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<!DOCTYPE html>
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<li class="toctree-l1"><a class="reference internal" href="notes/autograd.html">Autograd mechanics</a><ul>
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<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="#selu"><span class="hidden-section">SELU</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="#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>
</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="#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="#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="#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>
</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="#id14"><span class="hidden-section">conv1d</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="#id15"><span class="hidden-section">conv2d</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="#id16"><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>
<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="#id17"><span class="hidden-section">threshold</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="#id18"><span class="hidden-section">relu</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="#id19"><span class="hidden-section">hardtanh</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="#id20"><span class="hidden-section">relu6</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="#id21"><span class="hidden-section">elu</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="#id22"><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="#id23"><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="#glu"><span class="hidden-section">glu</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="#id24"><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="#id25"><span class="hidden-section">tanhshrink</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="#id26"><span class="hidden-section">softsign</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="#id27"><span class="hidden-section">softplus</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="#id28"><span class="hidden-section">softmin</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="#id29"><span class="hidden-section">softmax</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="#id30"><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="#id31"><span class="hidden-section">tanh</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="#id32"><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="#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="#id33"><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="#id34"><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="#id35"><span class="hidden-section">dropout2d</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="#id36"><span class="hidden-section">dropout3d</span></a></li>
</ul>
</li>
<li class="toctree-l2"><a class="reference internal" href="#id37">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="#id38">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="#id40"><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>
</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#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>
<li class="toctree-l2"><a class="reference internal" href="autograd.html#profiler">Profiler</a></li>
</ul>
</li>
<li class="toctree-l1"><a class="reference internal" href="distributions.html">torch.distributions</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 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="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 .cuda(), 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/2/library/string.html#module-string" title="(in Python v2.7)"><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 class="rubric">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="gp">>>> </span> <span class="nb">print</span><span class="p">(</span><span class="n">m</span><span class="p">)</span>
<span class="gp">>>> </span> <span class="k">if</span> <span class="nb">type</span><span class="p">(</span><span class="n">m</span><span class="p">)</span> <span class="o">==</span> <span class="n">nn</span><span class="o">.</span><span class="n">Linear</span><span class="p">:</span>
<span class="gp">>>> </span> <span class="n">m</span><span class="o">.</span><span class="n">weight</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">fill_</span><span class="p">(</span><span class="mf">1.0</span><span class="p">)</span>
<span class="gp">>>> </span> <span class="nb">print</span><span class="p">(</span><span class="n">m</span><span class="o">.</span><span class="n">weight</span><span class="p">)</span>
<span class="go">>>></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 (2 -> 2)</span>
<span class="go">Parameter containing:</span>
<span class="go"> 1 1</span>
<span class="go"> 1 1</span>
<span class="go">[torch.FloatTensor of size 2x2]</span>
<span class="go">Linear (2 -> 2)</span>
<span class="go">Parameter containing:</span>
<span class="go"> 1 1</span>
<span class="go"> 1 1</span>
<span class="go">[torch.FloatTensor of size 2x2]</span>
<span class="go">Sequential (</span>
<span class="go"> (0): Linear (2 -> 2)</span>
<span class="go"> (1): Linear (2 -> 2)</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/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>
<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 parameters and buffers to double 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.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>
<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 overriden 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 parameters and buffers to half 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
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"><ul class="first last simple">
<li><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.</li>
<li><strong>strict</strong> (<a class="reference external" href="https://docs.python.org/2/library/functions.html#bool" title="(in Python v2.7)"><em>bool</em></a>) – Strictly enforce that the keys in <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>
match the keys returned by this module’s <cite>:func:`state_dict()</cite>
function.</li>
</ul>
</td>
</tr>
</tbody>
</table>
</dd></dl>
<dl class="method">
<dt id="torch.nn.Module.modules">
<code class="descname">modules</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/nn/modules/module.html#Module.modules"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torch.nn.Module.modules" title="Permalink to this definition">¶</a></dt>
<dd><p>Returns an iterator over all modules in the network.</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 module in the network</td>
</tr>
</tbody>
</table>
<div class="admonition note">
<p class="first admonition-title">Note</p>
<p>Duplicate modules are returned only once. In the following
example, <code class="docutils literal"><span class="pre">l</span></code> will be returned only once.</p>
<div class="last highlight-default"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">l</span> <span class="o">=</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">nn</span><span class="o">.</span><span class="n">Sequential</span><span class="p">(</span><span class="n">l</span><span class="p">,</span> <span class="n">l</span><span class="p">)</span>
<span class="gp">>>> </span><span class="k">for</span> <span class="n">idx</span><span class="p">,</span> <span class="n">m</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">net</span><span class="o">.</span><span class="n">modules</span><span class="p">()):</span>
<span class="gp">>>> </span> <span class="nb">print</span><span class="p">(</span><span class="n">idx</span><span class="p">,</span> <span class="s1">'->'</span><span class="p">,</span> <span class="n">m</span><span class="p">)</span>
<span class="go">0 -> Sequential (</span>
<span class="go"> (0): Linear (2 -> 2)</span>
<span class="go"> (1): Linear (2 -> 2)</span>
<span class="go">)</span>
<span class="go">1 -> Linear (2 -> 2)</span>
</pre></div>
</div>
</div>
</dd></dl>
<dl class="method">
<dt id="torch.nn.Module.named_children">
<code class="descname">named_children</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/nn/modules/module.html#Module.named_children"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torch.nn.Module.named_children" title="Permalink to this definition">¶</a></dt>
<dd><p>Returns an iterator over immediate children modules, yielding both
the name of the module as well as the module itself.</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>(string, Module)</em> – Tuple containing a name and child module</td>
</tr>
</tbody>
</table>
<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">name</span><span class="p">,</span> <span class="n">module</span> <span class="ow">in</span> <span class="n">model</span><span class="o">.</span><span class="n">named_children</span><span class="p">():</span>
<span class="gp">>>> </span> <span class="k">if</span> <span class="n">name</span> <span class="ow">in</span> <span class="p">[</span><span class="s1">'conv4'</span><span class="p">,</span> <span class="s1">'conv5'</span><span class="p">]:</span>
<span class="gp">>>> </span> <span class="nb">print</span><span class="p">(</span><span class="n">module</span><span class="p">)</span>
</pre></div>
</div>
</dd></dl>
<dl class="method">
<dt id="torch.nn.Module.named_modules">
<code class="descname">named_modules</code><span class="sig-paren">(</span><em>memo=None</em>, <em>prefix=''</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/nn/modules/module.html#Module.named_modules"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torch.nn.Module.named_modules" title="Permalink to this definition">¶</a></dt>
<dd><p>Returns an iterator over all modules in the network, yielding
both the name of the module as well as the module itself.</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>(string, Module)</em> – Tuple of name and module</td>
</tr>
</tbody>
</table>
<div class="admonition note">
<p class="first admonition-title">Note</p>
<p>Duplicate modules are returned only once. In the following
example, <code class="docutils literal"><span class="pre">l</span></code> will be returned only once.</p>
<div class="last highlight-default"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">l</span> <span class="o">=</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">nn</span><span class="o">.</span><span class="n">Sequential</span><span class="p">(</span><span class="n">l</span><span class="p">,</span> <span class="n">l</span><span class="p">)</span>
<span class="gp">>>> </span><span class="k">for</span> <span class="n">idx</span><span class="p">,</span> <span class="n">m</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">net</span><span class="o">.</span><span class="n">named_modules</span><span class="p">()):</span>
<span class="gp">>>> </span> <span class="nb">print</span><span class="p">(</span><span class="n">idx</span><span class="p">,</span> <span class="s1">'->'</span><span class="p">,</span> <span class="n">m</span><span class="p">)</span>
<span class="go">0 -> ('', Sequential (</span>
<span class="go"> (0): Linear (2 -> 2)</span>
<span class="go"> (1): Linear (2 -> 2)</span>
<span class="go">))</span>
<span class="go">1 -> ('0', Linear (2 -> 2))</span>
</pre></div>
</div>
</div>
</dd></dl>
<dl class="method">
<dt id="torch.nn.Module.named_parameters">
<code class="descname">named_parameters</code><span class="sig-paren">(</span><em>memo=None</em>, <em>prefix=''</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/nn/modules/module.html#Module.named_parameters"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torch.nn.Module.named_parameters" title="Permalink to this definition">¶</a></dt>
<dd><p>Returns an iterator over module parameters, yielding both the
name of the parameter as well as the parameter itself</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>(string, Parameter)</em> – Tuple containing the name and parameter</td>
</tr>
</tbody>
</table>
<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">name</span><span class="p">,</span> <span class="n">param</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">named_parameters</span><span class="p">():</span>
<span class="gp">>>> </span> <span class="k">if</span> <span class="n">name</span> <span class="ow">in</span> <span class="p">[</span><span class="s1">'bias'</span><span class="p">]:</span>
<span class="gp">>>> </span> <span class="nb">print</span><span class="p">(</span><span class="n">param</span><span class="o">.</span><span class="n">size</span><span class="p">())</span>
</pre></div>
</div>
</dd></dl>
<dl class="method">
<dt id="torch.nn.Module.parameters">
<code class="descname">parameters</code><span class="sig-paren">(</span><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>
<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>Parameter</em> – module parameter</td>
</tr>
</tbody>
</table>
<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>
<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">a handle that can be used to remove the added hook by calling
<code class="docutils literal"><span class="pre">handle.remove()</span></code></td>
</tr>
<tr class="field-even field"><th class="field-name">Return type:</th><td class="field-body"><code class="xref py py-class docutils literal"><span class="pre">torch.utils.hooks.RemovableHandle</span></code></td>
</tr>
</tbody>
</table>