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<li class="toctree-l3"><a class="reference internal" href="#sequential"><span class="hidden-section">Sequential</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="#modulelist"><span class="hidden-section">ModuleList</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="#parameterlist"><span class="hidden-section">ParameterList</span></a></li>
</ul>
</li>
<li class="toctree-l2"><a class="reference internal" href="#convolution-layers">Convolution layers</a><ul>
<li class="toctree-l3"><a class="reference internal" href="#conv1d"><span class="hidden-section">Conv1d</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="#conv2d"><span class="hidden-section">Conv2d</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="#conv3d"><span class="hidden-section">Conv3d</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="#convtranspose1d"><span class="hidden-section">ConvTranspose1d</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="#convtranspose2d"><span class="hidden-section">ConvTranspose2d</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="#convtranspose3d"><span class="hidden-section">ConvTranspose3d</span></a></li>
</ul>
</li>
<li class="toctree-l2"><a class="reference internal" href="#pooling-layers">Pooling layers</a><ul>
<li class="toctree-l3"><a class="reference internal" href="#maxpool1d"><span class="hidden-section">MaxPool1d</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="#maxpool2d"><span class="hidden-section">MaxPool2d</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="#maxpool3d"><span class="hidden-section">MaxPool3d</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="#maxunpool1d"><span class="hidden-section">MaxUnpool1d</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="#maxunpool2d"><span class="hidden-section">MaxUnpool2d</span></a></li>
<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="#lppool1d"><span class="hidden-section">LPPool1d</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="#lppool2d"><span class="hidden-section">LPPool2d</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="#adaptivemaxpool1d"><span class="hidden-section">AdaptiveMaxPool1d</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="#adaptivemaxpool2d"><span class="hidden-section">AdaptiveMaxPool2d</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="#adaptivemaxpool3d"><span class="hidden-section">AdaptiveMaxPool3d</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="#adaptiveavgpool1d"><span class="hidden-section">AdaptiveAvgPool1d</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="#adaptiveavgpool2d"><span class="hidden-section">AdaptiveAvgPool2d</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="#adaptiveavgpool3d"><span class="hidden-section">AdaptiveAvgPool3d</span></a></li>
</ul>
</li>
<li class="toctree-l2"><a class="reference internal" href="#padding-layers">Padding layers</a><ul>
<li class="toctree-l3"><a class="reference internal" href="#reflectionpad1d"><span class="hidden-section">ReflectionPad1d</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="#reflectionpad2d"><span class="hidden-section">ReflectionPad2d</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="#replicationpad1d"><span class="hidden-section">ReplicationPad1d</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="#replicationpad2d"><span class="hidden-section">ReplicationPad2d</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="#replicationpad3d"><span class="hidden-section">ReplicationPad3d</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="#zeropad2d"><span class="hidden-section">ZeroPad2d</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="#constantpad1d"><span class="hidden-section">ConstantPad1d</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="#constantpad2d"><span class="hidden-section">ConstantPad2d</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="#constantpad3d"><span class="hidden-section">ConstantPad3d</span></a></li>
</ul>
</li>
<li class="toctree-l2"><a class="reference internal" href="#non-linear-activations-weighted-sum-nonlinearity">Non-linear activations (weighted sum, nonlinearity)</a><ul>
<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="#hardshrink"><span class="hidden-section">Hardshrink</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="#leakyrelu"><span class="hidden-section">LeakyReLU</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="#prelu"><span class="hidden-section">PReLU</span></a></li>
<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="#rrelu"><span class="hidden-section">RReLU</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="#selu"><span class="hidden-section">SELU</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="#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="#tanh"><span class="hidden-section">Tanh</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="#threshold"><span class="hidden-section">Threshold</span></a></li>
</ul>
</li>
<li class="toctree-l2"><a class="reference internal" href="#non-linear-activations-other">Non-linear activations (other)</a><ul>
<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="#softmax2d"><span class="hidden-section">Softmax2d</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>
<li class="toctree-l3"><a class="reference internal" href="#instancenorm1d"><span class="hidden-section">InstanceNorm1d</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="#instancenorm2d"><span class="hidden-section">InstanceNorm2d</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="#instancenorm3d"><span class="hidden-section">InstanceNorm3d</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="#layernorm"><span class="hidden-section">LayerNorm</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="#localresponsenorm"><span class="hidden-section">LocalResponseNorm</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>
<li class="toctree-l3"><a class="reference internal" href="#bilinear"><span class="hidden-section">Bilinear</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>
<li class="toctree-l3"><a class="reference internal" href="#alphadropout"><span class="hidden-section">AlphaDropout</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>
<li class="toctree-l3"><a class="reference internal" href="#embeddingbag"><span class="hidden-section">EmbeddingBag</span></a></li>
</ul>
</li>
<li class="toctree-l2"><a class="reference internal" href="#distance-functions">Distance functions</a><ul>
<li class="toctree-l3"><a class="reference internal" href="#cosinesimilarity"><span class="hidden-section">CosineSimilarity</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="#pairwisedistance"><span class="hidden-section">PairwiseDistance</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="#poissonnllloss"><span class="hidden-section">PoissonNLLLoss</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="#bcewithlogitsloss"><span class="hidden-section">BCEWithLogitsLoss</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>
<li class="toctree-l3"><a class="reference internal" href="#tripletmarginloss"><span class="hidden-section">TripletMarginLoss</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="#upsample"><span class="hidden-section">Upsample</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="#dataparallel-layers-multi-gpu-distributed">DataParallel layers (multi-GPU, distributed)</a><ul>
<li class="toctree-l3"><a class="reference internal" href="#dataparallel"><span class="hidden-section">DataParallel</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="#distributeddataparallel"><span class="hidden-section">DistributedDataParallel</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="#clip-grad-value"><span class="hidden-section">clip_grad_value_</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="#weight-norm"><span class="hidden-section">weight_norm</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="#remove-weight-norm"><span class="hidden-section">remove_weight_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>
<li class="toctree-l3"><a class="reference internal" href="#pad-sequence"><span class="hidden-section">pad_sequence</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="#pack-sequence"><span class="hidden-section">pack_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="#id20"><span class="hidden-section">conv1d</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="#id21"><span class="hidden-section">conv2d</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="#id22"><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-pool1d"><span class="hidden-section">lp_pool1d</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="#lp-pool2d"><span class="hidden-section">lp_pool2d</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="#adaptive-max-pool1d"><span class="hidden-section">adaptive_max_pool1d</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="#adaptive-max-pool2d"><span class="hidden-section">adaptive_max_pool2d</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="#adaptive-max-pool3d"><span class="hidden-section">adaptive_max_pool3d</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="#adaptive-avg-pool1d"><span class="hidden-section">adaptive_avg_pool1d</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="#adaptive-avg-pool2d"><span class="hidden-section">adaptive_avg_pool2d</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="#adaptive-avg-pool3d"><span class="hidden-section">adaptive_avg_pool3d</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="#id23"><span class="hidden-section">threshold</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="#id24"><span class="hidden-section">relu</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="#id25"><span class="hidden-section">hardtanh</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="#id26"><span class="hidden-section">relu6</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="#id27"><span class="hidden-section">elu</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="#id28"><span class="hidden-section">selu</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="#id29"><span class="hidden-section">prelu</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="#id30"><span class="hidden-section">rrelu</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="#glu"><span class="hidden-section">glu</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="#id31"><span class="hidden-section">logsigmoid</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="#id32"><span class="hidden-section">hardshrink</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="#id33"><span class="hidden-section">tanhshrink</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="#id34"><span class="hidden-section">softsign</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="#id35"><span class="hidden-section">softplus</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="#id36"><span class="hidden-section">softmin</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="#id37"><span class="hidden-section">softmax</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="#id38"><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="#id39"><span class="hidden-section">tanh</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="#id40"><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>
<li class="toctree-l3"><a class="reference internal" href="#instance-norm"><span class="hidden-section">instance_norm</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="#layer-norm"><span class="hidden-section">layer_norm</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="#local-response-norm"><span class="hidden-section">local_response_norm</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="#normalize"><span class="hidden-section">normalize</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="#id41"><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="#id42"><span class="hidden-section">dropout</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="#alpha-dropout"><span class="hidden-section">alpha_dropout</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="#id43"><span class="hidden-section">dropout2d</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="#id44"><span class="hidden-section">dropout3d</span></a></li>
</ul>
</li>
<li class="toctree-l2"><a class="reference internal" href="#id45">Distance functions</a><ul>
<li class="toctree-l3"><a class="reference internal" href="#pairwise-distance"><span class="hidden-section">pairwise_distance</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="#cosine-similarity"><span class="hidden-section">cosine_similarity</span></a></li>
</ul>
</li>
<li class="toctree-l2"><a class="reference internal" href="#id46">Loss functions</a><ul>
<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="#poisson-nll-loss"><span class="hidden-section">poisson_nll_loss</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="#cosine-embedding-loss"><span class="hidden-section">cosine_embedding_loss</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="#hinge-embedding-loss"><span class="hidden-section">hinge_embedding_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="#l1-loss"><span class="hidden-section">l1_loss</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="#mse-loss"><span class="hidden-section">mse_loss</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="#margin-ranking-loss"><span class="hidden-section">margin_ranking_loss</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="#multilabel-margin-loss"><span class="hidden-section">multilabel_margin_loss</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="#multilabel-soft-margin-loss"><span class="hidden-section">multilabel_soft_margin_loss</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="#multi-margin-loss"><span class="hidden-section">multi_margin_loss</span></a></li>
<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="#binary-cross-entropy-with-logits"><span class="hidden-section">binary_cross_entropy_with_logits</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>
<li class="toctree-l3"><a class="reference internal" href="#soft-margin-loss"><span class="hidden-section">soft_margin_loss</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="#triplet-margin-loss"><span class="hidden-section">triplet_margin_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>
<li class="toctree-l3"><a class="reference internal" href="#id47"><span class="hidden-section">upsample</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="#upsample-nearest"><span class="hidden-section">upsample_nearest</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="#upsample-bilinear"><span class="hidden-section">upsample_bilinear</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="#grid-sample"><span class="hidden-section">grid_sample</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="#affine-grid"><span class="hidden-section">affine_grid</span></a></li>
</ul>
</li>
<li class="toctree-l2"><a class="reference internal" href="#dataparallel-functions-multi-gpu-distributed">DataParallel functions (multi-GPU, distributed)</a><ul>
<li class="toctree-l3"><a class="reference internal" href="#data-parallel"><span class="hidden-section">data_parallel</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>
</li>
<li class="toctree-l2"><a class="reference internal" href="optim.html#algorithms">Algorithms</a></li>
<li class="toctree-l2"><a class="reference internal" href="optim.html#how-to-adjust-learning-rate">How to adjust Learning Rate</a></li>
</ul>
</li>
<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#locally-disabling-gradient-computation">Locally disabling gradient computation</a></li>
<li class="toctree-l2"><a class="reference internal" href="autograd.html#in-place-operations-on-tensors">In-place operations on Tensors</a><ul>
<li class="toctree-l3"><a class="reference internal" href="autograd.html#in-place-correctness-checks">In-place correctness checks</a></li>
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<li class="toctree-l1"><a class="reference internal" href="distributions.html">torch.distributions</a><ul>
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<li class="toctree-l2"><a class="reference internal" href="distributions.html#distribution"><span class="hidden-section">Distribution</span></a></li>
<li class="toctree-l2"><a class="reference internal" href="distributions.html#exponentialfamily"><span class="hidden-section">ExponentialFamily</span></a></li>
<li class="toctree-l2"><a class="reference internal" href="distributions.html#bernoulli"><span class="hidden-section">Bernoulli</span></a></li>
<li class="toctree-l2"><a class="reference internal" href="distributions.html#beta"><span class="hidden-section">Beta</span></a></li>
<li class="toctree-l2"><a class="reference internal" href="distributions.html#binomial"><span class="hidden-section">Binomial</span></a></li>
<li class="toctree-l2"><a class="reference internal" href="distributions.html#categorical"><span class="hidden-section">Categorical</span></a></li>
<li class="toctree-l2"><a class="reference internal" href="distributions.html#cauchy"><span class="hidden-section">Cauchy</span></a></li>
<li class="toctree-l2"><a class="reference internal" href="distributions.html#chi2"><span class="hidden-section">Chi2</span></a></li>
<li class="toctree-l2"><a class="reference internal" href="distributions.html#dirichlet"><span class="hidden-section">Dirichlet</span></a></li>
<li class="toctree-l2"><a class="reference internal" href="distributions.html#exponential"><span class="hidden-section">Exponential</span></a></li>
<li class="toctree-l2"><a class="reference internal" href="distributions.html#fishersnedecor"><span class="hidden-section">FisherSnedecor</span></a></li>
<li class="toctree-l2"><a class="reference internal" href="distributions.html#gamma"><span class="hidden-section">Gamma</span></a></li>
<li class="toctree-l2"><a class="reference internal" href="distributions.html#geometric"><span class="hidden-section">Geometric</span></a></li>
<li class="toctree-l2"><a class="reference internal" href="distributions.html#gumbel"><span class="hidden-section">Gumbel</span></a></li>
<li class="toctree-l2"><a class="reference internal" href="distributions.html#independent"><span class="hidden-section">Independent</span></a></li>
<li class="toctree-l2"><a class="reference internal" href="distributions.html#laplace"><span class="hidden-section">Laplace</span></a></li>
<li class="toctree-l2"><a class="reference internal" href="distributions.html#lognormal"><span class="hidden-section">LogNormal</span></a></li>
<li class="toctree-l2"><a class="reference internal" href="distributions.html#multinomial"><span class="hidden-section">Multinomial</span></a></li>
<li class="toctree-l2"><a class="reference internal" href="distributions.html#multivariatenormal"><span class="hidden-section">MultivariateNormal</span></a></li>
<li class="toctree-l2"><a class="reference internal" href="distributions.html#normal"><span class="hidden-section">Normal</span></a></li>
<li class="toctree-l2"><a class="reference internal" href="distributions.html#onehotcategorical"><span class="hidden-section">OneHotCategorical</span></a></li>
<li class="toctree-l2"><a class="reference internal" href="distributions.html#pareto"><span class="hidden-section">Pareto</span></a></li>
<li class="toctree-l2"><a class="reference internal" href="distributions.html#poisson"><span class="hidden-section">Poisson</span></a></li>
<li class="toctree-l2"><a class="reference internal" href="distributions.html#relaxedbernoulli"><span class="hidden-section">RelaxedBernoulli</span></a></li>
<li class="toctree-l2"><a class="reference internal" href="distributions.html#relaxedonehotcategorical"><span class="hidden-section">RelaxedOneHotCategorical</span></a></li>
<li class="toctree-l2"><a class="reference internal" href="distributions.html#studentt"><span class="hidden-section">StudentT</span></a></li>
<li class="toctree-l2"><a class="reference internal" href="distributions.html#transformeddistribution"><span class="hidden-section">TransformedDistribution</span></a></li>
<li class="toctree-l2"><a class="reference internal" href="distributions.html#uniform"><span class="hidden-section">Uniform</span></a></li>
<li class="toctree-l2"><a class="reference internal" href="distributions.html#module-torch.distributions.kl"><cite>KL Divergence</cite></a></li>
<li class="toctree-l2"><a class="reference internal" href="distributions.html#module-torch.distributions.transforms"><cite>Transforms</cite></a></li>
<li class="toctree-l2"><a class="reference internal" href="distributions.html#module-torch.distributions.constraints"><cite>Constraints</cite></a></li>
<li class="toctree-l2"><a class="reference internal" href="distributions.html#module-torch.distributions.constraint_registry"><cite>Constraint Registry</cite></a></li>
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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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</ul>
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<li class="toctree-l1"><a class="reference internal" href="distributed.html">torch.distributed</a><ul>
<li class="toctree-l2"><a class="reference internal" href="distributed.html#basics">Basics</a></li>
<li class="toctree-l2"><a class="reference internal" href="distributed.html#initialization">Initialization</a><ul>
<li class="toctree-l3"><a class="reference internal" href="distributed.html#tcp-initialization">TCP initialization</a></li>
<li class="toctree-l3"><a class="reference internal" href="distributed.html#shared-file-system-initialization">Shared file-system initialization</a></li>
<li class="toctree-l3"><a class="reference internal" href="distributed.html#environment-variable-initialization">Environment variable initialization</a></li>
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<li class="toctree-l2"><a class="reference internal" href="distributed.html#groups">Groups</a></li>
<li class="toctree-l2"><a class="reference internal" href="distributed.html#point-to-point-communication">Point-to-point communication</a></li>
<li class="toctree-l2"><a class="reference internal" href="distributed.html#collective-functions">Collective functions</a></li>
<li class="toctree-l2"><a class="reference internal" href="distributed.html#multi-gpu-collective-functions">Multi-GPU collective functions</a></li>
<li class="toctree-l2"><a class="reference internal" href="distributed.html#launch-utility">Launch utility</a></li>
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<li class="toctree-l1"><a class="reference internal" href="bottleneck.html">torch.utils.bottleneck</a></li>
<li class="toctree-l1"><a class="reference internal" href="checkpoint.html">torch.utils.checkpoint</a></li>
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<li class="toctree-l1"><a class="reference internal" href="data.html">torch.utils.data</a></li>
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<li class="toctree-l1"><a class="reference internal" href="model_zoo.html">torch.utils.model_zoo</a></li>
<li class="toctree-l1"><a class="reference internal" href="onnx.html">torch.onnx</a><ul>
<li class="toctree-l2"><a class="reference internal" href="onnx.html#example-end-to-end-alexnet-from-pytorch-to-caffe2">Example: End-to-end AlexNet from PyTorch to Caffe2</a></li>
<li class="toctree-l2"><a class="reference internal" href="onnx.html#limitations">Limitations</a></li>
<li class="toctree-l2"><a class="reference internal" href="onnx.html#supported-operators">Supported operators</a></li>
<li class="toctree-l2"><a class="reference internal" href="onnx.html#functions">Functions</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/index.html">torchvision</a><ul>
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<li class="toctree-l3"><a class="reference internal" href="torchvision/datasets.html#mnist">MNIST</a></li>
<li class="toctree-l3"><a class="reference internal" href="torchvision/datasets.html#fashion-mnist">Fashion-MNIST</a></li>
<li class="toctree-l3"><a class="reference internal" href="torchvision/datasets.html#emnist">EMNIST</a></li>
<li class="toctree-l3"><a class="reference internal" href="torchvision/datasets.html#coco">COCO</a><ul>
<li class="toctree-l4"><a class="reference internal" href="torchvision/datasets.html#captions">Captions</a></li>
<li class="toctree-l4"><a class="reference internal" href="torchvision/datasets.html#detection">Detection</a></li>
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<li class="toctree-l3"><a class="reference internal" href="torchvision/datasets.html#lsun">LSUN</a></li>
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<li class="toctree-l3"><a class="reference internal" href="torchvision/datasets.html#datasetfolder">DatasetFolder</a></li>
<li class="toctree-l3"><a class="reference internal" href="torchvision/datasets.html#imagenet-12">Imagenet-12</a></li>
<li class="toctree-l3"><a class="reference internal" href="torchvision/datasets.html#cifar">CIFAR</a></li>
<li class="toctree-l3"><a class="reference internal" href="torchvision/datasets.html#stl10">STL10</a></li>
<li class="toctree-l3"><a class="reference internal" href="torchvision/datasets.html#svhn">SVHN</a></li>
<li class="toctree-l3"><a class="reference internal" href="torchvision/datasets.html#phototour">PhotoTour</a></li>
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<li class="toctree-l2"><a class="reference internal" href="torchvision/models.html">torchvision.models</a><ul>
<li class="toctree-l3"><a class="reference internal" href="torchvision/models.html#id1">Alexnet</a></li>
<li class="toctree-l3"><a class="reference internal" href="torchvision/models.html#id2">VGG</a></li>
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<li class="toctree-l3"><a class="reference internal" href="torchvision/models.html#id4">SqueezeNet</a></li>
<li class="toctree-l3"><a class="reference internal" href="torchvision/models.html#id5">DenseNet</a></li>
<li class="toctree-l3"><a class="reference internal" href="torchvision/models.html#inception-v3">Inception v3</a></li>
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<li class="toctree-l2"><a class="reference internal" href="torchvision/transforms.html">torchvision.transforms</a><ul>
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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 Tensor that is to be considered a module parameter.</p>
<p>Parameters are <a class="reference internal" href="tensors.html#torch.Tensor" title="torch.Tensor"><code class="xref py py-class docutils literal"><span class="pre">Tensor</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 Tensor 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>
<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/3/library/functions.html#bool" title="(in Python v3.6)"><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. Default: <cite>True</cite></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="fm">__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 <cite>.cuda()</cite>, etc.</p>
<dl class="method">
<dt id="torch.nn.Module.add_module">
<code class="descname">add_module</code><span class="sig-paren">(</span><em>name</em>, <em>module</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/nn/modules/module.html#Module.add_module"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torch.nn.Module.add_module" title="Permalink to this definition">¶</a></dt>
<dd><p>Adds a child module to the current module.</p>
<p>The module can be accessed as an attribute using the given name.</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>name</strong> (<a class="reference external" href="https://docs.python.org/3/library/string.html#module-string" title="(in Python v3.6)"><em>string</em></a>) – name of the child module. The child module can be
accessed from this module using the given name</li>
<li><strong>parameter</strong> (<a class="reference internal" href="#torch.nn.Module" title="torch.nn.Module"><em>Module</em></a>) – child module to be added to the module.</li>
</ul>
</td>
</tr>
</tbody>
</table>
</dd></dl>
<dl class="method">
<dt id="torch.nn.Module.apply">
<code class="descname">apply</code><span class="sig-paren">(</span><em>fn</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/nn/modules/module.html#Module.apply"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torch.nn.Module.apply" title="Permalink to this definition">¶</a></dt>
<dd><p>Applies <code class="docutils literal"><span class="pre">fn</span></code> recursively to every submodule (as returned by <code class="docutils literal"><span class="pre">.children()</span></code>)
as well as self. Typical use includes initializing the parameters of a model
(see also <span class="xref std std-ref">torch-nn-init</span>).</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>fn</strong> (<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> -> None) – function to be applied to each submodule</td>
</tr>
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body">self</td>
</tr>
<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><a class="reference internal" href="#torch.nn.Module" title="torch.nn.Module">Module</a></td>
</tr>
</tbody>
</table>
<p>Example:</p>
<div class="highlight-default"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="k">def</span> <span class="nf">init_weights</span><span class="p">(</span><span class="n">m</span><span class="p">):</span>
<span class="go"> print(m)</span>
<span class="go"> if type(m) == nn.Linear:</span>
<span class="go"> m.weight.data.fill_(1.0)</span>
<span class="go"> print(m.weight)</span>
<span class="gp">>>> </span><span class="n">net</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">Linear</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">),</span> <span class="n">nn</span><span class="o">.</span><span class="n">Linear</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">))</span>
<span class="gp">>>> </span><span class="n">net</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span><span class="n">init_weights</span><span class="p">)</span>
<span class="go">Linear(in_features=2, out_features=2, bias=True)</span>
<span class="go">Parameter containing:</span>
<span class="go">tensor([[ 1., 1.],</span>
<span class="go"> [ 1., 1.]])</span>
<span class="go">Linear(in_features=2, out_features=2, bias=True)</span>
<span class="go">Parameter containing:</span>
<span class="go">tensor([[ 1., 1.],</span>
<span class="go"> [ 1., 1.]])</span>
<span class="go">Sequential(</span>
<span class="go"> (0): Linear(in_features=2, out_features=2, bias=True)</span>
<span class="go"> (1): Linear(in_features=2, out_features=2, bias=True)</span>
<span class="go">)</span>
<span class="go">Sequential(</span>
<span class="go"> (0): Linear(in_features=2, out_features=2, bias=True)</span>
<span class="go"> (1): Linear(in_features=2, out_features=2, bias=True)</span>
<span class="go">)</span>
</pre></div>
</div>
</dd></dl>
<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 immediate children modules.</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">Yields:</th><td class="field-body"><em>Module</em> – a child module</td>
</tr>
</tbody>
</table>
</dd></dl>
<dl class="method">
<dt id="torch.nn.Module.cpu">
<code class="descname">cpu</code><span class="sig-paren">(</span><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>
<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">Returns:</th><td class="field-body">self</td>
</tr>
<tr class="field-even field"><th class="field-name">Return type:</th><td class="field-body"><a class="reference internal" href="#torch.nn.Module" title="torch.nn.Module">Module</a></td>
</tr>
</tbody>
</table>
</dd></dl>
<dl class="method">
<dt id="torch.nn.Module.cuda">
<code class="descname">cuda</code><span class="sig-paren">(</span><em>device=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>
<p>This also makes associated parameters and buffers different objects. So
it should be called before constructing optimizer if the module will
live on GPU while being optimized.</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</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.6)"><em>int</em></a><em>, </em><em>optional</em>) – if specified, all parameters will be
copied to that device</td>
</tr>
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body">self</td>
</tr>
<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><a class="reference internal" href="#torch.nn.Module" title="torch.nn.Module">Module</a></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 floating point parameters and buffers to <code class="docutils literal"><span class="pre">double</span></code> datatype.</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">Returns:</th><td class="field-body">self</td>
</tr>
<tr class="field-even field"><th class="field-name">Return type:</th><td class="field-body"><a class="reference internal" href="#torch.nn.Module" title="torch.nn.Module">Module</a></td>
</tr>
</tbody>
</table>
</dd></dl>
<dl class="attribute">
<dt id="torch.nn.Module.dump_patches">
<code class="descname">dump_patches</code><em class="property"> = False</em><a class="headerlink" href="#torch.nn.Module.dump_patches" title="Permalink to this definition">¶</a></dt>
<dd><p>This allows better BC support for <a class="reference internal" href="#torch.nn.Module.load_state_dict" title="torch.nn.Module.load_state_dict"><code class="xref py py-meth docutils literal"><span class="pre">load_state_dict()</span></code></a>. In
<a class="reference internal" href="#torch.nn.Module.state_dict" title="torch.nn.Module.state_dict"><code class="xref py py-meth docutils literal"><span class="pre">state_dict()</span></code></a>, the version number will be saved as in the attribute
<cite>_metadata</cite> of the returned state dict, and thus pickled. <cite>_metadata</cite> is a
dictionary with keys follow the naming convention of state dict. See
<code class="docutils literal"><span class="pre">_load_from_state_dict</span></code> on how to use this information in loading.</p>
<p>If new parameters/buffers are added/removed from a module, this number shall
be bumped, and the module’s <cite>_load_from_state_dict</cite> method can compare the
version number and do appropriate changes if the state dict is from before
the change.</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 certain modules. See documentations of
particular modules for details of their behaviors in training/evaluation
mode, if they are affected, e.g. <a class="reference internal" href="#torch.nn.Dropout" title="torch.nn.Dropout"><code class="xref py py-class docutils literal"><span class="pre">Dropout</span></code></a>, <code class="xref py py-class docutils literal"><span class="pre">BatchNorm</span></code>,
etc.</p>
</dd></dl>
<dl class="method">
<dt id="torch.nn.Module.extra_repr">
<code class="descname">extra_repr</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/nn/modules/module.html#Module.extra_repr"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torch.nn.Module.extra_repr" title="Permalink to this definition">¶</a></dt>
<dd><p>Set the extra representation of the module</p>
<p>To print customized extra information, you should reimplement
this method in your own modules. Both single-line and multi-line
strings are acceptable.</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 floating point parameters and buffers to float datatype.</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">Returns:</th><td class="field-body">self</td>
</tr>
<tr class="field-even field"><th class="field-name">Return type:</th><td class="field-body"><a class="reference internal" href="#torch.nn.Module" title="torch.nn.Module">Module</a></td>
</tr>
</tbody>
</table>
</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 overridden by all subclasses.</p>
<div class="admonition note">
<p class="first admonition-title">Note</p>
<p class="last">Although the recipe for forward pass needs to be defined within
this function, one should call the <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> instance afterwards
instead of this since the former takes care of running the
registered hooks while the latter silently ignores them.</p>
</div>
</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 floating point parameters and buffers to <code class="docutils literal"><span class="pre">half</span></code> datatype.</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">Returns:</th><td class="field-body">self</td>
</tr>
<tr class="field-even field"><th class="field-name">Return type:</th><td class="field-body"><a class="reference internal" href="#torch.nn.Module" title="torch.nn.Module">Module</a></td>
</tr>
</tbody>
</table>
</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>, <em>strict=True</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. If <code class="xref py py-attr docutils literal"><span class="pre">strict</span></code> is <code class="docutils literal"><span class="pre">True</span></code>, then
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