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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-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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<li class="toctree-l3"><a class="reference internal" href="notes/cuda.html#use-nn-dataparallel-instead-of-multiprocessing">Use nn.DataParallel instead of multiprocessing</a></li>
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<li class="toctree-l3"><a class="reference internal" href="notes/serialization.html#recommended-approach-for-saving-a-model">Recommended approach for saving a model</a></li>
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<p class="caption"><span class="caption-text">Package Reference</span></p>
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<li class="toctree-l3"><a class="reference internal" href="#maxunpool3d"><span class="hidden-section">MaxUnpool3d</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="#avgpool1d"><span class="hidden-section">AvgPool1d</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="#avgpool2d"><span class="hidden-section">AvgPool2d</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="#avgpool3d"><span class="hidden-section">AvgPool3d</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="#fractionalmaxpool2d"><span class="hidden-section">FractionalMaxPool2d</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="#lppool2d"><span class="hidden-section">LPPool2d</span></a></li>
</ul>
</li>
<li class="toctree-l2"><a class="reference internal" href="#non-linear-activations">Non-linear Activations</a><ul>
<li class="toctree-l3"><a class="reference internal" href="#relu"><span class="hidden-section">ReLU</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="#relu6"><span class="hidden-section">ReLU6</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="#elu"><span class="hidden-section">ELU</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="#prelu"><span class="hidden-section">PReLU</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="#leakyrelu"><span class="hidden-section">LeakyReLU</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="#threshold"><span class="hidden-section">Threshold</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="#hardtanh"><span class="hidden-section">Hardtanh</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="#sigmoid"><span class="hidden-section">Sigmoid</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="#tanh"><span class="hidden-section">Tanh</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="#logsigmoid"><span class="hidden-section">LogSigmoid</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="#softplus"><span class="hidden-section">Softplus</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="#softshrink"><span class="hidden-section">Softshrink</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="#softsign"><span class="hidden-section">Softsign</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="#tanhshrink"><span class="hidden-section">Tanhshrink</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="#softmin"><span class="hidden-section">Softmin</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="#softmax"><span class="hidden-section">Softmax</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="#logsoftmax"><span class="hidden-section">LogSoftmax</span></a></li>
</ul>
</li>
<li class="toctree-l2"><a class="reference internal" href="#normalization-layers">Normalization layers</a><ul>
<li class="toctree-l3"><a class="reference internal" href="#batchnorm1d"><span class="hidden-section">BatchNorm1d</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="#batchnorm2d"><span class="hidden-section">BatchNorm2d</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="#batchnorm3d"><span class="hidden-section">BatchNorm3d</span></a></li>
</ul>
</li>
<li class="toctree-l2"><a class="reference internal" href="#recurrent-layers">Recurrent layers</a><ul>
<li class="toctree-l3"><a class="reference internal" href="#rnn"><span class="hidden-section">RNN</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="#lstm"><span class="hidden-section">LSTM</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="#gru"><span class="hidden-section">GRU</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="#rnncell"><span class="hidden-section">RNNCell</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="#lstmcell"><span class="hidden-section">LSTMCell</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="#grucell"><span class="hidden-section">GRUCell</span></a></li>
</ul>
</li>
<li class="toctree-l2"><a class="reference internal" href="#linear-layers">Linear layers</a><ul>
<li class="toctree-l3"><a class="reference internal" href="#linear"><span class="hidden-section">Linear</span></a></li>
</ul>
</li>
<li class="toctree-l2"><a class="reference internal" href="#dropout-layers">Dropout layers</a><ul>
<li class="toctree-l3"><a class="reference internal" href="#dropout"><span class="hidden-section">Dropout</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="#dropout2d"><span class="hidden-section">Dropout2d</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="#dropout3d"><span class="hidden-section">Dropout3d</span></a></li>
</ul>
</li>
<li class="toctree-l2"><a class="reference internal" href="#sparse-layers">Sparse layers</a><ul>
<li class="toctree-l3"><a class="reference internal" href="#embedding"><span class="hidden-section">Embedding</span></a></li>
</ul>
</li>
<li class="toctree-l2"><a class="reference internal" href="#loss-functions">Loss functions</a><ul>
<li class="toctree-l3"><a class="reference internal" href="#l1loss"><span class="hidden-section">L1Loss</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="#mseloss"><span class="hidden-section">MSELoss</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="#crossentropyloss"><span class="hidden-section">CrossEntropyLoss</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="#nllloss"><span class="hidden-section">NLLLoss</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="#nllloss2d"><span class="hidden-section">NLLLoss2d</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="#kldivloss"><span class="hidden-section">KLDivLoss</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="#bceloss"><span class="hidden-section">BCELoss</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="#marginrankingloss"><span class="hidden-section">MarginRankingLoss</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="#hingeembeddingloss"><span class="hidden-section">HingeEmbeddingLoss</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="#multilabelmarginloss"><span class="hidden-section">MultiLabelMarginLoss</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="#smoothl1loss"><span class="hidden-section">SmoothL1Loss</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="#softmarginloss"><span class="hidden-section">SoftMarginLoss</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="#multilabelsoftmarginloss"><span class="hidden-section">MultiLabelSoftMarginLoss</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="#cosineembeddingloss"><span class="hidden-section">CosineEmbeddingLoss</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="#multimarginloss"><span class="hidden-section">MultiMarginLoss</span></a></li>
</ul>
</li>
<li class="toctree-l2"><a class="reference internal" href="#vision-layers">Vision layers</a><ul>
<li class="toctree-l3"><a class="reference internal" href="#pixelshuffle"><span class="hidden-section">PixelShuffle</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="#upsamplingnearest2d"><span class="hidden-section">UpsamplingNearest2d</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="#upsamplingbilinear2d"><span class="hidden-section">UpsamplingBilinear2d</span></a></li>
</ul>
</li>
<li class="toctree-l2"><a class="reference internal" href="#multi-gpu-layers">Multi-GPU layers</a><ul>
<li class="toctree-l3"><a class="reference internal" href="#dataparallel"><span class="hidden-section">DataParallel</span></a></li>
</ul>
</li>
<li class="toctree-l2"><a class="reference internal" href="#utilities">Utilities</a><ul>
<li class="toctree-l3"><a class="reference internal" href="#clip-grad-norm"><span class="hidden-section">clip_grad_norm</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="#packedsequence"><span class="hidden-section">PackedSequence</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="#pack-padded-sequence"><span class="hidden-section">pack_padded_sequence</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="#pad-packed-sequence"><span class="hidden-section">pad_packed_sequence</span></a></li>
</ul>
</li>
</ul>
</li>
<li class="toctree-l1"><a class="reference internal" href="#torch-nn-functional">torch.nn.functional</a><ul>
<li class="toctree-l2"><a class="reference internal" href="#convolution-functions">Convolution functions</a><ul>
<li class="toctree-l3"><a class="reference internal" href="#id13"><span class="hidden-section">conv1d</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="#id14"><span class="hidden-section">conv2d</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="#id15"><span class="hidden-section">conv3d</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="#conv-transpose1d"><span class="hidden-section">conv_transpose1d</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="#conv-transpose2d"><span class="hidden-section">conv_transpose2d</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="#conv-transpose3d"><span class="hidden-section">conv_transpose3d</span></a></li>
</ul>
</li>
<li class="toctree-l2"><a class="reference internal" href="#pooling-functions">Pooling functions</a><ul>
<li class="toctree-l3"><a class="reference internal" href="#avg-pool1d"><span class="hidden-section">avg_pool1d</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="#avg-pool2d"><span class="hidden-section">avg_pool2d</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="#avg-pool3d"><span class="hidden-section">avg_pool3d</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="#max-pool1d"><span class="hidden-section">max_pool1d</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="#max-pool2d"><span class="hidden-section">max_pool2d</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="#max-pool3d"><span class="hidden-section">max_pool3d</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="#max-unpool1d"><span class="hidden-section">max_unpool1d</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="#max-unpool2d"><span class="hidden-section">max_unpool2d</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="#max-unpool3d"><span class="hidden-section">max_unpool3d</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="#lp-pool2d"><span class="hidden-section">lp_pool2d</span></a></li>
</ul>
</li>
<li class="toctree-l2"><a class="reference internal" href="#non-linear-activation-functions">Non-linear activation functions</a><ul>
<li class="toctree-l3"><a class="reference internal" href="#id16"><span class="hidden-section">threshold</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="#id17"><span class="hidden-section">relu</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="#id18"><span class="hidden-section">hardtanh</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="#id19"><span class="hidden-section">relu6</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="#id20"><span class="hidden-section">elu</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="#leaky-relu"><span class="hidden-section">leaky_relu</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="#id21"><span class="hidden-section">prelu</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="#rrelu"><span class="hidden-section">rrelu</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="#id22"><span class="hidden-section">logsigmoid</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="#hardshrink"><span class="hidden-section">hardshrink</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="#id23"><span class="hidden-section">tanhshrink</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="#id24"><span class="hidden-section">softsign</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="#id25"><span class="hidden-section">softplus</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="#id26"><span class="hidden-section">softmin</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="#id27"><span class="hidden-section">softmax</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="#id28"><span class="hidden-section">softshrink</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="#log-softmax"><span class="hidden-section">log_softmax</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="#id29"><span class="hidden-section">tanh</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="#id30"><span class="hidden-section">sigmoid</span></a></li>
</ul>
</li>
<li class="toctree-l2"><a class="reference internal" href="#normalization-functions">Normalization functions</a><ul>
<li class="toctree-l3"><a class="reference internal" href="#batch-norm"><span class="hidden-section">batch_norm</span></a></li>
</ul>
</li>
<li class="toctree-l2"><a class="reference internal" href="#linear-functions">Linear functions</a><ul>
<li class="toctree-l3"><a class="reference internal" href="#id31"><span class="hidden-section">linear</span></a></li>
</ul>
</li>
<li class="toctree-l2"><a class="reference internal" href="#dropout-functions">Dropout functions</a><ul>
<li class="toctree-l3"><a class="reference internal" href="#id32"><span class="hidden-section">dropout</span></a></li>
</ul>
</li>
<li class="toctree-l2"><a class="reference internal" href="#id33">Loss functions</a><ul>
<li class="toctree-l3"><a class="reference internal" href="#nll-loss"><span class="hidden-section">nll_loss</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="#kl-div"><span class="hidden-section">kl_div</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="#cross-entropy"><span class="hidden-section">cross_entropy</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="#binary-cross-entropy"><span class="hidden-section">binary_cross_entropy</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="#smooth-l1-loss"><span class="hidden-section">smooth_l1_loss</span></a></li>
</ul>
</li>
<li class="toctree-l2"><a class="reference internal" href="#vision-functions">Vision functions</a><ul>
<li class="toctree-l3"><a class="reference internal" href="#pixel-shuffle"><span class="hidden-section">pixel_shuffle</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="#pad"><span class="hidden-section">pad</span></a></li>
</ul>
</li>
</ul>
</li>
<li class="toctree-l1"><a class="reference internal" href="#torch-nn-init">torch.nn.init</a></li>
<li class="toctree-l1"><a class="reference internal" href="optim.html">torch.optim</a><ul>
<li class="toctree-l2"><a class="reference internal" href="optim.html#how-to-use-an-optimizer">How to use an optimizer</a><ul>
<li class="toctree-l3"><a class="reference internal" href="optim.html#constructing-it">Constructing it</a></li>
<li class="toctree-l3"><a class="reference internal" href="optim.html#per-parameter-options">Per-parameter options</a></li>
<li class="toctree-l3"><a class="reference internal" href="optim.html#taking-an-optimization-step">Taking an optimization step</a><ul>
<li class="toctree-l4"><a class="reference internal" href="optim.html#optimizer-step"><code class="docutils literal"><span class="pre">optimizer.step()</span></code></a></li>
<li class="toctree-l4"><a class="reference internal" href="optim.html#optimizer-step-closure"><code class="docutils literal"><span class="pre">optimizer.step(closure)</span></code></a></li>
</ul>
</li>
</ul>
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<li class="toctree-l2"><a class="reference internal" href="optim.html#algorithms">Algorithms</a></li>
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<li class="toctree-l1"><a class="reference internal" href="autograd.html">torch.autograd</a><ul>
<li class="toctree-l2"><a class="reference internal" href="autograd.html#variable">Variable</a><ul>
<li class="toctree-l3"><a class="reference internal" href="autograd.html#api-compatibility">API compatibility</a></li>
<li class="toctree-l3"><a class="reference internal" href="autograd.html#in-place-operations-on-variables">In-place operations on Variables</a></li>
<li class="toctree-l3"><a class="reference internal" href="autograd.html#in-place-correctness-checks">In-place correctness checks</a></li>
</ul>
</li>
<li class="toctree-l2"><a class="reference internal" href="autograd.html#function"><span class="hidden-section">Function</span></a></li>
</ul>
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<li class="toctree-l1"><a class="reference internal" href="multiprocessing.html">torch.multiprocessing</a><ul>
<li class="toctree-l2"><a class="reference internal" href="multiprocessing.html#strategy-management">Strategy management</a></li>
<li class="toctree-l2"><a class="reference internal" href="multiprocessing.html#sharing-cuda-tensors">Sharing CUDA tensors</a></li>
<li class="toctree-l2"><a class="reference internal" href="multiprocessing.html#sharing-strategies">Sharing strategies</a><ul>
<li class="toctree-l3"><a class="reference internal" href="multiprocessing.html#file-descriptor-file-descriptor">File descriptor - <code class="docutils literal"><span class="pre">file_descriptor</span></code></a></li>
<li class="toctree-l3"><a class="reference internal" href="multiprocessing.html#file-system-file-system">File system - <code class="docutils literal"><span class="pre">file_system</span></code></a></li>
</ul>
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<li class="toctree-l1"><a class="reference internal" href="legacy.html">torch.legacy</a></li>
<li class="toctree-l1"><a class="reference internal" href="cuda.html">torch.cuda</a><ul>
<li class="toctree-l2"><a class="reference internal" href="cuda.html#communication-collectives">Communication collectives</a></li>
<li class="toctree-l2"><a class="reference internal" href="cuda.html#streams-and-events">Streams and events</a></li>
</ul>
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<li class="toctree-l1"><a class="reference internal" href="ffi.html">torch.utils.ffi</a></li>
<li class="toctree-l1"><a class="reference internal" href="data.html">torch.utils.data</a></li>
<li class="toctree-l1"><a class="reference internal" href="model_zoo.html">torch.utils.model_zoo</a></li>
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<p class="caption"><span class="caption-text">torchvision Reference</span></p>
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<li class="toctree-l1"><a class="reference internal" href="torchvision/torchvision.html">torchvision</a></li>
<li class="toctree-l1"><a class="reference internal" href="torchvision/datasets.html">torchvision.datasets</a><ul>
<li class="toctree-l2"><a class="reference internal" href="torchvision/datasets.html#mnist">MNIST</a></li>
<li class="toctree-l2"><a class="reference internal" href="torchvision/datasets.html#coco">COCO</a><ul>
<li class="toctree-l3"><a class="reference internal" href="torchvision/datasets.html#captions">Captions:</a></li>
<li class="toctree-l3"><a class="reference internal" href="torchvision/datasets.html#detection">Detection:</a></li>
</ul>
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<li class="toctree-l2"><a class="reference internal" href="torchvision/datasets.html#lsun">LSUN</a></li>
<li class="toctree-l2"><a class="reference internal" href="torchvision/datasets.html#imagefolder">ImageFolder</a></li>
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<li class="toctree-l2"><a class="reference internal" href="torchvision/datasets.html#cifar">CIFAR</a></li>
<li class="toctree-l2"><a class="reference internal" href="torchvision/datasets.html#stl10">STL10</a></li>
</ul>
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<li class="toctree-l1"><a class="reference internal" href="torchvision/models.html">torchvision.models</a></li>
<li class="toctree-l1"><a class="reference internal" href="torchvision/transforms.html">torchvision.transforms</a><ul>
<li class="toctree-l2"><a class="reference internal" href="torchvision/transforms.html#transforms-on-pil-image">Transforms on PIL.Image</a></li>
<li class="toctree-l2"><a class="reference internal" href="torchvision/transforms.html#transforms-on-torch-tensor">Transforms on torch.*Tensor</a></li>
<li class="toctree-l2"><a class="reference internal" href="torchvision/transforms.html#conversion-transforms">Conversion Transforms</a></li>
<li class="toctree-l2"><a class="reference internal" href="torchvision/transforms.html#generic-transforms">Generic Transforms</a></li>
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<li class="toctree-l1"><a class="reference internal" href="torchvision/utils.html">torchvision.utils</a></li>
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<div class="section" id="module-torch.nn">
<span id="torch-nn"></span><h1>torch.nn<a class="headerlink" href="#module-torch.nn" title="Permalink to this headline">¶</a></h1>
<div class="section" id="parameters">
<h2>Parameters<a class="headerlink" href="#parameters" title="Permalink to this headline">¶</a></h2>
<dl class="class">
<dt id="torch.nn.Parameter">
<em class="property">class </em><code class="descclassname">torch.nn.</code><code class="descname">Parameter</code><a class="reference internal" href="_modules/torch/nn/parameter.html#Parameter"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torch.nn.Parameter" title="Permalink to this definition">¶</a></dt>
<dd><p>A kind of Variable that is to be considered a module parameter.</p>
<p>Parameters are <a class="reference internal" href="autograd.html#torch.autograd.Variable" title="torch.autograd.Variable"><code class="xref py py-class docutils literal"><span class="pre">Variable</span></code></a> subclasses, that have a
very special property when used with <a class="reference internal" href="#torch.nn.Module" title="torch.nn.Module"><code class="xref py py-class docutils literal"><span class="pre">Module</span></code></a> s - when they’re
assigned as Module attributes they are automatically added to the list of
its parameters, and will appear e.g. in <a class="reference internal" href="#torch.nn.Module.parameters" title="torch.nn.Module.parameters"><code class="xref py py-meth docutils literal"><span class="pre">parameters()</span></code></a> iterator.
Assigning a Variable doesn’t have such effect. This is because one might
want to cache some temporary state, like last hidden state of the RNN, in
the model. If there was no such class as <a class="reference internal" href="#torch.nn.Parameter" title="torch.nn.Parameter"><code class="xref py py-class docutils literal"><span class="pre">Parameter</span></code></a>, these
temporaries would get registered too.</p>
<p>Another difference is that parameters can’t be volatile and that they
require gradient by default.</p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
<li><strong>data</strong> (<a class="reference internal" href="tensors.html#torch.Tensor" title="torch.Tensor"><em>Tensor</em></a>) – parameter tensor.</li>
<li><strong>requires_grad</strong> (<a class="reference external" href="https://docs.python.org/2/library/functions.html#bool" title="(in Python v2.7)"><em>bool</em></a><em>, </em><em>optional</em>) – if the parameter requires gradient. See
<a class="reference internal" href="notes/autograd.html#excluding-subgraphs"><span class="std std-ref">Excluding subgraphs from backward</span></a> for more details.</li>
</ul>
</td>
</tr>
</tbody>
</table>
</dd></dl>
</div>
<div class="section" id="containers">
<h2>Containers<a class="headerlink" href="#containers" title="Permalink to this headline">¶</a></h2>
<div class="section" id="module">
<h3><span class="hidden-section">Module</span><a class="headerlink" href="#module" title="Permalink to this headline">¶</a></h3>
<dl class="class">
<dt id="torch.nn.Module">
<em class="property">class </em><code class="descclassname">torch.nn.</code><code class="descname">Module</code><a class="reference internal" href="_modules/torch/nn/modules/module.html#Module"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torch.nn.Module" title="Permalink to this definition">¶</a></dt>
<dd><p>Base class for all neural network modules.</p>
<p>Your models should also subclass this class.</p>
<p>Modules can also contain other Modules, allowing to nest them in
a tree structure. You can assign the submodules as regular attributes:</p>
<div class="highlight-default"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">torch.nn</span> <span class="k">as</span> <span class="nn">nn</span>
<span class="kn">import</span> <span class="nn">torch.nn.functional</span> <span class="k">as</span> <span class="nn">F</span>
<span class="k">class</span> <span class="nc">Model</span><span class="p">(</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">):</span>
<span class="k">def</span> <span class="nf">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="nb">super</span><span class="p">(</span><span class="n">Model</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="n">__init__</span><span class="p">()</span>
<span class="bp">self</span><span class="o">.</span><span class="n">conv1</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Conv2d</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">20</span><span class="p">,</span> <span class="mi">5</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">conv2</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Conv2d</span><span class="p">(</span><span class="mi">20</span><span class="p">,</span> <span class="mi">20</span><span class="p">,</span> <span class="mi">5</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">):</span>
<span class="n">x</span> <span class="o">=</span> <span class="n">F</span><span class="o">.</span><span class="n">relu</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">conv1</span><span class="p">(</span><span class="n">x</span><span class="p">))</span>
<span class="k">return</span> <span class="n">F</span><span class="o">.</span><span class="n">relu</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">conv2</span><span class="p">(</span><span class="n">x</span><span class="p">))</span>
</pre></div>
</div>
<p>Submodules assigned in this way will be registered, and will have their
parameters converted too when you call .cuda(), etc.</p>
<dl class="method">
<dt id="torch.nn.Module.children">
<code class="descname">children</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/nn/modules/module.html#Module.children"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torch.nn.Module.children" title="Permalink to this definition">¶</a></dt>
<dd><p>Returns an iterator over children modules.</p>
</dd></dl>
<dl class="method">
<dt id="torch.nn.Module.cpu">
<code class="descname">cpu</code><span class="sig-paren">(</span><em>device_id=None</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/nn/modules/module.html#Module.cpu"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torch.nn.Module.cpu" title="Permalink to this definition">¶</a></dt>
<dd><p>Moves all model parameters and buffers to the CPU.</p>
</dd></dl>
<dl class="method">
<dt id="torch.nn.Module.cuda">
<code class="descname">cuda</code><span class="sig-paren">(</span><em>device_id=None</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/nn/modules/module.html#Module.cuda"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torch.nn.Module.cuda" title="Permalink to this definition">¶</a></dt>
<dd><p>Moves all model parameters and buffers to the GPU.</p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><strong>device_id</strong> (<a class="reference external" href="https://docs.python.org/2/library/functions.html#int" title="(in Python v2.7)"><em>int</em></a><em>, </em><em>optional</em>) – if specified, all parameters will be
copied to that device</td>
</tr>
</tbody>
</table>
</dd></dl>
<dl class="method">
<dt id="torch.nn.Module.double">
<code class="descname">double</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/nn/modules/module.html#Module.double"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torch.nn.Module.double" title="Permalink to this definition">¶</a></dt>
<dd><p>Casts all parameters and buffers to double datatype.</p>
</dd></dl>
<dl class="method">
<dt id="torch.nn.Module.eval">
<code class="descname">eval</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/nn/modules/module.html#Module.eval"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torch.nn.Module.eval" title="Permalink to this definition">¶</a></dt>
<dd><p>Sets the module in evaluation mode.</p>
<p>This has any effect only on modules such as Dropout or BatchNorm.</p>
</dd></dl>
<dl class="method">
<dt id="torch.nn.Module.float">
<code class="descname">float</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/nn/modules/module.html#Module.float"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torch.nn.Module.float" title="Permalink to this definition">¶</a></dt>
<dd><p>Casts all parameters and buffers to float datatype.</p>
</dd></dl>
<dl class="method">
<dt id="torch.nn.Module.forward">
<code class="descname">forward</code><span class="sig-paren">(</span><em>*input</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/nn/modules/module.html#Module.forward"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torch.nn.Module.forward" title="Permalink to this definition">¶</a></dt>
<dd><p>Defines the computation performed at every call.</p>
<p>Should be overriden by all subclasses.</p>
</dd></dl>
<dl class="method">
<dt id="torch.nn.Module.half">
<code class="descname">half</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/nn/modules/module.html#Module.half"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torch.nn.Module.half" title="Permalink to this definition">¶</a></dt>
<dd><p>Casts all parameters and buffers to half datatype.</p>
</dd></dl>
<dl class="method">
<dt id="torch.nn.Module.load_state_dict">
<code class="descname">load_state_dict</code><span class="sig-paren">(</span><em>state_dict</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/nn/modules/module.html#Module.load_state_dict"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torch.nn.Module.load_state_dict" title="Permalink to this definition">¶</a></dt>
<dd><p>Copies parameters and buffers from <a class="reference internal" href="#torch.nn.Module.state_dict" title="torch.nn.Module.state_dict"><code class="xref py py-attr docutils literal"><span class="pre">state_dict</span></code></a> into
this module and its descendants. The keys of <a class="reference internal" href="#torch.nn.Module.state_dict" title="torch.nn.Module.state_dict"><code class="xref py py-attr docutils literal"><span class="pre">state_dict</span></code></a> must
exactly match the keys returned by this module’s <a class="reference internal" href="#torch.nn.Module.state_dict" title="torch.nn.Module.state_dict"><code class="xref py py-func docutils literal"><span class="pre">state_dict()</span></code></a>
function.</p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><strong>state_dict</strong> (<a class="reference external" href="https://docs.python.org/2/library/stdtypes.html#dict" title="(in Python v2.7)"><em>dict</em></a>) – A dict containing parameters and
persistent buffers.</td>
</tr>
</tbody>
</table>
</dd></dl>
<dl class="method">
<dt id="torch.nn.Module.parameters">
<code class="descname">parameters</code><span class="sig-paren">(</span><em>memo=None</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/nn/modules/module.html#Module.parameters"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torch.nn.Module.parameters" title="Permalink to this definition">¶</a></dt>
<dd><p>Returns an iterator over module parameters.</p>
<p>This is typically passed to an optimizer.</p>
<p class="rubric">Example</p>
<div class="highlight-default"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="k">for</span> <span class="n">param</span> <span class="ow">in</span> <span class="n">model</span><span class="o">.</span><span class="n">parameters</span><span class="p">():</span>
<span class="gp">>>> </span> <span class="nb">print</span><span class="p">(</span><span class="nb">type</span><span class="p">(</span><span class="n">param</span><span class="o">.</span><span class="n">data</span><span class="p">),</span> <span class="n">param</span><span class="o">.</span><span class="n">size</span><span class="p">())</span>
<span class="go"><class 'torch.FloatTensor'> (20L,)</span>
<span class="go"><class 'torch.FloatTensor'> (20L, 1L, 5L, 5L)</span>
</pre></div>
</div>
</dd></dl>
<dl class="method">
<dt id="torch.nn.Module.register_backward_hook">
<code class="descname">register_backward_hook</code><span class="sig-paren">(</span><em>hook</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/nn/modules/module.html#Module.register_backward_hook"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torch.nn.Module.register_backward_hook" title="Permalink to this definition">¶</a></dt>
<dd><p>Registers a backward hook on the module.</p>
<p>The hook will be called every time the gradients with respect to module
inputs are computed. The hook should have the following signature:</p>
<div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">hook</span><span class="p">(</span><span class="n">module</span><span class="p">,</span> <span class="n">grad_input</span><span class="p">,</span> <span class="n">grad_output</span><span class="p">)</span> <span class="o">-></span> <span class="n">Tensor</span> <span class="ow">or</span> <span class="kc">None</span>
</pre></div>
</div>
<p>The <code class="xref py py-attr docutils literal"><span class="pre">grad_input</span></code> and <code class="xref py py-attr docutils literal"><span class="pre">grad_output</span></code> may be tuples if the
module has multiple inputs or outputs. The hook should not modify its
arguments, but it can optionally return a new gradient with respect to
input that will be used in place of <code class="xref py py-attr docutils literal"><span class="pre">grad_input</span></code> in subsequent
computations.</p>
<p>This function returns a handle with a method <code class="docutils literal"><span class="pre">handle.remove()</span></code>
that removes the hook from the module.</p>
</dd></dl>
<dl class="method">
<dt id="torch.nn.Module.register_buffer">
<code class="descname">register_buffer</code><span class="sig-paren">(</span><em>name</em>, <em>tensor</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/nn/modules/module.html#Module.register_buffer"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torch.nn.Module.register_buffer" title="Permalink to this definition">¶</a></dt>
<dd><p>Adds a persistent buffer to the module.</p>
<p>This is typically used to register a buffer that should not to be
considered a model parameter. For example, BatchNorm’s <code class="docutils literal"><span class="pre">running_mean</span></code>
is not a parameter, but is part of the persistent state.</p>
<p>Buffers can be accessed as attributes using given names.</p>
<p class="rubric">Example</p>
<div class="highlight-default"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="bp">self</span><span class="o">.</span><span class="n">register_buffer</span><span class="p">(</span><span class="s1">'running_mean'</span><span class="p">,</span> <span class="n">torch</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="n">num_features</span><span class="p">))</span>
</pre></div>
</div>
</dd></dl>
<dl class="method">
<dt id="torch.nn.Module.register_forward_hook">
<code class="descname">register_forward_hook</code><span class="sig-paren">(</span><em>hook</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/nn/modules/module.html#Module.register_forward_hook"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torch.nn.Module.register_forward_hook" title="Permalink to this definition">¶</a></dt>
<dd><p>Registers a forward hook on the module.</p>
<p>The hook will be called every time <a class="reference internal" href="#torch.nn.Module.forward" title="torch.nn.Module.forward"><code class="xref py py-func docutils literal"><span class="pre">forward()</span></code></a> computes an output.
It should have the following signature:</p>
<div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">hook</span><span class="p">(</span><span class="n">module</span><span class="p">,</span> <span class="nb">input</span><span class="p">,</span> <span class="n">output</span><span class="p">)</span> <span class="o">-></span> <span class="kc">None</span>
</pre></div>
</div>
<p>The hook should not modify the input or output.
This function returns a handle with a method <code class="docutils literal"><span class="pre">handle.remove()</span></code>
that removes the hook from the module.</p>
</dd></dl>
<dl class="method">
<dt id="torch.nn.Module.register_parameter">
<code class="descname">register_parameter</code><span class="sig-paren">(</span><em>name</em>, <em>param</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/nn/modules/module.html#Module.register_parameter"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torch.nn.Module.register_parameter" title="Permalink to this definition">¶</a></dt>
<dd><p>Adds a parameter to the module.</p>
<p>The parameter can be accessed as an attribute using given name.</p>
</dd></dl>
<dl class="method">
<dt id="torch.nn.Module.state_dict">
<code class="descname">state_dict</code><span class="sig-paren">(</span><em>destination=None</em>, <em>prefix=''</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/nn/modules/module.html#Module.state_dict"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torch.nn.Module.state_dict" title="Permalink to this definition">¶</a></dt>
<dd><p>Returns a dictionary containing a whole state of the module.</p>
<p>Both parameters and persistent buffers (e.g. running averages) are
included. Keys are corresponding parameter and buffer names.</p>
<p class="rubric">Example</p>
<div class="highlight-default"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">module</span><span class="o">.</span><span class="n">state_dict</span><span class="p">()</span><span class="o">.</span><span class="n">keys</span><span class="p">()</span>
<span class="go">['bias', 'weight']</span>
</pre></div>
</div>
</dd></dl>
<dl class="method">
<dt id="torch.nn.Module.train">
<code class="descname">train</code><span class="sig-paren">(</span><em>mode=True</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/nn/modules/module.html#Module.train"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torch.nn.Module.train" title="Permalink to this definition">¶</a></dt>
<dd><p>Sets the module in training mode.</p>
<p>This has any effect only on modules such as Dropout or BatchNorm.</p>
</dd></dl>
<dl class="method">
<dt id="torch.nn.Module.zero_grad">
<code class="descname">zero_grad</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/nn/modules/module.html#Module.zero_grad"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torch.nn.Module.zero_grad" title="Permalink to this definition">¶</a></dt>
<dd><p>Sets gradients of all model parameters to zero.</p>
</dd></dl>
</dd></dl>
</div>
<div class="section" id="sequential">
<h3><span class="hidden-section">Sequential</span><a class="headerlink" href="#sequential" title="Permalink to this headline">¶</a></h3>
<dl class="class">
<dt id="torch.nn.Sequential">
<em class="property">class </em><code class="descclassname">torch.nn.</code><code class="descname">Sequential</code><span class="sig-paren">(</span><em>*args</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/nn/modules/container.html#Sequential"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torch.nn.Sequential" title="Permalink to this definition">¶</a></dt>
<dd><p>A sequential container.
Modules will be added to it in the order they are passed in the constructor.
Alternatively, an ordered dict of modules can also be passed in.</p>
<p>To make it easier to understand, given is a small example:</p>
<div class="highlight-default"><div class="highlight"><pre><span></span><span class="c1"># Example of using Sequential</span>
<span class="n">model</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Sequential</span><span class="p">(</span>
<span class="n">nn</span><span class="o">.</span><span class="n">Conv2d</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span><span class="mi">20</span><span class="p">,</span><span class="mi">5</span><span class="p">),</span>
<span class="n">nn</span><span class="o">.</span><span class="n">ReLU</span><span class="p">(),</span>
<span class="n">nn</span><span class="o">.</span><span class="n">Conv2d</span><span class="p">(</span><span class="mi">20</span><span class="p">,</span><span class="mi">64</span><span class="p">,</span><span class="mi">5</span><span class="p">),</span>
<span class="n">nn</span><span class="o">.</span><span class="n">ReLU</span><span class="p">()</span>
<span class="p">)</span>
<span class="c1"># Example of using Sequential with OrderedDict</span>
<span class="n">model</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Sequential</span><span class="p">(</span><span class="n">OrderedDict</span><span class="p">([</span>
<span class="p">(</span><span class="s1">'conv1'</span><span class="p">,</span> <span class="n">nn</span><span class="o">.</span><span class="n">Conv2d</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span><span class="mi">20</span><span class="p">,</span><span class="mi">5</span><span class="p">)),</span>
<span class="p">(</span><span class="s1">'relu1'</span><span class="p">,</span> <span class="n">nn</span><span class="o">.</span><span class="n">ReLU</span><span class="p">()),</span>
<span class="p">(</span><span class="s1">'conv2'</span><span class="p">,</span> <span class="n">nn</span><span class="o">.</span><span class="n">Conv2d</span><span class="p">(</span><span class="mi">20</span><span class="p">,</span><span class="mi">64</span><span class="p">,</span><span class="mi">5</span><span class="p">)),</span>
<span class="p">(</span><span class="s1">'relu2'</span><span class="p">,</span> <span class="n">nn</span><span class="o">.</span><span class="n">ReLU</span><span class="p">())</span>
<span class="p">]))</span>
</pre></div>
</div>
</dd></dl>
</div>
<div class="section" id="modulelist">
<h3><span class="hidden-section">ModuleList</span><a class="headerlink" href="#modulelist" title="Permalink to this headline">¶</a></h3>
<dl class="class">
<dt id="torch.nn.ModuleList">
<em class="property">class </em><code class="descclassname">torch.nn.</code><code class="descname">ModuleList</code><span class="sig-paren">(</span><em>modules=None</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/nn/modules/container.html#ModuleList"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torch.nn.ModuleList" title="Permalink to this definition">¶</a></dt>
<dd><p>Holds submodules in a list.</p>
<p>ModuleList can be indexed like a regular Python list, but modules it contains
are properly registered, and will be visible by all Module methods.</p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><strong>modules</strong> (<a class="reference external" href="https://docs.python.org/2/library/functions.html#list" title="(in Python v2.7)"><em>list</em></a><em>, </em><em>optional</em>) – a list of modules to add</td>
</tr>
</tbody>
</table>
<p>Example:</p>
<div class="highlight-default"><div class="highlight"><pre><span></span><span class="k">class</span> <span class="nc">MyModule</span><span class="p">(</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">):</span>
<span class="k">def</span> <span class="nf">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="nb">super</span><span class="p">(</span><span class="n">MyModule</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="n">__init__</span><span class="p">()</span>
<span class="bp">self</span><span class="o">.</span><span class="n">linears</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">ModuleList</span><span class="p">([</span><span class="n">nn</span><span class="o">.</span><span class="n">Linear</span><span class="p">(</span><span class="mi">10</span><span class="p">,</span> <span class="mi">10</span><span class="p">)</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">10</span><span class="p">)])</span>
<span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">):</span>
<span class="c1"># ModuleList can act as an iterable, or be indexed using ints</span>
<span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">l</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">linears</span><span class="p">):</span>
<span class="n">x</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">linears</span><span class="p">[</span><span class="n">i</span> <span class="o">//</span> <span class="mi">2</span><span class="p">](</span><span class="n">x</span><span class="p">)</span> <span class="o">+</span> <span class="n">l</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
<span class="k">return</span> <span class="n">x</span>
</pre></div>
</div>
<dl class="method">
<dt id="torch.nn.ModuleList.append">
<code class="descname">append</code><span class="sig-paren">(</span><em>module</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/nn/modules/container.html#ModuleList.append"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torch.nn.ModuleList.append" title="Permalink to this definition">¶</a></dt>
<dd><p>Appends a given module at the end of the list.</p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><strong>module</strong> (<a class="reference internal" href="#torch.nn.Module" title="torch.nn.Module"><em>nn.Module</em></a>) – module to append</td>
</tr>
</tbody>
</table>
</dd></dl>
<dl class="method">
<dt id="torch.nn.ModuleList.extend">
<code class="descname">extend</code><span class="sig-paren">(</span><em>modules</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/nn/modules/container.html#ModuleList.extend"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torch.nn.ModuleList.extend" title="Permalink to this definition">¶</a></dt>
<dd><p>Appends modules from a Python list at the end.</p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><strong>modules</strong> (<a class="reference external" href="https://docs.python.org/2/library/functions.html#list" title="(in Python v2.7)"><em>list</em></a>) – list of modules to append</td>
</tr>
</tbody>
</table>
</dd></dl>
</dd></dl>
</div>
<div class="section" id="parameterlist">
<h3><span class="hidden-section">ParameterList</span><a class="headerlink" href="#parameterlist" title="Permalink to this headline">¶</a></h3>
<dl class="class">
<dt id="torch.nn.ParameterList">
<em class="property">class </em><code class="descclassname">torch.nn.</code><code class="descname">ParameterList</code><span class="sig-paren">(</span><em>parameters=None</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/nn/modules/container.html#ParameterList"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torch.nn.ParameterList" title="Permalink to this definition">¶</a></dt>
<dd><p>Holds submodules in a list.</p>
<p>ParameterList can be indexed like a regular Python list, but parameters it contains
are properly registered, and will be visible by all Module methods.</p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><strong>modules</strong> (<a class="reference external" href="https://docs.python.org/2/library/functions.html#list" title="(in Python v2.7)"><em>list</em></a><em>, </em><em>optional</em>) – a list of <code class="xref py py-class docutils literal"><span class="pre">nn.Parameter`</span></code> to add</td>
</tr>
</tbody>
</table>
<p>Example:</p>
<div class="highlight-default"><div class="highlight"><pre><span></span><span class="k">class</span> <span class="nc">MyModule</span><span class="p">(</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">):</span>
<span class="k">def</span> <span class="nf">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="nb">super</span><span class="p">(</span><span class="n">MyModule</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="n">__init__</span><span class="p">()</span>
<span class="bp">self</span><span class="o">.</span><span class="n">params</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">ParameterList</span><span class="p">([</span><span class="n">nn</span><span class="o">.</span><span class="n">Parameter</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">10</span><span class="p">,</span> <span class="mi">10</span><span class="p">))</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">10</span><span class="p">)])</span>
<span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">):</span>
<span class="c1"># ModuleList can act as an iterable, or be indexed using ints</span>
<span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">p</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">params</span><span class="p">):</span>
<span class="n">x</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">params</span><span class="p">[</span><span class="n">i</span> <span class="o">//</span> <span class="mi">2</span><span class="p">]</span><span class="o">.</span><span class="n">mm</span><span class="p">(</span><span class="n">x</span><span class="p">)</span> <span class="o">+</span> <span class="n">p</span><span class="o">.</span><span class="n">mm</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
<span class="k">return</span> <span class="n">x</span>
</pre></div>
</div>
<dl class="method">
<dt id="torch.nn.ParameterList.append">
<code class="descname">append</code><span class="sig-paren">(</span><em>parameter</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/nn/modules/container.html#ParameterList.append"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torch.nn.ParameterList.append" title="Permalink to this definition">¶</a></dt>
<dd><p>Appends a given parameter at the end of the list.</p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><strong>parameter</strong> (<a class="reference internal" href="#torch.nn.Parameter" title="torch.nn.Parameter"><em>nn.Parameter</em></a>) – parameter to append</td>
</tr>
</tbody>
</table>
</dd></dl>
<dl class="method">
<dt id="torch.nn.ParameterList.extend">
<code class="descname">extend</code><span class="sig-paren">(</span><em>parameters</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/nn/modules/container.html#ParameterList.extend"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torch.nn.ParameterList.extend" title="Permalink to this definition">¶</a></dt>
<dd><p>Appends parameters from a Python list at the end.</p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><strong>parameters</strong> (<a class="reference external" href="https://docs.python.org/2/library/functions.html#list" title="(in Python v2.7)"><em>list</em></a>) – list of parameters to append</td>
</tr>
</tbody>
</table>
</dd></dl>
</dd></dl>
</div>
</div>
<div class="section" id="convolution-layers">
<h2>Convolution Layers<a class="headerlink" href="#convolution-layers" title="Permalink to this headline">¶</a></h2>
<div class="section" id="conv1d">
<h3><span class="hidden-section">Conv1d</span><a class="headerlink" href="#conv1d" title="Permalink to this headline">¶</a></h3>
<dl class="class">
<dt id="torch.nn.Conv1d">
<em class="property">class </em><code class="descclassname">torch.nn.</code><code class="descname">Conv1d</code><span class="sig-paren">(</span><em>in_channels</em>, <em>out_channels</em>, <em>kernel_size</em>, <em>stride=1</em>, <em>padding=0</em>, <em>dilation=1</em>, <em>groups=1</em>, <em>bias=True</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/nn/modules/conv.html#Conv1d"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torch.nn.Conv1d" title="Permalink to this definition">¶</a></dt>
<dd><p>Applies a 1D convolution over an input signal composed of several input
planes.</p>
<p>In the simplest case, the output value of the layer with input size <span class="math">\((N, C_{in}, L)\)</span>
and output <span class="math">\((N, C_{out}, L_{out})\)</span> can be precisely described as:</p>
<div class="math">
\[\begin{array}{ll}
out(N_i, C_{out_j}) = bias(C_{out_j})
+ \sum_{{k}=0}^{C_{in}-1} weight(C_{out_j}, k) \star input(N_i, k)
\end{array}\]</div>
<p>where <span class="math">\(\star\)</span> is the valid <a class="reference external" href="https://en.wikipedia.org/wiki/Cross-correlation">cross-correlation</a> operator</p>
<div class="line-block">
<div class="line"><code class="xref py py-attr docutils literal"><span class="pre">stride</span></code> controls the stride for the cross-correlation.</div>
<div class="line">If <code class="xref py py-attr docutils literal"><span class="pre">padding</span></code> is non-zero, then the input is implicitly zero-padded on both sides
for <code class="xref py py-attr docutils literal"><span class="pre">padding</span></code> number of points</div>
<div class="line"><code class="xref py py-attr docutils literal"><span class="pre">dilation</span></code> controls the spacing between the kernel points. It is harder to describe,
but this <a class="reference external" href="https://github.com/vdumoulin/conv_arithmetic/blob/master/README.md">link</a> has a nice visualization of what <code class="xref py py-attr docutils literal"><span class="pre">dilation</span></code> does.</div>
<div class="line"><code class="xref py py-attr docutils literal"><span class="pre">groups</span></code> controls the connections between inputs and outputs.</div>
<div class="line-block">
<div class="line">At groups=1, all inputs are convolved to all outputs.</div>
<div class="line">At groups=2, the operation becomes equivalent to having two conv layers
side by side, each seeing half the input channels,
and producing half the output channels, and both subsequently concatenated.</div>
</div>
</div>
<div class="admonition note">
<p class="first admonition-title">Note</p>
<p class="last">Depending of the size of your kernel, several (of the last)
columns of the input might be lost, because it is a valid <a class="reference external" href="https://en.wikipedia.org/wiki/Cross-correlation">cross-correlation</a>,
and not a full <a class="reference external" href="https://en.wikipedia.org/wiki/Cross-correlation">cross-correlation</a>.
It is up to the user to add proper padding.</p>
</div>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
<li><strong>in_channels</strong> (<a class="reference external" href="https://docs.python.org/2/library/functions.html#int" title="(in Python v2.7)"><em>int</em></a>) – Number of channels in the input image</li>
<li><strong>out_channels</strong> (<a class="reference external" href="https://docs.python.org/2/library/functions.html#int" title="(in Python v2.7)"><em>int</em></a>) – Number of channels produced by the convolution</li>
<li><strong>kernel_size</strong> (<a class="reference external" href="https://docs.python.org/2/library/functions.html#int" title="(in Python v2.7)"><em>int</em></a><em> or </em><a class="reference external" href="https://docs.python.org/2/library/functions.html#tuple" title="(in Python v2.7)"><em>tuple</em></a>) – Size of the convolving kernel</li>
<li><strong>stride</strong> (<a class="reference external" href="https://docs.python.org/2/library/functions.html#int" title="(in Python v2.7)"><em>int</em></a><em> or </em><a class="reference external" href="https://docs.python.org/2/library/functions.html#tuple" title="(in Python v2.7)"><em>tuple</em></a><em>, </em><em>optional</em>) – Stride of the convolution</li>
<li><strong>padding</strong> (<a class="reference external" href="https://docs.python.org/2/library/functions.html#int" title="(in Python v2.7)"><em>int</em></a><em> or </em><a class="reference external" href="https://docs.python.org/2/library/functions.html#tuple" title="(in Python v2.7)"><em>tuple</em></a><em>, </em><em>optional</em>) – Zero-padding added to both sides of the input</li>
<li><strong>dilation</strong> (<a class="reference external" href="https://docs.python.org/2/library/functions.html#int" title="(in Python v2.7)"><em>int</em></a><em> or </em><a class="reference external" href="https://docs.python.org/2/library/functions.html#tuple" title="(in Python v2.7)"><em>tuple</em></a><em>, </em><em>optional</em>) – Spacing between kernel elements</li>
<li><strong>groups</strong> (<a class="reference external" href="https://docs.python.org/2/library/functions.html#int" title="(in Python v2.7)"><em>int</em></a><em>, </em><em>optional</em>) – Number of blocked connections from input channels to output channels</li>
<li><strong>bias</strong> (<a class="reference external" href="https://docs.python.org/2/library/functions.html#bool" title="(in Python v2.7)"><em>bool</em></a><em>, </em><em>optional</em>) – If True, adds a learnable bias to the output</li>
</ul>
</td>
</tr>
</tbody>
</table>
<dl class="docutils">
<dt>Shape:</dt>
<dd><ul class="first last simple">
<li>Input: <span class="math">\((N, C_{in}, L_{in})\)</span></li>
<li>Output: <span class="math">\((N, C_{out}, L_{out})\)</span> where
<span class="math">\(L_{out} = floor((L_{in} + 2 * padding - dilation * (kernel\_size - 1) - 1) / stride + 1)\)</span></li>
</ul>
</dd>
</dl>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Variables:</th><td class="field-body"><ul class="first last simple">
<li><strong>weight</strong> (<a class="reference internal" href="tensors.html#torch.Tensor" title="torch.Tensor"><em>Tensor</em></a>) – the learnable weights of the module of shape (out_channels, in_channels, kernel_size)</li>
<li><strong>bias</strong> (<a class="reference internal" href="tensors.html#torch.Tensor" title="torch.Tensor"><em>Tensor</em></a>) – the learnable bias of the module of shape (out_channels)</li>
</ul>
</td>
</tr>
</tbody>
</table>
<p>Examples:</p>
<div class="highlight-default"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">m</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Conv1d</span><span class="p">(</span><span class="mi">16</span><span class="p">,</span> <span class="mi">33</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="n">stride</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
<span class="gp">>>> </span><span class="nb">input</span> <span class="o">=</span> <span class="n">autograd</span><span class="o">.</span><span class="n">Variable</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">20</span><span class="p">,</span> <span class="mi">16</span><span class="p">,</span> <span class="mi">50</span><span class="p">))</span>
<span class="gp">>>> </span><span class="n">output</span> <span class="o">=</span> <span class="n">m</span><span class="p">(</span><span class="nb">input</span><span class="p">)</span>
</pre></div>
</div>
</dd></dl>
</div>
<div class="section" id="conv2d">
<h3><span class="hidden-section">Conv2d</span><a class="headerlink" href="#conv2d" title="Permalink to this headline">¶</a></h3>
<dl class="class">
<dt id="torch.nn.Conv2d">
<em class="property">class </em><code class="descclassname">torch.nn.</code><code class="descname">Conv2d</code><span class="sig-paren">(</span><em>in_channels</em>, <em>out_channels</em>, <em>kernel_size</em>, <em>stride=1</em>, <em>padding=0</em>, <em>dilation=1</em>, <em>groups=1</em>, <em>bias=True</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/nn/modules/conv.html#Conv2d"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torch.nn.Conv2d" title="Permalink to this definition">¶</a></dt>
<dd><p>Applies a 2D convolution over an input signal composed of several input
planes.</p>
<p>In the simplest case, the output value of the layer with input size <span class="math">\((N, C_{in}, H, W)\)</span>
and output <span class="math">\((N, C_{out}, H_{out}, W_{out})\)</span> can be precisely described as:</p>
<div class="math">
\[\begin{array}{ll}
out(N_i, C_{out_j}) = bias(C_{out_j})
+ \sum_{{k}=0}^{C_{in}-1} weight(C_{out_j}, k) \star input(N_i, k)
\end{array}\]</div>
<p>where <span class="math">\(\star\)</span> is the valid 2D <a class="reference external" href="https://en.wikipedia.org/wiki/Cross-correlation">cross-correlation</a> operator</p>
<div class="line-block">
<div class="line"><code class="xref py py-attr docutils literal"><span class="pre">stride</span></code> controls the stride for the cross-correlation.</div>
<div class="line">If <code class="xref py py-attr docutils literal"><span class="pre">padding</span></code> is non-zero, then the input is implicitly zero-padded on both sides
for <code class="xref py py-attr docutils literal"><span class="pre">padding</span></code> number of points</div>
<div class="line"><code class="xref py py-attr docutils literal"><span class="pre">dilation</span></code> controls the spacing between the kernel points. It is harder to describe,
but this <a class="reference external" href="https://github.com/vdumoulin/conv_arithmetic/blob/master/README.md">link</a> has a nice visualization of what <code class="xref py py-attr docutils literal"><span class="pre">dilation</span></code> does.</div>
<div class="line"><code class="xref py py-attr docutils literal"><span class="pre">groups</span></code> controls the connections between inputs and outputs.</div>
<div class="line-block">
<div class="line">At groups=1, all inputs are convolved to all outputs.</div>
<div class="line">At groups=2, the operation becomes equivalent to having two conv layers
side by side, each seeing half the input channels,
and producing half the output channels, and both subsequently concatenated.</div>
</div>
</div>
<p>The parameters <code class="xref py py-attr docutils literal"><span class="pre">kernel_size</span></code>, <code class="xref py py-attr docutils literal"><span class="pre">stride</span></code>, <code class="xref py py-attr docutils literal"><span class="pre">padding</span></code>, <code class="xref py py-attr docutils literal"><span class="pre">dilation</span></code> can either be:</p>
<blockquote>
<div><ul class="simple">
<li>a single <code class="docutils literal"><span class="pre">int</span></code> – in which case the same value is used for the height and width dimension</li>
<li>a <code class="docutils literal"><span class="pre">tuple</span></code> of two ints – in which case, the first <cite>int</cite> is used for the height dimension,
and the second <cite>int</cite> for the width dimension</li>
</ul>
</div></blockquote>
<div class="admonition note">
<p class="first admonition-title">Note</p>
<p class="last">Depending of the size of your kernel, several (of the last)