id31"><span class="hidden-section">linear</span></a></li>
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<li class="toctree-l3"><a class="reference internal" href="../../../nn.html#id32"><span class="hidden-section">dropout</span></a></li>
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<li class="toctree-l3"><a class="reference internal" href="../../../nn.html#pairwise-distance"><span class="hidden-section">pairwise_distance</span></a></li>
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<li class="toctree-l3"><a class="reference internal" href="../../../nn.html#nll-loss"><span class="hidden-section">nll_loss</span></a></li>
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<li class="toctree-l3"><a class="reference internal" href="../../../nn.html#smooth-l1-loss"><span class="hidden-section">smooth_l1_loss</span></a></li>
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<h1>Source code for torch.nn.functional</h1><div class="highlight"><pre>
<span></span><span class="sd">"""Functional interface"""</span>
<span class="kn">import</span> <span class="nn">torch</span>
<span class="kn">from</span> <span class="nn">.</span> <span class="k">import</span> <span class="n">_functions</span>
<span class="kn">from</span> <span class="nn">.modules</span> <span class="k">import</span> <span class="n">utils</span>
<span class="kn">from</span> <span class="nn">._functions.padding</span> <span class="k">import</span> <span class="n">ConstantPad2d</span>
<span class="kn">from</span> <span class="nn">.modules.utils</span> <span class="k">import</span> <span class="n">_single</span><span class="p">,</span> <span class="n">_pair</span><span class="p">,</span> <span class="n">_triple</span>
<span class="c1"># Convolutions</span>
<span class="n">ConvNd</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">_C</span><span class="o">.</span><span class="n">_functions</span><span class="o">.</span><span class="n">ConvNd</span>
<div class="viewcode-block" id="conv2d"><a class="viewcode-back" href="../../../nn.html#torch.nn.functional.conv2d">[docs]</a><span class="k">def</span> <span class="nf">conv2d</span><span class="p">(</span><span class="nb">input</span><span class="p">,</span> <span class="n">weight</span><span class="p">,</span> <span class="n">bias</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">stride</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">padding</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">dilation</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
<span class="n">groups</span><span class="o">=</span><span class="mi">1</span><span class="p">):</span>
<span class="sd">"""Applies a 2D convolution over an input image composed of several input</span>
<span class="sd"> planes.</span>
<span class="sd"> See :class:`~torch.nn.Conv2d` for details and output shape.</span>
<span class="sd"> Args:</span>
<span class="sd"> input: input tensor (minibatch x in_channels x iH x iW)</span>
<span class="sd"> weight: filters tensor (out_channels, in_channels/groups, kH, kW)</span>
<span class="sd"> bias: optional bias tensor (out_channels)</span>
<span class="sd"> stride: the stride of the convolving kernel. Can be a single number or</span>
<span class="sd"> a tuple (sh x sw). Default: 1</span>
<span class="sd"> padding: implicit zero padding on the input. Can be a single number or</span>
<span class="sd"> a tuple. Default: 0</span>
<span class="sd"> groups: split input into groups, in_channels should be divisible by</span>
<span class="sd"> the number of groups</span>
<span class="sd"> Examples:</span>
<span class="sd"> >>> # With square kernels and equal stride</span>
<span class="sd"> >>> filters = autograd.Variable(torch.randn(8,4,3,3))</span>
<span class="sd"> >>> inputs = autograd.Variable(torch.randn(1,4,5,5))</span>
<span class="sd"> >>> F.conv2d(inputs, filters, padding=1)</span>
<span class="sd"> """</span>
<span class="n">f</span> <span class="o">=</span> <span class="n">ConvNd</span><span class="p">(</span><span class="n">_pair</span><span class="p">(</span><span class="n">stride</span><span class="p">),</span> <span class="n">_pair</span><span class="p">(</span><span class="n">padding</span><span class="p">),</span> <span class="n">_pair</span><span class="p">(</span><span class="n">dilation</span><span class="p">),</span> <span class="kc">False</span><span class="p">,</span>
<span class="n">_pair</span><span class="p">(</span><span class="mi">0</span><span class="p">),</span> <span class="n">groups</span><span class="p">,</span> <span class="n">torch</span><span class="o">.</span><span class="n">backends</span><span class="o">.</span><span class="n">cudnn</span><span class="o">.</span><span class="n">benchmark</span><span class="p">,</span> <span class="n">torch</span><span class="o">.</span><span class="n">backends</span><span class="o">.</span><span class="n">cudnn</span><span class="o">.</span><span class="n">enabled</span><span class="p">)</span>
<span class="k">return</span> <span class="n">f</span><span class="p">(</span><span class="nb">input</span><span class="p">,</span> <span class="n">weight</span><span class="p">,</span> <span class="n">bias</span><span class="p">)</span></div>
<div class="viewcode-block" id="conv1d"><a class="viewcode-back" href="../../../nn.html#torch.nn.functional.conv1d">[docs]</a><span class="k">def</span> <span class="nf">conv1d</span><span class="p">(</span><span class="nb">input</span><span class="p">,</span> <span class="n">weight</span><span class="p">,</span> <span class="n">bias</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">stride</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">padding</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">dilation</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
<span class="n">groups</span><span class="o">=</span><span class="mi">1</span><span class="p">):</span>
<span class="sd">"""Applies a 1D convolution over an input signal composed of several input</span>
<span class="sd"> planes.</span>
<span class="sd"> See :class:`~torch.nn.Conv1d` for details and output shape.</span>
<span class="sd"> Args:</span>
<span class="sd"> input: input tensor of shape (minibatch x in_channels x iW)</span>
<span class="sd"> weight: filters of shape (out_channels, in_channels, kW)</span>
<span class="sd"> bias: optional bias of shape (out_channels)</span>
<span class="sd"> stride: the stride of the convolving kernel, default 1</span>
<span class="sd"> Examples:</span>
<span class="sd"> >>> filters = autograd.Variable(torch.randn(33, 16, 3))</span>
<span class="sd"> >>> inputs = autograd.Variable(torch.randn(20, 16, 50))</span>
<span class="sd"> >>> F.conv1d(inputs, filters)</span>
<span class="sd"> """</span>
<span class="n">f</span> <span class="o">=</span> <span class="n">ConvNd</span><span class="p">(</span><span class="n">_single</span><span class="p">(</span><span class="n">stride</span><span class="p">),</span> <span class="n">_single</span><span class="p">(</span><span class="n">padding</span><span class="p">),</span> <span class="n">_single</span><span class="p">(</span><span class="n">dilation</span><span class="p">),</span> <span class="kc">False</span><span class="p">,</span>
<span class="n">_single</span><span class="p">(</span><span class="mi">0</span><span class="p">),</span> <span class="n">groups</span><span class="p">,</span> <span class="n">torch</span><span class="o">.</span><span class="n">backends</span><span class="o">.</span><span class="n">cudnn</span><span class="o">.</span><span class="n">benchmark</span><span class="p">,</span> <span class="n">torch</span><span class="o">.</span><span class="n">backends</span><span class="o">.</span><span class="n">cudnn</span><span class="o">.</span><span class="n">enabled</span><span class="p">)</span>
<span class="k">return</span> <span class="n">f</span><span class="p">(</span><span class="nb">input</span><span class="p">,</span> <span class="n">weight</span><span class="p">,</span> <span class="n">bias</span><span class="p">)</span></div>
<div class="viewcode-block" id="conv3d"><a class="viewcode-back" href="../../../nn.html#torch.nn.functional.conv3d">[docs]</a><span class="k">def</span> <span class="nf">conv3d</span><span class="p">(</span><span class="nb">input</span><span class="p">,</span> <span class="n">weight</span><span class="p">,</span> <span class="n">bias</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">stride</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">padding</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">dilation</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
<span class="n">groups</span><span class="o">=</span><span class="mi">1</span><span class="p">):</span>
<span class="sd">"""Applies a 3D convolution over an input image composed of several input</span>
<span class="sd"> planes.</span>
<span class="sd"> See :class:`~torch.nn.Conv3d` for details and output shape.</span>
<span class="sd"> Args:</span>
<span class="sd"> input: input tensor of shape (minibatch x in_channels x iT x iH x iW)</span>
<span class="sd"> weight: filters tensor of shape (out_channels, in_channels, kT, kH, kW)</span>
<span class="sd"> bias: optional bias tensor of shape (out_channels)</span>
<span class="sd"> stride: the stride of the convolving kernel. Can be a single number or</span>
<span class="sd"> a tuple (st x sh x sw). Default: 1</span>
<span class="sd"> padding: implicit zero padding on the input. Can be a single number or</span>
<span class="sd"> a tuple. Default: 0</span>
<span class="sd"> Examples:</span>
<span class="sd"> >>> filters = autograd.Variable(torch.randn(33, 16, 3, 3, 3))</span>
<span class="sd"> >>> inputs = autograd.Variable(torch.randn(20, 16, 50, 10, 20))</span>
<span class="sd"> >>> F.conv3d(inputs, filters)</span>
<span class="sd"> """</span>
<span class="n">f</span> <span class="o">=</span> <span class="n">ConvNd</span><span class="p">(</span><span class="n">_triple</span><span class="p">(</span><span class="n">stride</span><span class="p">),</span> <span class="n">_triple</span><span class="p">(</span><span class="n">padding</span><span class="p">),</span> <span class="n">_triple</span><span class="p">(</span><span class="n">dilation</span><span class="p">),</span> <span class="kc">False</span><span class="p">,</span>
<span class="n">_triple</span><span class="p">(</span><span class="mi">0</span><span class="p">),</span> <span class="n">groups</span><span class="p">,</span> <span class="n">torch</span><span class="o">.</span><span class="n">backends</span><span class="o">.</span><span class="n">cudnn</span><span class="o">.</span><span class="n">benchmark</span><span class="p">,</span> <span class="n">torch</span><span class="o">.</span><span class="n">backends</span><span class="o">.</span><span class="n">cudnn</span><span class="o">.</span><span class="n">enabled</span><span class="p">)</span>
<span class="k">return</span> <span class="n">f</span><span class="p">(</span><span class="nb">input</span><span class="p">,</span> <span class="n">weight</span><span class="p">,</span> <span class="n">bias</span><span class="p">)</span></div>
<div class="viewcode-block" id="conv_transpose1d"><a class="viewcode-back" href="../../../nn.html#torch.nn.functional.conv_transpose1d">[docs]</a><span class="k">def</span> <span class="nf">conv_transpose1d</span><span class="p">(</span><span class="nb">input</span><span class="p">,</span> <span class="n">weight</span><span class="p">,</span> <span class="n">bias</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">stride</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">padding</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span>
<span class="n">output_padding</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">groups</span><span class="o">=</span><span class="mi">1</span><span class="p">):</span>
<span class="n">f</span> <span class="o">=</span> <span class="n">ConvNd</span><span class="p">(</span><span class="n">_single</span><span class="p">(</span><span class="n">stride</span><span class="p">),</span> <span class="n">_single</span><span class="p">(</span><span class="n">padding</span><span class="p">),</span> <span class="n">_single</span><span class="p">(</span><span class="mi">1</span><span class="p">),</span> <span class="kc">True</span><span class="p">,</span>
<span class="n">_single</span><span class="p">(</span><span class="n">output_padding</span><span class="p">),</span> <span class="n">groups</span><span class="p">,</span> <span class="n">torch</span><span class="o">.</span><span class="n">backends</span><span class="o">.</span><span class="n">cudnn</span><span class="o">.</span><span class="n">benchmark</span><span class="p">,</span> <span class="n">torch</span><span class="o">.</span><span class="n">backends</span><span class="o">.</span><span class="n">cudnn</span><span class="o">.</span><span class="n">enabled</span><span class="p">)</span>
<span class="k">return</span> <span class="n">f</span><span class="p">(</span><span class="nb">input</span><span class="p">,</span> <span class="n">weight</span><span class="p">,</span> <span class="n">bias</span><span class="p">)</span></div>
<div class="viewcode-block" id="conv_transpose2d"><a class="viewcode-back" href="../../../nn.html#torch.nn.functional.conv_transpose2d">[docs]</a><span class="k">def</span> <span class="nf">conv_transpose2d</span><span class="p">(</span><span class="nb">input</span><span class="p">,</span> <span class="n">weight</span><span class="p">,</span> <span class="n">bias</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">stride</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">padding</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span>
<span class="n">output_padding</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">groups</span><span class="o">=</span><span class="mi">1</span><span class="p">):</span>
<span class="sd">"""Applies a 2D transposed convolution operator over an input image</span>
<span class="sd"> composed of several input planes, sometimes also called "deconvolution".</span>
<span class="sd"> See :class:`~torch.nn.ConvTranspose2d` for details and output shape.</span>
<span class="sd"> Args:</span>
<span class="sd"> input: input tensor of shape (minibatch x in_channels x iH x iW)</span>
<span class="sd"> weight: filters of shape (in_channels x out_channels x kH x kW)</span>
<span class="sd"> bias: optional bias of shape (out_channels)</span>
<span class="sd"> stride: the stride of the convolving kernel, a single number or a</span>
<span class="sd"> tuple (sh x sw). Default: 1</span>
<span class="sd"> padding: implicit zero padding on the input, a single number or a</span>
<span class="sd"> tuple (padh x padw). Default: 0</span>
<span class="sd"> groups: split input into groups, in_channels should be divisible by</span>
<span class="sd"> the number of groups</span>
<span class="sd"> output_padding: A zero-padding of 0 <= padding < stride that should be</span>
<span class="sd"> added to the output. Can be a single number or a tuple. Default: 0</span>
<span class="sd"> """</span>
<span class="n">f</span> <span class="o">=</span> <span class="n">ConvNd</span><span class="p">(</span><span class="n">_pair</span><span class="p">(</span><span class="n">stride</span><span class="p">),</span> <span class="n">_pair</span><span class="p">(</span><span class="n">padding</span><span class="p">),</span> <span class="n">_pair</span><span class="p">(</span><span class="mi">1</span><span class="p">),</span> <span class="kc">True</span><span class="p">,</span>
<span class="n">_pair</span><span class="p">(</span><span class="n">output_padding</span><span class="p">),</span> <span class="n">groups</span><span class="p">,</span> <span class="n">torch</span><span class="o">.</span><span class="n">backends</span><span class="o">.</span><span class="n">cudnn</span><span class="o">.</span><span class="n">benchmark</span><span class="p">,</span> <span class="n">torch</span><span class="o">.</span><span class="n">backends</span><span class="o">.</span><span class="n">cudnn</span><span class="o">.</span><span class="n">enabled</span><span class="p">)</span>
<span class="k">return</span> <span class="n">f</span><span class="p">(</span><span class="nb">input</span><span class="p">,</span> <span class="n">weight</span><span class="p">,</span> <span class="n">bias</span><span class="p">)</span></div>
<div class="viewcode-block" id="conv_transpose3d"><a class="viewcode-back" href="../../../nn.html#torch.nn.functional.conv_transpose3d">[docs]</a><span class="k">def</span> <span class="nf">conv_transpose3d</span><span class="p">(</span><span class="nb">input</span><span class="p">,</span> <span class="n">weight</span><span class="p">,</span> <span class="n">bias</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">stride</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">padding</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span>
<span class="n">output_padding</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">groups</span><span class="o">=</span><span class="mi">1</span><span class="p">):</span>
<span class="sd">"""Applies a 3D transposed convolution operator over an input image</span>
<span class="sd"> composed of several input planes, sometimes also called "deconvolution"</span>
<span class="sd"> See :class:`~torch.nn.ConvTranspose3d` for details and output shape.</span>
<span class="sd"> Args:</span>
<span class="sd"> input: input tensor of shape (minibatch x in_channels x iT x iH x iW)</span>
<span class="sd"> weight: filters of shape (in_channels x out_channels x kH x kW)</span>
<span class="sd"> bias: optional bias of shape (out_channels)</span>
<span class="sd"> stride: the stride of the convolving kernel, a single number or a</span>
<span class="sd"> tuple (sh x sw). Default: 1</span>
<span class="sd"> padding: implicit zero padding on the input, a single number or a</span>
<span class="sd"> tuple (padh x padw). Default: 0</span>
<span class="sd"> """</span>
<span class="n">f</span> <span class="o">=</span> <span class="n">ConvNd</span><span class="p">(</span><span class="n">_triple</span><span class="p">(</span><span class="n">stride</span><span class="p">),</span> <span class="n">_triple</span><span class="p">(</span><span class="n">padding</span><span class="p">),</span> <span class="n">_triple</span><span class="p">(</span><span class="mi">1</span><span class="p">),</span> <span class="kc">True</span><span class="p">,</span>
<span class="n">_triple</span><span class="p">(</span><span class="n">output_padding</span><span class="p">),</span> <span class="n">groups</span><span class="p">,</span> <span class="n">torch</span><span class="o">.</span><span class="n">backends</span><span class="o">.</span><span class="n">cudnn</span><span class="o">.</span><span class="n">benchmark</span><span class="p">,</span> <span class="n">torch</span><span class="o">.</span><span class="n">backends</span><span class="o">.</span><span class="n">cudnn</span><span class="o">.</span><span class="n">enabled</span><span class="p">)</span>
<span class="k">return</span> <span class="n">f</span><span class="p">(</span><span class="nb">input</span><span class="p">,</span> <span class="n">weight</span><span class="p">,</span> <span class="n">bias</span><span class="p">)</span></div>
<span class="c1"># Pooling</span>
<div class="viewcode-block" id="avg_pool1d"><a class="viewcode-back" href="../../../nn.html#torch.nn.functional.avg_pool1d">[docs]</a><span class="k">def</span> <span class="nf">avg_pool1d</span><span class="p">(</span><span class="nb">input</span><span class="p">,</span> <span class="n">kernel_size</span><span class="p">,</span> <span class="n">stride</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">padding</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span>
<span class="n">ceil_mode</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">count_include_pad</span><span class="o">=</span><span class="kc">True</span><span class="p">):</span>
<span class="sd">r"""Applies a 1D average pooling over an input signal composed of several</span>
<span class="sd"> input planes.</span>
<span class="sd"> See :class:`~torch.nn.AvgPool1d` for details and output shape.</span>
<span class="sd"> Args:</span>
<span class="sd"> kernel_size: the size of the window</span>
<span class="sd"> stride: the stride of the window. Default value is :attr:`kernel_size`</span>
<span class="sd"> padding: implicit zero padding to be added on both sides</span>
<span class="sd"> ceil_mode: when True, will use `ceil` instead of `floor` to compute the output shape</span>
<span class="sd"> count_include_pad: when True, will include the zero-padding in the averaging calculation</span>
<span class="sd"> Example:</span>
<span class="sd"> >>> # pool of square window of size=3, stride=2</span>
<span class="sd"> >>> input = Variable(torch.Tensor([[[1,2,3,4,5,6,7]]]))</span>
<span class="sd"> >>> F.avg_pool1d(input, kernel_size=3, stride=2)</span>
<span class="sd"> Variable containing:</span>
<span class="sd"> (0 ,.,.) =</span>
<span class="sd"> 2 4 6</span>
<span class="sd"> [torch.FloatTensor of size 1x1x3]</span>
<span class="sd"> """</span>
<span class="k">if</span> <span class="nb">input</span><span class="o">.</span><span class="n">dim</span><span class="p">()</span> <span class="o">!=</span> <span class="mi">3</span><span class="p">:</span>
<span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">'expected 3D input (got </span><span class="si">{}</span><span class="s1"> dimensions)'</span>
<span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="nb">input</span><span class="o">.</span><span class="n">dim</span><span class="p">()))</span>
<span class="n">kernel_size</span> <span class="o">=</span> <span class="n">_single</span><span class="p">(</span><span class="n">kernel_size</span><span class="p">)</span> <span class="o">+</span> <span class="p">(</span><span class="mi">1</span><span class="p">,)</span>
<span class="n">stride</span> <span class="o">=</span> <span class="n">_single</span><span class="p">(</span><span class="n">stride</span><span class="p">)</span> <span class="o">+</span> <span class="p">(</span><span class="mi">1</span><span class="p">,)</span> <span class="k">if</span> <span class="n">stride</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="k">else</span> <span class="n">kernel_size</span>
<span class="n">padding</span> <span class="o">=</span> <span class="n">_single</span><span class="p">(</span><span class="n">padding</span><span class="p">)</span> <span class="o">+</span> <span class="p">(</span><span class="mi">0</span><span class="p">,)</span>
<span class="n">f</span> <span class="o">=</span> <span class="n">_functions</span><span class="o">.</span><span class="n">thnn</span><span class="o">.</span><span class="n">AvgPool2d</span><span class="p">(</span><span class="n">kernel_size</span><span class="p">,</span> <span class="n">stride</span><span class="p">,</span> <span class="n">padding</span><span class="p">,</span>
<span class="n">ceil_mode</span><span class="p">,</span> <span class="n">count_include_pad</span><span class="p">)</span>
<span class="k">return</span> <span class="n">f</span><span class="p">(</span><span class="nb">input</span><span class="o">.</span><span class="n">unsqueeze</span><span class="p">(</span><span class="mi">3</span><span class="p">))</span><span class="o">.</span><span class="n">squeeze</span><span class="p">(</span><span class="mi">3</span><span class="p">)</span></div>
<div class="viewcode-block" id="avg_pool2d"><a class="viewcode-back" href="../../../nn.html#torch.nn.functional.avg_pool2d">[docs]</a><span class="k">def</span> <span class="nf">avg_pool2d</span><span class="p">(</span><span class="nb">input</span><span class="p">,</span> <span class="n">kernel_size</span><span class="p">,</span> <span class="n">stride</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">padding</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span>
<span class="n">ceil_mode</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">count_include_pad</span><span class="o">=</span><span class="kc">True</span><span class="p">):</span>
<span class="sd">"""Applies 2D average-pooling operation in kh x kw regions by step size</span>
<span class="sd"> dh x dw steps. The number of output features is equal to the number of</span>
<span class="sd"> input planes.</span>
<span class="sd"> See :class:`~torch.nn.AvgPool2d` for details and output shape.</span>
<span class="sd"> Args:</span>
<span class="sd"> input: input tensor (minibatch x in_channels x iH x iW)</span>
<span class="sd"> kernel_size: size of the pooling region, a single number or a</span>
<span class="sd"> tuple (kh x kw)</span>
<span class="sd"> stride: stride of the pooling operation, a single number or a</span>
<span class="sd"> tuple (sh x sw). Default is equal to kernel size</span>
<span class="sd"> padding: implicit zero padding on the input, a single number or</span>
<span class="sd"> a tuple (padh x padw), Default: 0</span>
<span class="sd"> ceil_mode: operation that defines spatial output shape</span>
<span class="sd"> count_include_pad: divide by the number of elements inside the</span>
<span class="sd"> original non-padded image or kh * kw</span>
<span class="sd"> """</span>
<span class="k">return</span> <span class="n">_functions</span><span class="o">.</span><span class="n">thnn</span><span class="o">.</span><span class="n">AvgPool2d</span><span class="p">(</span><span class="n">kernel_size</span><span class="p">,</span> <span class="n">stride</span><span class="p">,</span> <span class="n">padding</span><span class="p">,</span>
<span class="n">ceil_mode</span><span class="p">,</span> <span class="n">count_include_pad</span><span class="p">)(</span><span class="nb">input</span><span class="p">)</span></div>
<div class="viewcode-block" id="avg_pool3d"><a class="viewcode-back" href="../../../nn.html#torch.nn.functional.avg_pool3d">[docs]</a><span class="k">def</span> <span class="nf">avg_pool3d</span><span class="p">(</span><span class="nb">input</span><span class="p">,</span> <span class="n">kernel_size</span><span class="p">,</span> <span class="n">stride</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
<span class="sd">"""Applies 3D average-pooling operation in kt x kh x kw regions by step</span>
<span class="sd"> size kt x dh x dw steps. The number of output features is equal to the</span>
<span class="sd"> number of input planes / dt.</span>
<span class="sd"> """</span>
<span class="k">return</span> <span class="n">_functions</span><span class="o">.</span><span class="n">thnn</span><span class="o">.</span><span class="n">AvgPool3d</span><span class="p">(</span><span class="n">kernel_size</span><span class="p">,</span> <span class="n">stride</span><span class="p">)(</span><span class="nb">input</span><span class="p">)</span></div>
<span class="c1"># share the same interface</span>
<div class="viewcode-block" id="max_pool1d"><a class="viewcode-back" href="../../../nn.html#torch.nn.functional.max_pool1d">[docs]</a><span class="k">def</span> <span class="nf">max_pool1d</span><span class="p">(</span><span class="nb">input</span><span class="p">,</span> <span class="n">kernel_size</span><span class="p">,</span> <span class="n">stride</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">padding</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">dilation</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
<span class="n">ceil_mode</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">return_indices</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
<span class="k">return</span> <span class="n">_functions</span><span class="o">.</span><span class="n">thnn</span><span class="o">.</span><span class="n">MaxPool1d</span><span class="p">(</span><span class="n">kernel_size</span><span class="p">,</span> <span class="n">stride</span><span class="p">,</span> <span class="n">padding</span><span class="p">,</span> <span class="n">dilation</span><span class="p">,</span>
<span class="n">return_indices</span><span class="p">,</span> <span class="n">ceil_mode</span><span class="p">)(</span><span class="nb">input</span><span class="p">)</span></div>
<div class="viewcode-block" id="max_pool2d"><a class="viewcode-back" href="../../../nn.html#torch.nn.functional.max_pool2d">[docs]</a><span class="k">def</span> <span class="nf">max_pool2d</span><span class="p">(</span><span class="nb">input</span><span class="p">,</span> <span class="n">kernel_size</span><span class="p">,</span> <span class="n">stride</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">padding</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">dilation</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
<span class="n">ceil_mode</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">return_indices</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
<span class="k">return</span> <span class="n">_functions</span><span class="o">.</span><span class="n">thnn</span><span class="o">.</span><span class="n">MaxPool2d</span><span class="p">(</span><span class="n">kernel_size</span><span class="p">,</span> <span class="n">stride</span><span class="p">,</span> <span class="n">padding</span><span class="p">,</span> <span class="n">dilation</span><span class="p">,</span>
<span class="n">return_indices</span><span class="p">,</span> <span class="n">ceil_mode</span><span class="p">)(</span><span class="nb">input</span><span class="p">)</span></div>
<div class="viewcode-block" id="max_pool3d"><a class="viewcode-back" href="../../../nn.html#torch.nn.functional.max_pool3d">[docs]</a><span class="k">def</span> <span class="nf">max_pool3d</span><span class="p">(</span><span class="nb">input</span><span class="p">,</span> <span class="n">kernel_size</span><span class="p">,</span> <span class="n">stride</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">padding</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">dilation</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
<span class="n">ceil_mode</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">return_indices</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
<span class="k">return</span> <span class="n">_functions</span><span class="o">.</span><span class="n">thnn</span><span class="o">.</span><span class="n">MaxPool3d</span><span class="p">(</span><span class="n">kernel_size</span><span class="p">,</span> <span class="n">stride</span><span class="p">,</span> <span class="n">padding</span><span class="p">,</span> <span class="n">dilation</span><span class="p">,</span>
<span class="n">return_indices</span><span class="p">,</span> <span class="n">ceil_mode</span><span class="p">)(</span><span class="nb">input</span><span class="p">)</span></div>
<span class="k">def</span> <span class="nf">_unpool_output_size</span><span class="p">(</span><span class="nb">input</span><span class="p">,</span> <span class="n">kernel_size</span><span class="p">,</span> <span class="n">stride</span><span class="p">,</span> <span class="n">padding</span><span class="p">,</span> <span class="n">output_size</span><span class="p">):</span>
<span class="n">input_size</span> <span class="o">=</span> <span class="nb">input</span><span class="o">.</span><span class="n">size</span><span class="p">()</span>
<span class="n">default_size</span> <span class="o">=</span> <span class="p">[]</span>
<span class="k">for</span> <span class="n">d</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">kernel_size</span><span class="p">)):</span>
<span class="n">default_size</span><span class="o">.</span><span class="n">append</span><span class="p">((</span><span class="n">input_size</span><span class="p">[</span><span class="n">d</span> <span class="o">+</span> <span class="mi">2</span><span class="p">]</span> <span class="o">-</span> <span class="mi">1</span><span class="p">)</span> <span class="o">*</span> <span class="n">stride</span><span class="p">[</span><span class="n">d</span><span class="p">]</span> <span class="o">+</span>
<span class="n">kernel_size</span><span class="p">[</span><span class="n">d</span><span class="p">]</span> <span class="o">-</span> <span class="mi">2</span> <span class="o">*</span> <span class="n">padding</span><span class="p">[</span><span class="n">d</span><span class="p">])</span>
<span class="k">if</span> <span class="n">output_size</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
<span class="k">return</span> <span class="n">default_size</span>
<span class="n">output_size</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="n">output_size</span><span class="p">)</span>
<span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">output_size</span><span class="p">)</span> <span class="o">==</span> <span class="nb">len</span><span class="p">(</span><span class="n">kernel_size</span><span class="p">)</span> <span class="o">+</span> <span class="mi">2</span><span class="p">:</span>
<span class="n">output_size</span> <span class="o">=</span> <span class="n">output_size</span><span class="p">[</span><span class="mi">2</span><span class="p">:]</span>
<span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">output_size</span><span class="p">)</span> <span class="o">!=</span> <span class="nb">len</span><span class="p">(</span><span class="n">kernel_size</span><span class="p">):</span>
<span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">"output_size should be a sequence containing "</span>
<span class="s2">"</span><span class="si">{}</span><span class="s2"> or </span><span class="si">{}</span><span class="s2"> elements, but it has a length of '</span><span class="si">{}</span><span class="s2">'"</span>
<span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">kernel_size</span><span class="p">),</span> <span class="nb">len</span><span class="p">(</span><span class="n">kernel_size</span><span class="p">)</span> <span class="o">+</span> <span class="mi">2</span><span class="p">,</span>
<span class="nb">len</span><span class="p">(</span><span class="n">output_size</span><span class="p">)))</span>
<span class="k">for</span> <span class="n">d</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">kernel_size</span><span class="p">)):</span>
<span class="n">min_size</span> <span class="o">=</span> <span class="n">default_size</span><span class="p">[</span><span class="n">d</span><span class="p">]</span> <span class="o">-</span> <span class="n">stride</span><span class="p">[</span><span class="n">d</span><span class="p">]</span>
<span class="n">max_size</span> <span class="o">=</span> <span class="n">default_size</span><span class="p">[</span><span class="n">d</span><span class="p">]</span> <span class="o">+</span> <span class="n">stride</span><span class="p">[</span><span class="n">d</span><span class="p">]</span>
<span class="k">if</span> <span class="ow">not</span> <span class="p">(</span><span class="n">min_size</span> <span class="o"><</span> <span class="n">output_size</span><span class="p">[</span><span class="n">d</span><span class="p">]</span> <span class="o"><</span> <span class="n">max_size</span><span class="p">):</span>
<span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span>
<span class="s1">'invalid output_size "</span><span class="si">{}</span><span class="s1">" (dim </span><span class="si">{}</span><span class="s1"> must be between </span><span class="si">{}</span><span class="s1"> and </span><span class="si">{}</span><span class="s1">)'</span>
<span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">output_size</span><span class="p">,</span> <span class="n">d</span><span class="p">,</span> <span class="n">min_size</span><span class="p">,</span> <span class="n">max_size</span><span class="p">))</span>
<span class="k">return</span> <span class="n">output_size</span>
<div class="viewcode-block" id="max_unpool1d"><a class="viewcode-back" href="../../../nn.html#torch.nn.functional.max_unpool1d">[docs]</a><span class="k">def</span> <span class="nf">max_unpool1d</span><span class="p">(</span><span class="nb">input</span><span class="p">,</span> <span class="n">indices</span><span class="p">,</span> <span class="n">kernel_size</span><span class="p">,</span> <span class="n">stride</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">padding</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span>
<span class="n">output_size</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
<span class="n">kernel_size</span> <span class="o">=</span> <span class="n">_single</span><span class="p">(</span><span class="n">kernel_size</span><span class="p">)</span>
<span class="n">stride</span> <span class="o">=</span> <span class="n">_single</span><span class="p">(</span><span class="n">stride</span><span class="p">)</span>
<span class="n">padding</span> <span class="o">=</span> <span class="n">_single</span><span class="p">(</span><span class="n">padding</span><span class="p">)</span>
<span class="n">output_size</span> <span class="o">=</span> <span class="n">_unpool_output_size</span><span class="p">(</span><span class="nb">input</span><span class="p">,</span> <span class="n">kernel_size</span><span class="p">,</span> <span class="n">stride</span><span class="p">,</span> <span class="n">padding</span><span class="p">,</span>
<span class="n">output_size</span><span class="p">)</span>
<span class="n">f</span> <span class="o">=</span> <span class="n">_functions</span><span class="o">.</span><span class="n">thnn</span><span class="o">.</span><span class="n">MaxUnpool2d</span><span class="p">(</span><span class="n">output_size</span> <span class="o">+</span> <span class="p">[</span><span class="mi">1</span><span class="p">])</span>
<span class="k">return</span> <span class="n">f</span><span class="p">(</span><span class="nb">input</span><span class="o">.</span><span class="n">unsqueeze</span><span class="p">(</span><span class="mi">3</span><span class="p">),</span> <span class="n">indices</span><span class="o">.</span><span class="n">unsqueeze</span><span class="p">(</span><span class="mi">3</span><span class="p">))</span><span class="o">.</span><span class="n">squeeze</span><span class="p">(</span><span class="mi">3</span><span class="p">)</span></div>
<div class="viewcode-block" id="max_unpool2d"><a class="viewcode-back" href="../../../nn.html#torch.nn.functional.max_unpool2d">[docs]</a><span class="k">def</span> <span class="nf">max_unpool2d</span><span class="p">(</span><span class="nb">input</span><span class="p">,</span> <span class="n">indices</span><span class="p">,</span> <span class="n">kernel_size</span><span class="p">,</span> <span class="n">stride</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">padding</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span>
<span class="n">output_size</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
<span class="n">kernel_size</span> <span class="o">=</span> <span class="n">_pair</span><span class="p">(</span><span class="n">kernel_size</span><span class="p">)</span>
<span class="n">stride</span> <span class="o">=</span> <span class="n">_pair</span><span class="p">(</span><span class="n">stride</span><span class="p">)</span>
<span class="n">padding</span> <span class="o">=</span> <span class="n">_pair</span><span class="p">(</span><span class="n">padding</span><span class="p">)</span>
<span class="n">output_size</span> <span class="o">=</span> <span class="n">_unpool_output_size</span><span class="p">(</span><span class="nb">input</span><span class="p">,</span> <span class="n">kernel_size</span><span class="p">,</span> <span class="n">stride</span><span class="p">,</span> <span class="n">padding</span><span class="p">,</span>
<span class="n">output_size</span><span class="p">)</span>
<span class="n">f</span> <span class="o">=</span> <span class="n">_functions</span><span class="o">.</span><span class="n">thnn</span><span class="o">.</span><span class="n">MaxUnpool2d</span><span class="p">(</span><span class="n">output_size</span><span class="p">)</span>
<span class="k">return</span> <span class="n">f</span><span class="p">(</span><span class="nb">input</span><span class="p">,</span> <span class="n">indices</span><span class="p">)</span></div>
<div class="viewcode-block" id="max_unpool3d"><a class="viewcode-back" href="../../../nn.html#torch.nn.functional.max_unpool3d">[docs]</a><span class="k">def</span> <span class="nf">max_unpool3d</span><span class="p">(</span><span class="nb">input</span><span class="p">,</span> <span class="n">indices</span><span class="p">,</span> <span class="n">kernel_size</span><span class="p">,</span> <span class="n">stride</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">padding</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span>
<span class="n">output_size</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
<span class="n">kernel_size</span> <span class="o">=</span> <span class="n">_triple</span><span class="p">(</span><span class="n">kernel_size</span><span class="p">)</span>
<span class="n">stride</span> <span class="o">=</span> <span class="n">_triple</span><span class="p">(</span><span class="n">stride</span><span class="p">)</span>
<span class="n">padding</span> <span class="o">=</span> <span class="n">_triple</span><span class="p">(</span><span class="n">padding</span><span class="p">)</span>
<span class="n">output_size</span> <span class="o">=</span> <span class="n">_unpool_output_size</span><span class="p">(</span><span class="nb">input</span><span class="p">,</span> <span class="n">kernel_size</span><span class="p">,</span> <span class="n">stride</span><span class="p">,</span> <span class="n">padding</span><span class="p">,</span>
<span class="n">output_size</span><span class="p">)</span>
<span class="n">f</span> <span class="o">=</span> <span class="n">_functions</span><span class="o">.</span><span class="n">thnn</span><span class="o">.</span><span class="n">MaxUnpool3d</span><span class="p">(</span><span class="n">output_size</span><span class="p">,</span> <span class="n">stride</span><span class="p">,</span> <span class="n">padding</span><span class="p">)</span>
<span class="k">return</span> <span class="n">f</span><span class="p">(</span><span class="nb">input</span><span class="p">,</span> <span class="n">indices</span><span class="p">)</span></div>
<div class="viewcode-block" id="lp_pool2d"><a class="viewcode-back" href="../../../nn.html#torch.nn.functional.lp_pool2d">[docs]</a><span class="k">def</span> <span class="nf">lp_pool2d</span><span class="p">(</span><span class="nb">input</span><span class="p">,</span> <span class="n">norm_type</span><span class="p">,</span> <span class="n">kernel_size</span><span class="p">,</span> <span class="n">stride</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">ceil_mode</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
<span class="n">kw</span><span class="p">,</span> <span class="n">kh</span> <span class="o">=</span> <span class="n">utils</span><span class="o">.</span><span class="n">_pair</span><span class="p">(</span><span class="n">kernel_size</span><span class="p">)</span>
<span class="n">out</span> <span class="o">=</span> <span class="n">avg_pool2d</span><span class="p">(</span><span class="nb">input</span><span class="o">.</span><span class="n">pow</span><span class="p">(</span><span class="n">norm_type</span><span class="p">),</span> <span class="n">kernel_size</span><span class="p">,</span> <span class="n">stride</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="n">ceil_mode</span><span class="p">)</span>
<span class="k">return</span> <span class="n">out</span><span class="o">.</span><span class="n">mul</span><span class="p">(</span><span class="n">kw</span> <span class="o">*</span> <span class="n">kh</span><span class="p">)</span><span class="o">.</span><span class="n">pow</span><span class="p">(</span><span class="mf">1.</span> <span class="o">/</span> <span class="n">norm_type</span><span class="p">)</span></div>
<div class="viewcode-block" id="adaptive_max_pool1d"><a class="viewcode-back" href="../../../nn.html#torch.nn.functional.adaptive_max_pool1d">[docs]</a><span class="k">def</span> <span class="nf">adaptive_max_pool1d</span><span class="p">(</span><span class="nb">input</span><span class="p">,</span> <span class="n">output_size</span><span class="p">,</span> <span class="n">return_indices</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
<span class="sd">r"""Applies a 1D adaptive max pooling over an input signal composed of</span>
<span class="sd"> several input planes.</span>
<span class="sd"> See :class:`~torch.nn.AdaptiveMaxPool1d` for details and output shape.</span>
<span class="sd"> Args:</span>
<span class="sd"> output_size: the target output size (single integer)</span>
<span class="sd"> return_indices: whether to return pooling indices</span>
<span class="sd"> """</span>
<span class="k">return</span> <span class="n">_functions</span><span class="o">.</span><span class="n">thnn</span><span class="o">.</span><span class="n">AdaptiveMaxPool1d</span><span class="p">(</span><span class="n">output_size</span><span class="p">,</span> <span class="n">return_indices</span><span class="p">)(</span><span class="nb">input</span><span class="p">)</span></div>
<div class="viewcode-block" id="adaptive_max_pool2d"><a class="viewcode-back" href="../../../nn.html#torch.nn.functional.adaptive_max_pool2d">[docs]</a><span class="k">def</span> <span class="nf">adaptive_max_pool2d</span><span class="p">(</span><span class="nb">input</span><span class="p">,</span> <span class="n">output_size</span><span class="p">,</span> <span class="n">return_indices</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
<span class="sd">r"""Applies a 2D adaptive max pooling over an input signal composed of</span>
<span class="sd"> several input planes.</span>
<span class="sd"> See :class:`~torch.nn.AdaptiveMaxPool2d` for details and output shape.</span>
<span class="sd"> Args:</span>
<span class="sd"> output_size: the target output size (single integer or double-integer tuple)</span>
<span class="sd"> return_indices: whether to return pooling indices</span>
<span class="sd"> """</span>
<span class="k">return</span> <span class="n">_functions</span><span class="o">.</span><span class="n">thnn</span><span class="o">.</span><span class="n">AdaptiveMaxPool2d</span><span class="p">(</span><span class="n">output_size</span><span class="p">,</span> <span class="n">return_indices</span><span class="p">)(</span><span class="nb">input</span><span class="p">)</span></div>
<div class="viewcode-block" id="adaptive_avg_pool1d"><a class="viewcode-back" href="../../../nn.html#torch.nn.functional.adaptive_avg_pool1d">[docs]</a><span class="k">def</span> <span class="nf">adaptive_avg_pool1d</span><span class="p">(</span><span class="nb">input</span><span class="p">,</span> <span class="n">output_size</span><span class="p">):</span>
<span class="sd">r"""Applies a 1D adaptive average pooling over an input signal composed of</span>
<span class="sd"> several input planes.</span>
<span class="sd"> See :class:`~torch.nn.AdaptiveAvgPool1d` for details and output shape.</span>
<span class="sd"> Args:</span>
<span class="sd"> output_size: the target output size (single integer)</span>
<span class="sd"> """</span>
<span class="k">return</span> <span class="n">_functions</span><span class="o">.</span><span class="n">thnn</span><span class="o">.</span><span class="n">AdaptiveAvgPool1d</span><span class="p">(</span><span class="n">output_size</span><span class="p">)(</span><span class="nb">input</span><span class="p">)</span></div>
<div class="viewcode-block" id="adaptive_avg_pool2d"><a class="viewcode-back" href="../../../nn.html#torch.nn.functional.adaptive_avg_pool2d">[docs]</a><span class="k">def</span> <span class="nf">adaptive_avg_pool2d</span><span class="p">(</span><span class="nb">input</span><span class="p">,</span> <span class="n">output_size</span><span class="p">):</span>
<span class="sd">r"""Applies a 2D adaptive average pooling over an input signal composed of</span>
<span class="sd"> several input planes.</span>
<span class="sd"> See :class:`~torch.nn.AdaptiveAvgPool2d` for details and output shape.</span>
<span class="sd"> Args:</span>
<span class="sd"> output_size: the target output size (single integer or double-integer tuple)</span>
<span class="sd"> """</span>
<span class="k">return</span> <span class="n">_functions</span><span class="o">.</span><span class="n">thnn</span><span class="o">.</span><span class="n">AdaptiveAvgPool2d</span><span class="p">(</span><span class="n">output_size</span><span class="p">)(</span><span class="nb">input</span><span class="p">)</span></div>
<span class="c1"># Activation functions</span>
<div class="viewcode-block" id="dropout"><a class="viewcode-back" href="../../../nn.html#torch.nn.functional.dropout">[docs]</a><span class="k">def</span> <span class="nf">dropout</span><span class="p">(</span><span class="nb">input</span><span class="p">,</span> <span class="n">p</span><span class="o">=</span><span class="mf">0.5</span><span class="p">,</span> <span class="n">training</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">inplace</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
<span class="k">return</span> <span class="n">_functions</span><span class="o">.</span><span class="n">dropout</span><span class="o">.</span><span class="n">Dropout</span><span class="p">(</span><span class="n">p</span><span class="p">,</span> <span class="n">training</span><span class="p">,</span> <span class="n">inplace</span><span class="p">)(</span><span class="nb">input</span><span class="p">)</span></div>
<div class="viewcode-block" id="threshold"><a class="viewcode-back" href="../../../nn.html#torch.nn.functional.threshold">[docs]</a><span class="k">def</span> <span class="nf">threshold</span><span class="p">(</span><span class="nb">input</span><span class="p">,</span> <span class="n">threshold</span><span class="p">,</span> <span class="n">value</span><span class="p">,</span> <span class="n">inplace</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
<span class="k">return</span> <span class="n">_functions</span><span class="o">.</span><span class="n">thnn</span><span class="o">.</span><span class="n">auto</span><span class="o">.</span><span class="n">Threshold</span><span class="p">(</span><span class="n">threshold</span><span class="p">,</span> <span class="n">value</span><span class="p">,</span> <span class="n">inplace</span><span class="p">)(</span><span class="nb">input</span><span class="p">)</span></div>
<div class="viewcode-block" id="relu"><a class="viewcode-back" href="../../../nn.html#torch.nn.functional.relu">[docs]</a><span class="k">def</span> <span class="nf">relu</span><span class="p">(</span><span class="nb">input</span><span class="p">,</span> <span class="n">inplace</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
<span class="k">return</span> <span class="n">_functions</span><span class="o">.</span><span class="n">thnn</span><span class="o">.</span><span class="n">auto</span><span class="o">.</span><span class="n">Threshold</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="n">inplace</span><span class="p">)(</span><span class="nb">input</span><span class="p">)</span></div>
<div class="viewcode-block" id="hardtanh"><a class="viewcode-back" href="../../../nn.html#torch.nn.functional.hardtanh">[docs]</a><span class="k">def</span> <span class="nf">hardtanh</span><span class="p">(</span><span class="nb">input</span><span class="p">,</span> <span class="n">min_val</span><span class="o">=-</span><span class="mf">1.</span><span class="p">,</span> <span class="n">max_val</span><span class="o">=</span><span class="mf">1.</span><span class="p">,</span> <span class="n">inplace</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
<span class="k">return</span> <span class="n">_functions</span><span class="o">.</span><span class="n">thnn</span><span class="o">.</span><span class="n">auto</span><span class="o">.</span><span class="n">Hardtanh</span><span class="p">(</span><span class="n">min_val</span><span class="p">,</span> <span class="n">max_val</span><span class="p">,</span> <span class="n">inplace</span><span class="p">)(</span><span class="nb">input</span><span class="p">)</span></div>
<div class="viewcode-block" id="relu6"><a class="viewcode-back" href="../../../nn.html#torch.nn.functional.relu6">[docs]</a><span class="k">def</span> <span class="nf">relu6</span><span class="p">(</span><span class="nb">input</span><span class="p">,</span> <span class="n">inplace</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
<span class="k">return</span> <span class="n">_functions</span><span class="o">.</span><span class="n">thnn</span><span class="o">.</span><span class="n">auto</span><span class="o">.</span><span class="n">Hardtanh</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">6</span><span class="p">,</span> <span class="n">inplace</span><span class="p">)(</span><span class="nb">input</span><span class="p">)</span></div>
<div class="viewcode-block" id="elu"><a class="viewcode-back" href="../../../nn.html#torch.nn.functional.elu">[docs]</a><span class="k">def</span> <span class="nf">elu</span><span class="p">(</span><span class="nb">input</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">1.</span><span class="p">,</span> <span class="n">inplace</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
<span class="k">return</span> <span class="n">_functions</span><span class="o">.</span><span class="n">thnn</span><span class="o">.</span><span class="n">auto</span><span class="o">.</span><span class="n">ELU</span><span class="p">(</span><span class="n">alpha</span><span class="p">,</span> <span class="n">inplace</span><span class="p">)(</span><span class="nb">input</span><span class="p">)</span></div>
<div class="viewcode-block" id="leaky_relu"><a class="viewcode-back" href="../../../nn.html#torch.nn.functional.leaky_relu">[docs]</a><span class="k">def</span> <span class="nf">leaky_relu</span><span class="p">(</span><span class="nb">input</span><span class="p">,</span> <span class="n">negative_slope</span><span class="o">=</span><span class="mi">1</span><span class="n">e</span><span class="o">-</span><span class="mi">2</span><span class="p">,</span> <span class="n">inplace</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
<span class="k">return</span> <span class="n">_functions</span><span class="o">.</span><span class="n">thnn</span><span class="o">.</span><span class="n">auto</span><span class="o">.</span><span class="n">LeakyReLU</span><span class="p">(</span><span class="n">negative_slope</span><span class="p">,</span> <span class="n">inplace</span><span class="p">)(</span><span class="nb">input</span><span class="p">)</span></div>
<div class="viewcode-block" id="prelu"><a class="viewcode-back" href="../../../nn.html#torch.nn.functional.prelu">[docs]</a><span class="k">def</span> <span class="nf">prelu</span><span class="p">(</span><span class="nb">input</span><span class="p">,</span> <span class="n">weight</span><span class="p">):</span>
<span class="k">return</span> <span class="n">_functions</span><span class="o">.</span><span class="n">thnn</span><span class="o">.</span><span class="n">PReLU</span><span class="p">()(</span><span class="nb">input</span><span class="p">,</span> <span class="n">weight</span><span class="p">)</span></div>
<div class="viewcode-block" id="rrelu"><a class="viewcode-back" href="../../../nn.html#torch.nn.functional.rrelu">[docs]</a><span class="k">def</span> <span class="nf">rrelu</span><span class="p">(</span><span class="nb">input</span><span class="p">,</span> <span class="n">lower</span><span class="o">=</span><span class="mf">1.</span> <span class="o">/</span> <span class="mi">8</span><span class="p">,</span> <span class="n">upper</span><span class="o">=</span><span class="mf">1.</span> <span class="o">/</span> <span class="mi">3</span><span class="p">,</span> <span class="n">training</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">inplace</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
<span class="k">return</span> <span class="n">_functions</span><span class="o">.</span><span class="n">thnn</span><span class="o">.</span><span class="n">RReLU</span><span class="p">(</span><span class="n">lower</span><span class="p">,</span> <span class="n">upper</span><span class="p">,</span> <span class="n">training</span><span class="p">,</span> <span class="n">inplace</span><span class="p">)(</span><span class="nb">input</span><span class="p">)</span></div>
<div class="viewcode-block" id="logsigmoid"><a class="viewcode-back" href="../../../nn.html#torch.nn.functional.logsigmoid">[docs]</a><span class="k">def</span> <span class="nf">logsigmoid</span><span class="p">(</span><span class="nb">input</span><span class="p">):</span>
<span class="k">return</span> <span class="n">_functions</span><span class="o">.</span><span class="n">thnn</span><span class="o">.</span><span class="n">LogSigmoid</span><span class="p">()(</span><span class="nb">input</span><span class="p">)</span></div>
<div class="viewcode-block" id="hardshrink"><a class="viewcode-back" href="../../../nn.html#torch.nn.functional.hardshrink">[docs]</a><span class="k">def</span> <span class="nf">hardshrink</span><span class="p">(</span><span class="nb">input</span><span class="p">,</span> <span class="n">lambd</span><span class="o">=</span><span class="mf">0.5</span><span class="p">):</span>
<span class="k">return</span> <span class="n">_functions</span><span class="o">.</span><span class="n">thnn</span><span class="o">.</span><span class="n">auto</span><span class="o">.</span><span class="n">Hardshrink</span><span class="p">(</span><span class="n">lambd</span><span class="p">)(</span><span class="nb">input</span><span class="p">)</span></div>
<div class="viewcode-block" id="tanhshrink"><a class="viewcode-back" href="../../../nn.html#torch.nn.functional.tanhshrink">[docs]</a><span class="k">def</span> <span class="nf">tanhshrink</span><span class="p">(</span><span class="nb">input</span><span class="p">):</span>
<span class="k">return</span> <span class="nb">input</span> <span class="o">-</span> <span class="n">torch</span><span class="o">.</span><span class="n">tanh</span><span class="p">(</span><span class="nb">input</span><span class="p">)</span></div>
<div class="viewcode-block" id="softsign"><a class="viewcode-back" href="../../../nn.html#torch.nn.functional.softsign">[docs]</a><span class="k">def</span> <span class="nf">softsign</span><span class="p">(</span><span class="nb">input</span><span class="p">):</span>
<span class="k">return</span> <span class="n">_functions</span><span class="o">.</span><span class="n">activation</span><span class="o">.</span><span class="n">Softsign</span><span class="p">()(</span><span class="nb">input</span><span class="p">)</span></div>
<div class="viewcode-block" id="softplus"><a class="viewcode-back" href="../../../nn.html#torch.nn.functional.softplus">[docs]</a><span class="k">def</span> <span class="nf">softplus</span><span class="p">(</span><span class="nb">input</span><span class="p">,</span> <span class="n">beta</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">threshold</span><span class="o">=</span><span class="mi">20</span><span class="p">):</span>
<span class="k">return</span> <span class="n">_functions</span><span class="o">.</span><span class="n">thnn</span><span class="o">.</span><span class="n">auto</span><span class="o">.</span><span class="n">Softplus</span><span class="p">(</span><span class="n">beta</span><span class="p">,</span> <span class="n">threshold</span><span class="p">)(</span><span class="nb">input</span><span class="p">)</span></div>
<div class="viewcode-block" id="softmin"><a class="viewcode-back" href="../../../nn.html#torch.nn.functional.softmin">[docs]</a><span class="k">def</span> <span class="nf">softmin</span><span class="p">(</span><span class="nb">input</span><span class="p">):</span>
<span class="k">return</span> <span class="n">_functions</span><span class="o">.</span><span class="n">thnn</span><span class="o">.</span><span class="n">Softmin</span><span class="p">()(</span><span class="nb">input</span><span class="p">)</span></div>
<div class="viewcode-block" id="softmax"><a class="viewcode-back" href="../../../nn.html#torch.nn.functional.softmax">[docs]</a><span class="k">def</span> <span class="nf">softmax</span><span class="p">(</span><span class="nb">input</span><span class="p">):</span>
<span class="k">return</span> <span class="n">_functions</span><span class="o">.</span><span class="n">thnn</span><span class="o">.</span><span class="n">auto</span><span class="o">.</span><span class="n">Softmax</span><span class="p">()(</span><span class="nb">input</span><span class="p">)</span></div>
<div class="viewcode-block" id="softshrink"><a class="viewcode-back" href="../../../nn.html#torch.nn.functional.softshrink">[docs]</a><span class="k">def</span> <span class="nf">softshrink</span><span class="p">(</span><span class="nb">input</span><span class="p">,</span> <span class="n">lambd</span><span class="o">=</span><span class="mf">0.5</span><span class="p">):</span>
<span class="k">return</span> <span class="n">_functions</span><span class="o">.</span><span class="n">thnn</span><span class="o">.</span><span class="n">auto</span><span class="o">.</span><span class="n">Softshrink</span><span class="p">(</span><span class="n">lambd</span><span class="p">)(</span><span class="nb">input</span><span class="p">)</span></div>
<div class="viewcode-block" id="log_softmax"><a class="viewcode-back" href="../../../nn.html#torch.nn.functional.log_softmax">[docs]</a><span class="k">def</span> <span class="nf">log_softmax</span><span class="p">(</span><span class="nb">input</span><span class="p">):</span>
<span class="k">return</span> <span class="n">_functions</span><span class="o">.</span><span class="n">thnn</span><span class="o">.</span><span class="n">LogSoftmax</span><span class="p">()(</span><span class="nb">input</span><span class="p">)</span></div>
<div class="viewcode-block" id="tanh"><a class="viewcode-back" href="../../../nn.html#torch.nn.functional.tanh">[docs]</a><span class="k">def</span> <span class="nf">tanh</span><span class="p">(</span><span class="nb">input</span><span class="p">):</span>
<span class="k">return</span> <span class="n">torch</span><span class="o">.</span><span class="n">tanh</span><span class="p">(</span><span class="nb">input</span><span class="p">)</span></div>
<div class="viewcode-block" id="sigmoid"><a class="viewcode-back" href="../../../nn.html#torch.nn.functional.sigmoid">[docs]</a><span class="k">def</span> <span class="nf">sigmoid</span><span class="p">(</span><span class="nb">input</span><span class="p">):</span>
<span class="k">return</span> <span class="n">torch</span><span class="o">.</span><span class="n">sigmoid</span><span class="p">(</span><span class="nb">input</span><span class="p">)</span></div>
<span class="c1"># etc.</span>
<div class="viewcode-block" id="linear"><a class="viewcode-back" href="../../../nn.html#torch.nn.functional.linear">[docs]</a><span class="k">def</span> <span class="nf">linear</span><span class="p">(</span><span class="nb">input</span><span class="p">,</span> <span class="n">weight</span><span class="p">,</span> <span class="n">bias</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
<span class="n">state</span> <span class="o">=</span> <span class="n">_functions</span><span class="o">.</span><span class="n">linear</span><span class="o">.</span><span class="n">Linear</span><span class="p">()</span>
<span class="k">return</span> <span class="n">bias</span> <span class="ow">and</span> <span class="n">state</span><span class="p">(</span><span class="nb">input</span><span class="p">,</span> <span class="n">weight</span><span class="p">,</span> <span class="n">bias</span><span class="p">)</span> <span class="ow">or</span> <span class="n">state</span><span class="p">(</span><span class="nb">input</span><span class="p">,</span> <span class="n">weight</span><span class="p">)</span></div>
<div class="viewcode-block" id="batch_norm"><a class="viewcode-back" href="../../../nn.html#torch.nn.functional.batch_norm">[docs]</a><span class="k">def</span> <span class="nf">batch_norm</span><span class="p">(</span><span class="nb">input</span><span class="p">,</span> <span class="n">running_mean</span><span class="p">,</span> <span class="n">running_var</span><span class="p">,</span> <span class="n">weight</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">bias</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
<span class="n">training</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">momentum</span><span class="o">=</span><span class="mf">0.1</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">5</span><span class="p">):</span>
<span class="n">f</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">_C</span><span class="o">.</span><span class="n">_functions</span><span class="o">.</span><span class="n">BatchNorm</span><span class="p">(</span><span class="n">running_mean</span><span class="p">,</span> <span class="n">running_var</span><span class="p">,</span> <span class="n">training</span><span class="p">,</span> <span class="n">momentum</span><span class="p">,</span> <span class="n">eps</span><span class="p">,</span> <span class="n">torch</span><span class="o">.</span><span class="n">backends</span><span class="o">.</span><span class="n">cudnn</span><span class="o">.</span><span class="n">enabled</span><span class="p">)</span>
<span class="k">return</span> <span class="n">f</span><span class="p">(</span><span class="nb">input</span><span class="p">,</span> <span class="n">weight</span><span class="p">,</span> <span class="n">bias</span><span class="p">)</span></div>
<span class="c1"># loss</span>
<div class="viewcode-block" id="nll_loss"><a class="viewcode-back" href="../../../nn.html#torch.nn.functional.nll_loss">[docs]</a><span class="k">def</span> <span class="nf">nll_loss</span><span class="p">(</span><span class="nb">input</span><span class="p">,</span> <span class="n">target</span><span class="p">,</span> <span class="n">weight</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">size_average</span><span class="o">=</span><span class="kc">True</span><span class="p">):</span>
<span class="sd">r"""The negative log likelihood loss.</span>
<span class="sd"> See :class:`~torch.nn.NLLLoss` for details.</span>
<span class="sd"> Args:</span>
<span class="sd"> input: :math:`(N, C)` where `C = number of classes`</span>
<span class="sd"> target: :math:`(N)` where each value is `0 <= targets[i] <= C-1`</span>
<span class="sd"> weight (Variable, optional): a manual rescaling weight given to each</span>
<span class="sd"> class. If given, has to be a Variable of size "nclasses"</span>
<span class="sd"> size_average (bool, optional): By default, the losses are averaged</span>
<span class="sd"> over observations for each minibatch. However, if the field</span>
<span class="sd"> sizeAverage is set to False, the losses are instead summed</span>
<span class="sd"> for each minibatch.</span>
<span class="sd"> Attributes:</span>
<span class="sd"> weight: the class-weights given as input to the constructor</span>
<span class="sd"> Example:</span>
<span class="sd"> >>> # input is of size nBatch x nClasses = 3 x 5</span>
<span class="sd"> >>> input = autograd.Variable(torch.randn(3, 5))</span>
<span class="sd"> >>> # each element in target has to have 0 <= value < nclasses</span>
<span class="sd"> >>> target = autograd.Variable(torch.LongTensor([1, 0, 4]))</span>
<span class="sd"> >>> output = F.nll_loss(F.log_softmax(input), target)</span>
<span class="sd"> >>> output.backward()</span>
<span class="sd"> """</span>
<span class="n">dim</span> <span class="o">=</span> <span class="nb">input</span><span class="o">.</span><span class="n">dim</span><span class="p">()</span>
<span class="k">if</span> <span class="n">dim</span> <span class="o">==</span> <span class="mi">2</span><span class="p">:</span>
<span class="n">f</span> <span class="o">=</span> <span class="n">_functions</span><span class="o">.</span><span class="n">thnn</span><span class="o">.</span><span class="n">NLLLoss</span><span class="p">(</span><span class="n">size_average</span><span class="p">,</span> <span class="n">weight</span><span class="o">=</span><span class="n">weight</span><span class="p">)</span>
<span class="k">elif</span> <span class="n">dim</span> <span class="o">==</span> <span class="mi">4</span><span class="p">:</span>
<span class="n">f</span> <span class="o">=</span> <span class="n">_functions</span><span class="o">.</span><span class="n">thnn</span><span class="o">.</span><span class="n">NLLLoss2d</span><span class="p">(</span><span class="n">size_average</span><span class="p">,</span> <span class="n">weight</span><span class="o">=</span><span class="n">weight</span><span class="p">)</span>
<span class="k">else</span><span class="p">:</span>
<span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">'Expected 2 or 4 dimensions (got </span><span class="si">{}</span><span class="s1">)'</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">dim</span><span class="p">))</span>
<span class="k">return</span> <span class="n">f</span><span class="p">(</span><span class="nb">input</span><span class="p">,</span> <span class="n">target</span><span class="p">)</span></div>
<div class="viewcode-block" id="kl_div"><a class="viewcode-back" href="../../../nn.html#torch.nn.functional.kl_div">[docs]</a><span class="k">def</span> <span class="nf">kl_div</span><span class="p">(</span><span class="nb">input</span><span class="p">,</span> <span class="n">target</span><span class="p">,</span> <span class="n">size_average</span><span class="o">=</span><span class="kc">True</span><span class="p">):</span>
<span class="sd">r"""The `Kullback-Leibler divergence`_ Loss.</span>
<span class="sd"> See :class:`~torch.nn.KLDivLoss` for details.</span>
<span class="sd"> Args:</span>
<span class="sd"> input: Variable of arbitrary shape</span>
<span class="sd"> target: Variable of the same shape as input</span>
<span class="sd"> size_average: if True the output is divided by the number of elements</span>
<span class="sd"> in input tensor</span>
<span class="sd"> """</span>
<span class="k">return</span> <span class="n">_functions</span><span class="o">.</span><span class="n">thnn</span><span class="o">.</span><span class="n">KLDivLoss</span><span class="p">(</span><span class="n">size_average</span><span class="p">)(</span><span class="nb">input</span><span class="p">,</span> <span class="n">target</span><span class="p">)</span></div>
<div class="viewcode-block" id="cross_entropy"><a class="viewcode-back" href="../../../nn.html#torch.nn.functional.cross_entropy">[docs]</a><span class="k">def</span> <span class="nf">cross_entropy</span><span class="p">(</span><span class="nb">input</span><span class="p">,</span> <span class="n">target</span><span class="p">,</span> <span class="n">weight</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">size_average</span><span class="o">=</span><span class="kc">True</span><span class="p">):</span>