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<!DOCTYPE html>
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<p class="caption"><span class="caption-text">Notes</span></p>
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<li class="toctree-l1"><a class="reference internal" href="notes/autograd.html">Autograd mechanics</a><ul>
<li class="toctree-l2"><a class="reference internal" href="notes/autograd.html#excluding-subgraphs-from-backward">Excluding subgraphs from backward</a><ul>
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<li class="toctree-l1"><a class="reference internal" href="notes/extending.html">Extending PyTorch</a><ul>
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<li class="toctree-l2"><a class="reference internal" href="notes/faq.html#my-model-reports-cuda-runtime-error-2-out-of-memory">My model reports “cuda runtime error(2): out of memory”</a></li>
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<li class="toctree-l1"><a class="reference internal" href="notes/multiprocessing.html">Multiprocessing best practices</a><ul>
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<li class="toctree-l2"><a class="reference internal" href="#math-operations">Math operations</a><ul>
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</ul>
</li>
<li class="toctree-l2"><a class="reference internal" href="nn.html#convolution-layers">Convolution layers</a><ul>
<li class="toctree-l3"><a class="reference internal" href="nn.html#conv1d"><span class="hidden-section">Conv1d</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#conv2d"><span class="hidden-section">Conv2d</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#conv3d"><span class="hidden-section">Conv3d</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#convtranspose1d"><span class="hidden-section">ConvTranspose1d</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#convtranspose2d"><span class="hidden-section">ConvTranspose2d</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#convtranspose3d"><span class="hidden-section">ConvTranspose3d</span></a></li>
</ul>
</li>
<li class="toctree-l2"><a class="reference internal" href="nn.html#pooling-layers">Pooling layers</a><ul>
<li class="toctree-l3"><a class="reference internal" href="nn.html#maxpool1d"><span class="hidden-section">MaxPool1d</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#maxpool2d"><span class="hidden-section">MaxPool2d</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#maxpool3d"><span class="hidden-section">MaxPool3d</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#maxunpool1d"><span class="hidden-section">MaxUnpool1d</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#maxunpool2d"><span class="hidden-section">MaxUnpool2d</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#maxunpool3d"><span class="hidden-section">MaxUnpool3d</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#avgpool1d"><span class="hidden-section">AvgPool1d</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#avgpool2d"><span class="hidden-section">AvgPool2d</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#avgpool3d"><span class="hidden-section">AvgPool3d</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#fractionalmaxpool2d"><span class="hidden-section">FractionalMaxPool2d</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#lppool1d"><span class="hidden-section">LPPool1d</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#lppool2d"><span class="hidden-section">LPPool2d</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#adaptivemaxpool1d"><span class="hidden-section">AdaptiveMaxPool1d</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#adaptivemaxpool2d"><span class="hidden-section">AdaptiveMaxPool2d</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#adaptivemaxpool3d"><span class="hidden-section">AdaptiveMaxPool3d</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#adaptiveavgpool1d"><span class="hidden-section">AdaptiveAvgPool1d</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#adaptiveavgpool2d"><span class="hidden-section">AdaptiveAvgPool2d</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#adaptiveavgpool3d"><span class="hidden-section">AdaptiveAvgPool3d</span></a></li>
</ul>
</li>
<li class="toctree-l2"><a class="reference internal" href="nn.html#padding-layers">Padding layers</a><ul>
<li class="toctree-l3"><a class="reference internal" href="nn.html#reflectionpad1d"><span class="hidden-section">ReflectionPad1d</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#reflectionpad2d"><span class="hidden-section">ReflectionPad2d</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#replicationpad1d"><span class="hidden-section">ReplicationPad1d</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#replicationpad2d"><span class="hidden-section">ReplicationPad2d</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#replicationpad3d"><span class="hidden-section">ReplicationPad3d</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#zeropad2d"><span class="hidden-section">ZeroPad2d</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#constantpad1d"><span class="hidden-section">ConstantPad1d</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#constantpad2d"><span class="hidden-section">ConstantPad2d</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#constantpad3d"><span class="hidden-section">ConstantPad3d</span></a></li>
</ul>
</li>
<li class="toctree-l2"><a class="reference internal" href="nn.html#non-linear-activations-weighted-sum-nonlinearity">Non-linear activations (weighted sum, nonlinearity)</a><ul>
<li class="toctree-l3"><a class="reference internal" href="nn.html#elu"><span class="hidden-section">ELU</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#hardshrink"><span class="hidden-section">Hardshrink</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#hardtanh"><span class="hidden-section">Hardtanh</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#leakyrelu"><span class="hidden-section">LeakyReLU</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#logsigmoid"><span class="hidden-section">LogSigmoid</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#prelu"><span class="hidden-section">PReLU</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#relu"><span class="hidden-section">ReLU</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#relu6"><span class="hidden-section">ReLU6</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#rrelu"><span class="hidden-section">RReLU</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#selu"><span class="hidden-section">SELU</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#sigmoid"><span class="hidden-section">Sigmoid</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#softplus"><span class="hidden-section">Softplus</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#softshrink"><span class="hidden-section">Softshrink</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#softsign"><span class="hidden-section">Softsign</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#tanh"><span class="hidden-section">Tanh</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#tanhshrink"><span class="hidden-section">Tanhshrink</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#threshold"><span class="hidden-section">Threshold</span></a></li>
</ul>
</li>
<li class="toctree-l2"><a class="reference internal" href="nn.html#non-linear-activations-other">Non-linear activations (other)</a><ul>
<li class="toctree-l3"><a class="reference internal" href="nn.html#softmin"><span class="hidden-section">Softmin</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#softmax"><span class="hidden-section">Softmax</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#softmax2d"><span class="hidden-section">Softmax2d</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#logsoftmax"><span class="hidden-section">LogSoftmax</span></a></li>
</ul>
</li>
<li class="toctree-l2"><a class="reference internal" href="nn.html#normalization-layers">Normalization layers</a><ul>
<li class="toctree-l3"><a class="reference internal" href="nn.html#batchnorm1d"><span class="hidden-section">BatchNorm1d</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#batchnorm2d"><span class="hidden-section">BatchNorm2d</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#batchnorm3d"><span class="hidden-section">BatchNorm3d</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#instancenorm1d"><span class="hidden-section">InstanceNorm1d</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#instancenorm2d"><span class="hidden-section">InstanceNorm2d</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#instancenorm3d"><span class="hidden-section">InstanceNorm3d</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#layernorm"><span class="hidden-section">LayerNorm</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#localresponsenorm"><span class="hidden-section">LocalResponseNorm</span></a></li>
</ul>
</li>
<li class="toctree-l2"><a class="reference internal" href="nn.html#recurrent-layers">Recurrent layers</a><ul>
<li class="toctree-l3"><a class="reference internal" href="nn.html#rnn"><span class="hidden-section">RNN</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#lstm"><span class="hidden-section">LSTM</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#gru"><span class="hidden-section">GRU</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#rnncell"><span class="hidden-section">RNNCell</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#lstmcell"><span class="hidden-section">LSTMCell</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#grucell"><span class="hidden-section">GRUCell</span></a></li>
</ul>
</li>
<li class="toctree-l2"><a class="reference internal" href="nn.html#linear-layers">Linear layers</a><ul>
<li class="toctree-l3"><a class="reference internal" href="nn.html#linear"><span class="hidden-section">Linear</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#bilinear"><span class="hidden-section">Bilinear</span></a></li>
</ul>
</li>
<li class="toctree-l2"><a class="reference internal" href="nn.html#dropout-layers">Dropout layers</a><ul>
<li class="toctree-l3"><a class="reference internal" href="nn.html#dropout"><span class="hidden-section">Dropout</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#dropout2d"><span class="hidden-section">Dropout2d</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#dropout3d"><span class="hidden-section">Dropout3d</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#alphadropout"><span class="hidden-section">AlphaDropout</span></a></li>
</ul>
</li>
<li class="toctree-l2"><a class="reference internal" href="nn.html#sparse-layers">Sparse layers</a><ul>
<li class="toctree-l3"><a class="reference internal" href="nn.html#embedding"><span class="hidden-section">Embedding</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#embeddingbag"><span class="hidden-section">EmbeddingBag</span></a></li>
</ul>
</li>
<li class="toctree-l2"><a class="reference internal" href="nn.html#distance-functions">Distance functions</a><ul>
<li class="toctree-l3"><a class="reference internal" href="nn.html#cosinesimilarity"><span class="hidden-section">CosineSimilarity</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#pairwisedistance"><span class="hidden-section">PairwiseDistance</span></a></li>
</ul>
</li>
<li class="toctree-l2"><a class="reference internal" href="nn.html#loss-functions">Loss functions</a><ul>
<li class="toctree-l3"><a class="reference internal" href="nn.html#l1loss"><span class="hidden-section">L1Loss</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#mseloss"><span class="hidden-section">MSELoss</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#crossentropyloss"><span class="hidden-section">CrossEntropyLoss</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#nllloss"><span class="hidden-section">NLLLoss</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#poissonnllloss"><span class="hidden-section">PoissonNLLLoss</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#kldivloss"><span class="hidden-section">KLDivLoss</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#bceloss"><span class="hidden-section">BCELoss</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#bcewithlogitsloss"><span class="hidden-section">BCEWithLogitsLoss</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#marginrankingloss"><span class="hidden-section">MarginRankingLoss</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#hingeembeddingloss"><span class="hidden-section">HingeEmbeddingLoss</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#multilabelmarginloss"><span class="hidden-section">MultiLabelMarginLoss</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#smoothl1loss"><span class="hidden-section">SmoothL1Loss</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#softmarginloss"><span class="hidden-section">SoftMarginLoss</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#multilabelsoftmarginloss"><span class="hidden-section">MultiLabelSoftMarginLoss</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#cosineembeddingloss"><span class="hidden-section">CosineEmbeddingLoss</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#multimarginloss"><span class="hidden-section">MultiMarginLoss</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#tripletmarginloss"><span class="hidden-section">TripletMarginLoss</span></a></li>
</ul>
</li>
<li class="toctree-l2"><a class="reference internal" href="nn.html#vision-layers">Vision layers</a><ul>
<li class="toctree-l3"><a class="reference internal" href="nn.html#pixelshuffle"><span class="hidden-section">PixelShuffle</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#upsample"><span class="hidden-section">Upsample</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#upsamplingnearest2d"><span class="hidden-section">UpsamplingNearest2d</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#upsamplingbilinear2d"><span class="hidden-section">UpsamplingBilinear2d</span></a></li>
</ul>
</li>
<li class="toctree-l2"><a class="reference internal" href="nn.html#dataparallel-layers-multi-gpu-distributed">DataParallel layers (multi-GPU, distributed)</a><ul>
<li class="toctree-l3"><a class="reference internal" href="nn.html#dataparallel"><span class="hidden-section">DataParallel</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#distributeddataparallel"><span class="hidden-section">DistributedDataParallel</span></a></li>
</ul>
</li>
<li class="toctree-l2"><a class="reference internal" href="nn.html#utilities">Utilities</a><ul>
<li class="toctree-l3"><a class="reference internal" href="nn.html#clip-grad-norm"><span class="hidden-section">clip_grad_norm_</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#clip-grad-value"><span class="hidden-section">clip_grad_value_</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#weight-norm"><span class="hidden-section">weight_norm</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#remove-weight-norm"><span class="hidden-section">remove_weight_norm</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#packedsequence"><span class="hidden-section">PackedSequence</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#pack-padded-sequence"><span class="hidden-section">pack_padded_sequence</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#pad-packed-sequence"><span class="hidden-section">pad_packed_sequence</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#pad-sequence"><span class="hidden-section">pad_sequence</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#pack-sequence"><span class="hidden-section">pack_sequence</span></a></li>
</ul>
</li>
</ul>
</li>
<li class="toctree-l1"><a class="reference internal" href="nn.html#torch-nn-functional">torch.nn.functional</a><ul>
<li class="toctree-l2"><a class="reference internal" href="nn.html#convolution-functions">Convolution functions</a><ul>
<li class="toctree-l3"><a class="reference internal" href="nn.html#id20"><span class="hidden-section">conv1d</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#id21"><span class="hidden-section">conv2d</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#id22"><span class="hidden-section">conv3d</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#conv-transpose1d"><span class="hidden-section">conv_transpose1d</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#conv-transpose2d"><span class="hidden-section">conv_transpose2d</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#conv-transpose3d"><span class="hidden-section">conv_transpose3d</span></a></li>
</ul>
</li>
<li class="toctree-l2"><a class="reference internal" href="nn.html#pooling-functions">Pooling functions</a><ul>
<li class="toctree-l3"><a class="reference internal" href="nn.html#avg-pool1d"><span class="hidden-section">avg_pool1d</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#avg-pool2d"><span class="hidden-section">avg_pool2d</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#avg-pool3d"><span class="hidden-section">avg_pool3d</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#max-pool1d"><span class="hidden-section">max_pool1d</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#max-pool2d"><span class="hidden-section">max_pool2d</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#max-pool3d"><span class="hidden-section">max_pool3d</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#max-unpool1d"><span class="hidden-section">max_unpool1d</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#max-unpool2d"><span class="hidden-section">max_unpool2d</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#max-unpool3d"><span class="hidden-section">max_unpool3d</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#lp-pool1d"><span class="hidden-section">lp_pool1d</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#lp-pool2d"><span class="hidden-section">lp_pool2d</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#adaptive-max-pool1d"><span class="hidden-section">adaptive_max_pool1d</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#adaptive-max-pool2d"><span class="hidden-section">adaptive_max_pool2d</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#adaptive-max-pool3d"><span class="hidden-section">adaptive_max_pool3d</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#adaptive-avg-pool1d"><span class="hidden-section">adaptive_avg_pool1d</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#adaptive-avg-pool2d"><span class="hidden-section">adaptive_avg_pool2d</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#adaptive-avg-pool3d"><span class="hidden-section">adaptive_avg_pool3d</span></a></li>
</ul>
</li>
<li class="toctree-l2"><a class="reference internal" href="nn.html#non-linear-activation-functions">Non-linear activation functions</a><ul>
<li class="toctree-l3"><a class="reference internal" href="nn.html#id23"><span class="hidden-section">threshold</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#id24"><span class="hidden-section">relu</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#id25"><span class="hidden-section">hardtanh</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#id26"><span class="hidden-section">relu6</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#id27"><span class="hidden-section">elu</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#id28"><span class="hidden-section">selu</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#leaky-relu"><span class="hidden-section">leaky_relu</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#id29"><span class="hidden-section">prelu</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#id30"><span class="hidden-section">rrelu</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#glu"><span class="hidden-section">glu</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#id31"><span class="hidden-section">logsigmoid</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#id32"><span class="hidden-section">hardshrink</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#id33"><span class="hidden-section">tanhshrink</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#id34"><span class="hidden-section">softsign</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#id35"><span class="hidden-section">softplus</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#id36"><span class="hidden-section">softmin</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#id37"><span class="hidden-section">softmax</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#id38"><span class="hidden-section">softshrink</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#log-softmax"><span class="hidden-section">log_softmax</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#id39"><span class="hidden-section">tanh</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#id40"><span class="hidden-section">sigmoid</span></a></li>
</ul>
</li>
<li class="toctree-l2"><a class="reference internal" href="nn.html#normalization-functions">Normalization functions</a><ul>
<li class="toctree-l3"><a class="reference internal" href="nn.html#batch-norm"><span class="hidden-section">batch_norm</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#instance-norm"><span class="hidden-section">instance_norm</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#layer-norm"><span class="hidden-section">layer_norm</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#local-response-norm"><span class="hidden-section">local_response_norm</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#normalize"><span class="hidden-section">normalize</span></a></li>
</ul>
</li>
<li class="toctree-l2"><a class="reference internal" href="nn.html#linear-functions">Linear functions</a><ul>
<li class="toctree-l3"><a class="reference internal" href="nn.html#id41"><span class="hidden-section">linear</span></a></li>
</ul>
</li>
<li class="toctree-l2"><a class="reference internal" href="nn.html#dropout-functions">Dropout functions</a><ul>
<li class="toctree-l3"><a class="reference internal" href="nn.html#id42"><span class="hidden-section">dropout</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#alpha-dropout"><span class="hidden-section">alpha_dropout</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#id43"><span class="hidden-section">dropout2d</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#id44"><span class="hidden-section">dropout3d</span></a></li>
</ul>
</li>
<li class="toctree-l2"><a class="reference internal" href="nn.html#id45">Distance functions</a><ul>
<li class="toctree-l3"><a class="reference internal" href="nn.html#pairwise-distance"><span class="hidden-section">pairwise_distance</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#cosine-similarity"><span class="hidden-section">cosine_similarity</span></a></li>
</ul>
</li>
<li class="toctree-l2"><a class="reference internal" href="nn.html#id46">Loss functions</a><ul>
<li class="toctree-l3"><a class="reference internal" href="nn.html#binary-cross-entropy"><span class="hidden-section">binary_cross_entropy</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#poisson-nll-loss"><span class="hidden-section">poisson_nll_loss</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#cosine-embedding-loss"><span class="hidden-section">cosine_embedding_loss</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#cross-entropy"><span class="hidden-section">cross_entropy</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#hinge-embedding-loss"><span class="hidden-section">hinge_embedding_loss</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#kl-div"><span class="hidden-section">kl_div</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#l1-loss"><span class="hidden-section">l1_loss</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#mse-loss"><span class="hidden-section">mse_loss</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#margin-ranking-loss"><span class="hidden-section">margin_ranking_loss</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#multilabel-margin-loss"><span class="hidden-section">multilabel_margin_loss</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#multilabel-soft-margin-loss"><span class="hidden-section">multilabel_soft_margin_loss</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#multi-margin-loss"><span class="hidden-section">multi_margin_loss</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#nll-loss"><span class="hidden-section">nll_loss</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#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="nn.html#smooth-l1-loss"><span class="hidden-section">smooth_l1_loss</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#soft-margin-loss"><span class="hidden-section">soft_margin_loss</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#triplet-margin-loss"><span class="hidden-section">triplet_margin_loss</span></a></li>
</ul>
</li>
<li class="toctree-l2"><a class="reference internal" href="nn.html#vision-functions">Vision functions</a><ul>
<li class="toctree-l3"><a class="reference internal" href="nn.html#pixel-shuffle"><span class="hidden-section">pixel_shuffle</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#pad"><span class="hidden-section">pad</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#id47"><span class="hidden-section">upsample</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#upsample-nearest"><span class="hidden-section">upsample_nearest</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#upsample-bilinear"><span class="hidden-section">upsample_bilinear</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#grid-sample"><span class="hidden-section">grid_sample</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#affine-grid"><span class="hidden-section">affine_grid</span></a></li>
</ul>
</li>
<li class="toctree-l2"><a class="reference internal" href="nn.html#dataparallel-functions-multi-gpu-distributed">DataParallel functions (multi-GPU, distributed)</a><ul>
<li class="toctree-l3"><a class="reference internal" href="nn.html#data-parallel"><span class="hidden-section">data_parallel</span></a></li>
</ul>
</li>
</ul>
</li>
<li class="toctree-l1"><a class="reference internal" href="nn.html#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>
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<li class="toctree-l2"><a class="reference internal" href="optim.html#algorithms">Algorithms</a></li>
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<li class="toctree-l1"><a class="reference internal" href="autograd.html">torch.autograd</a><ul>
<li class="toctree-l2"><a class="reference internal" href="autograd.html#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-l2"><a class="reference internal" href="autograd.html#variable-deprecated">Variable (deprecated)</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>
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<li class="toctree-l2"><a class="reference internal" href="distributions.html#chi2"><span class="hidden-section">Chi2</span></a></li>
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<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>
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<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>
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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>
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<li class="toctree-l2"><a class="reference internal" href="distributed.html#multi-gpu-collective-functions">Multi-GPU collective functions</a></li>
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<li class="toctree-l1"><a class="reference internal" href="bottleneck.html">torch.utils.bottleneck</a></li>
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<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>
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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#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>
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<li class="toctree-l3"><a class="reference internal" href="torchvision/models.html#id1">Alexnet</a></li>
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<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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<div class="section" id="module-torch">
<span id="torch"></span><h1>torch<a class="headerlink" href="#module-torch" title="Permalink to this headline">¶</a></h1>
<div class="section" id="tensors">
<h2>Tensors<a class="headerlink" href="#tensors" title="Permalink to this headline">¶</a></h2>
<dl class="function">
<dt id="torch.is_tensor">
<code class="descclassname">torch.</code><code class="descname">is_tensor</code><span class="sig-paren">(</span><em>obj</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch.html#is_tensor"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torch.is_tensor" title="Permalink to this definition">¶</a></dt>
<dd><p>Returns True if <cite>obj</cite> is a PyTorch tensor.</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>obj</strong> (<em>Object</em>) – Object to test</td>
</tr>
</tbody>
</table>
</dd></dl>
<dl class="function">
<dt id="torch.is_storage">
<code class="descclassname">torch.</code><code class="descname">is_storage</code><span class="sig-paren">(</span><em>obj</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch.html#is_storage"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torch.is_storage" title="Permalink to this definition">¶</a></dt>
<dd><p>Returns True if <cite>obj</cite> is a PyTorch storage object.</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>obj</strong> (<em>Object</em>) – Object to test</td>
</tr>
</tbody>
</table>
</dd></dl>
<dl class="function">
<dt id="torch.set_default_dtype">
<code class="descclassname">torch.</code><code class="descname">set_default_dtype</code><span class="sig-paren">(</span><em>d</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch.html#set_default_dtype"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torch.set_default_dtype" title="Permalink to this definition">¶</a></dt>
<dd><p>Sets the default floating point dtype to <code class="xref py py-attr docutils literal"><span class="pre">d</span></code>. This type will be
used as default floating point type for type inference in
<a class="reference internal" href="#torch.tensor" title="torch.tensor"><code class="xref py py-func docutils literal"><span class="pre">torch.tensor()</span></code></a>.</p>
<p>The default floating point dtype is initially <code class="docutils literal"><span class="pre">torch.float32</span></code>.</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>d</strong> (<a class="reference internal" href="tensor_attributes.html#torch.torch.dtype" title="torch.torch.dtype"><code class="xref py py-class docutils literal"><span class="pre">torch.dtype</span></code></a>) – the floating point dtype to make the default</td>
</tr>
</tbody>
</table>
<p>Example:</p>
<div class="highlight-default"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">torch</span><span class="o">.</span><span class="n">tensor</span><span class="p">([</span><span class="mf">1.2</span><span class="p">,</span> <span class="mi">3</span><span class="p">])</span><span class="o">.</span><span class="n">dtype</span> <span class="c1"># initial default for floating point is torch.float32</span>
<span class="go">torch.float32</span>
<span class="gp">>>> </span><span class="n">torch</span><span class="o">.</span><span class="n">set_default_dtype</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">float64</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">torch</span><span class="o">.</span><span class="n">tensor</span><span class="p">([</span><span class="mf">1.2</span><span class="p">,</span> <span class="mi">3</span><span class="p">])</span><span class="o">.</span><span class="n">dtype</span> <span class="c1"># a new floating point tensor</span>
<span class="go">torch.float64</span>
</pre></div>
</div>
</dd></dl>
<dl class="function">
<dt id="torch.get_default_dtype">
<code class="descclassname">torch.</code><code class="descname">get_default_dtype</code><span class="sig-paren">(</span><span class="sig-paren">)</span> → :class:`torch.dtype`<a class="headerlink" href="#torch.get_default_dtype" title="Permalink to this definition">¶</a></dt>
<dd><p>Get the current default floating point <a class="reference internal" href="tensor_attributes.html#torch.torch.dtype" title="torch.torch.dtype"><code class="xref py py-class docutils literal"><span class="pre">torch.dtype</span></code></a>.</p>
<p>Example:</p>
<div class="highlight-default"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">torch</span><span class="o">.</span><span class="n">get_default_dtype</span><span class="p">()</span> <span class="c1"># initial default for floating point is torch.float32</span>
<span class="go">torch.float32</span>
<span class="gp">>>> </span><span class="n">torch</span><span class="o">.</span><span class="n">set_default_dtype</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">float64</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">torch</span><span class="o">.</span><span class="n">get_default_dtype</span><span class="p">()</span> <span class="c1"># default is now changed to torch.float64</span>
<span class="go">torch.float64</span>
<span class="gp">>>> </span><span class="n">torch</span><span class="o">.</span><span class="n">set_default_tensor_type</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">FloatTensor</span><span class="p">)</span> <span class="c1"># setting tensor type also affects this</span>
<span class="gp">>>> </span><span class="n">torch</span><span class="o">.</span><span class="n">get_default_dtype</span><span class="p">()</span> <span class="c1"># changed to torch.float32, the dtype for torch.FloatTensor</span>
<span class="go">torch.float32</span>
</pre></div>
</div>
</dd></dl>
<dl class="function">
<dt id="torch.set_default_tensor_type">
<code class="descclassname">torch.</code><code class="descname">set_default_tensor_type</code><span class="sig-paren">(</span><em>t</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch.html#set_default_tensor_type"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torch.set_default_tensor_type" title="Permalink to this definition">¶</a></dt>
<dd><p>Sets the default <code class="docutils literal"><span class="pre">torch.Tensor</span></code> type to floating point tensor type
<a class="reference internal" href="#torch.t" title="torch.t"><code class="xref py py-attr docutils literal"><span class="pre">t</span></code></a>. This type will also be used as default floating point type for
type inference in <a class="reference internal" href="#torch.tensor" title="torch.tensor"><code class="xref py py-func docutils literal"><span class="pre">torch.tensor()</span></code></a>.</p>
<p>The default floating point tensor type is initially <code class="docutils literal"><span class="pre">torch.FloatTensor</span></code>.</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>t</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#type" title="(in Python v3.6)"><em>type</em></a><em> or </em><a class="reference external" href="https://docs.python.org/3/library/string.html#module-string" title="(in Python v3.6)"><em>string</em></a>) – the floating point tensor type or its name</td>
</tr>
</tbody>
</table>
<p>Example:</p>
<div class="highlight-default"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">torch</span><span class="o">.</span><span class="n">tensor</span><span class="p">([</span><span class="mf">1.2</span><span class="p">,</span> <span class="mi">3</span><span class="p">])</span><span class="o">.</span><span class="n">dtype</span> <span class="c1"># initial default for floating point is torch.float32</span>
<span class="go">torch.float32</span>
<span class="gp">>>> </span><span class="n">torch</span><span class="o">.</span><span class="n">set_default_tensor_type</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">DoubleTensor</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">torch</span><span class="o">.</span><span class="n">tensor</span><span class="p">([</span><span class="mf">1.2</span><span class="p">,</span> <span class="mi">3</span><span class="p">])</span><span class="o">.</span><span class="n">dtype</span> <span class="c1"># a new floating point tensor</span>
<span class="go">torch.float64</span>
</pre></div>
</div>
</dd></dl>
<dl class="function">
<dt id="torch.numel">
<code class="descclassname">torch.</code><code class="descname">numel</code><span class="sig-paren">(</span><em>input</em><span class="sig-paren">)</span> → int<a class="headerlink" href="#torch.numel" title="Permalink to this definition">¶</a></dt>
<dd><p>Returns the total number of elements in the <code class="xref py py-attr docutils literal"><span class="pre">input</span></code> tensor.</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>input</strong> (<a class="reference internal" href="tensors.html#torch.Tensor" title="torch.Tensor"><em>Tensor</em></a>) – the input tensor</td>
</tr>
</tbody>
</table>
<p>Example:</p>
<div class="highlight-default"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">a</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="mi">5</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">torch</span><span class="o">.</span><span class="n">numel</span><span class="p">(</span><span class="n">a</span><span class="p">)</span>
<span class="go">120</span>
<span class="gp">>>> </span><span class="n">a</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="mi">4</span><span class="p">,</span><span class="mi">4</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">torch</span><span class="o">.</span><span class="n">numel</span><span class="p">(</span><span class="n">a</span><span class="p">)</span>
<span class="go">16</span>
</pre></div>
</div>
</dd></dl>
<dl class="function">
<dt id="torch.set_printoptions">
<code class="descclassname">torch.</code><code class="descname">set_printoptions</code><span class="sig-paren">(</span><em>precision=None</em>, <em>threshold=None</em>, <em>edgeitems=None</em>, <em>linewidth=None</em>, <em>profile=None</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/_tensor_str.html#set_printoptions"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torch.set_printoptions" title="Permalink to this definition">¶</a></dt>
<dd><p>Set options for printing. Items shamelessly taken from NumPy</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>precision</strong> – Number of digits of precision for floating point output
(default = 8).</li>
<li><strong>threshold</strong> – Total number of array elements which trigger summarization
rather than full <cite>repr</cite> (default = 1000).</li>
<li><strong>edgeitems</strong> – Number of array items in summary at beginning and end of
each dimension (default = 3).</li>
<li><strong>linewidth</strong> – The number of characters per line for the purpose of
inserting line breaks (default = 80). Thresholded matrices will
ignore this parameter.</li>
<li><strong>profile</strong> – Sane defaults for pretty printing. Can override with any of
the above options. (any one of <cite>default</cite>, <cite>short</cite>, <cite>full</cite>)</li>
</ul>
</td>
</tr>
</tbody>
</table>
</dd></dl>
<dl class="function">
<dt id="torch.set_flush_denormal">
<code class="descclassname">torch.</code><code class="descname">set_flush_denormal</code><span class="sig-paren">(</span><em>mode</em><span class="sig-paren">)</span> → bool<a class="headerlink" href="#torch.set_flush_denormal" title="Permalink to this definition">¶</a></dt>
<dd><p>Disables denormal floating numbers on CPU.</p>
<p>Returns <code class="docutils literal"><span class="pre">True</span></code> if your system supports flushing denormal numbers and it
successfully configures flush denormal mode. <a class="reference internal" href="#torch.set_flush_denormal" title="torch.set_flush_denormal"><code class="xref py py-meth docutils literal"><span class="pre">set_flush_denormal()</span></code></a>
is only supported on x86 architectures supporting SSE3.</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>mode</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.6)"><em>bool</em></a>) – Controls whether to enable flush denormal mode or not</td>
</tr>
</tbody>
</table>
<p>Example:</p>
<div class="highlight-default"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">torch</span><span class="o">.</span><span class="n">set_flush_denormal</span><span class="p">(</span><span class="kc">True</span><span class="p">)</span>
<span class="go">True</span>
<span class="gp">>>> </span><span class="n">torch</span><span class="o">.</span><span class="n">tensor</span><span class="p">([</span><span class="mf">1e-323</span><span class="p">],</span> <span class="n">dtype</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">float64</span><span class="p">)</span>
<span class="go">tensor([ 0.], dtype=torch.float64)</span>
<span class="gp">>>> </span><span class="n">torch</span><span class="o">.</span><span class="n">set_flush_denormal</span><span class="p">(</span><span class="kc">False</span><span class="p">)</span>
<span class="go">True</span>
<span class="gp">>>> </span><span class="n">torch</span><span class="o">.</span><span class="n">tensor</span><span class="p">([</span><span class="mf">1e-323</span><span class="p">],</span> <span class="n">dtype</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">float64</span><span class="p">)</span>
<span class="go">tensor(9.88131e-324 *</span>
<span class="go"> [ 1.0000], dtype=torch.float64)</span>
</pre></div>
</div>
</dd></dl>
<div class="section" id="creation-ops">
<span id="tensor-creation-ops"></span><h3>Creation Ops<a class="headerlink" href="#creation-ops" title="Permalink to this headline">¶</a></h3>
<div class="admonition note">
<p class="first admonition-title">Note</p>
<p class="last">Random sampling creation ops are listed under <a class="reference internal" href="#random-sampling"><span class="std std-ref">Random sampling</span></a> and
include:
<a class="reference internal" href="#torch.rand" title="torch.rand"><code class="xref py py-func docutils literal"><span class="pre">torch.rand()</span></code></a>
<a class="reference internal" href="#torch.rand_like" title="torch.rand_like"><code class="xref py py-func docutils literal"><span class="pre">torch.rand_like()</span></code></a>
<a class="reference internal" href="#torch.randn" title="torch.randn"><code class="xref py py-func docutils literal"><span class="pre">torch.randn()</span></code></a>
<a class="reference internal" href="#torch.randn_like" title="torch.randn_like"><code class="xref py py-func docutils literal"><span class="pre">torch.randn_like()</span></code></a>
<a class="reference internal" href="#torch.randint" title="torch.randint"><code class="xref py py-func docutils literal"><span class="pre">torch.randint()</span></code></a>
<a class="reference internal" href="#torch.randint_like" title="torch.randint_like"><code class="xref py py-func docutils literal"><span class="pre">torch.randint_like()</span></code></a>
<a class="reference internal" href="#torch.randperm" title="torch.randperm"><code class="xref py py-func docutils literal"><span class="pre">torch.randperm()</span></code></a>
You may also use <a class="reference internal" href="#torch.empty" title="torch.empty"><code class="xref py py-func docutils literal"><span class="pre">torch.empty()</span></code></a> with the <a class="reference internal" href="#inplace-random-sampling"><span class="std std-ref">In-place random sampling</span></a>
methods to create <a class="reference internal" href="tensors.html#torch.Tensor" title="torch.Tensor"><code class="xref py py-class docutils literal"><span class="pre">torch.Tensor</span></code></a> s with values sampled from a broader
range of distributions.</p>
</div>
<dl class="function">
<dt id="torch.tensor">
<code class="descclassname">torch.</code><code class="descname">tensor</code><span class="sig-paren">(</span><em>data</em>, <em>dtype=None</em>, <em>device=None</em>, <em>requires_grad=False</em><span class="sig-paren">)</span> → Tensor<a class="headerlink" href="#torch.tensor" title="Permalink to this definition">¶</a></dt>
<dd><p>Constructs a tensor with <code class="xref py py-attr docutils literal"><span class="pre">data</span></code>.</p>
<div class="admonition warning">
<p class="first admonition-title">Warning</p>
<p class="last"><a class="reference internal" href="#torch.tensor" title="torch.tensor"><code class="xref py py-func docutils literal"><span class="pre">torch.tensor()</span></code></a> always copies <code class="xref py py-attr docutils literal"><span class="pre">data</span></code>. If you have a Tensor
<code class="docutils literal"><span class="pre">data</span></code> and want to avoid a copy, use <a class="reference internal" href="tensors.html#torch.Tensor.requires_grad_" title="torch.Tensor.requires_grad_"><code class="xref py py-func docutils literal"><span class="pre">torch.Tensor.requires_grad_()</span></code></a>
or <a class="reference internal" href="autograd.html#torch.Tensor.detach" title="torch.Tensor.detach"><code class="xref py py-func docutils literal"><span class="pre">torch.Tensor.detach()</span></code></a>.
If you have a NumPy <code class="docutils literal"><span class="pre">ndarray</span></code> and want to avoid a copy, use
<a class="reference internal" href="#torch.from_numpy" title="torch.from_numpy"><code class="xref py py-func docutils literal"><span class="pre">torch.from_numpy()</span></code></a>.</p>
</div>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
<li><strong>data</strong> (<em>array_like</em>) – Initial data for the tensor. Can be a list, tuple,
NumPy <code class="docutils literal"><span class="pre">ndarray</span></code>, scalar, and other types.</li>
<li><strong>dtype</strong> (<a class="reference internal" href="tensor_attributes.html#torch.torch.dtype" title="torch.torch.dtype"><code class="xref py py-class docutils literal"><span class="pre">torch.dtype</span></code></a>, optional) – the desired data type of returned tensor.
Default: if None, infers data type from <code class="xref py py-attr docutils literal"><span class="pre">data</span></code>.</li>
<li><strong>device</strong> (<a class="reference internal" href="tensor_attributes.html#torch.torch.device" title="torch.torch.device"><code class="xref py py-class docutils literal"><span class="pre">torch.device</span></code></a>, optional) – the desired device of returned tensor.
Default: if None, uses the current device for the default tensor type
(see <a class="reference internal" href="#torch.set_default_tensor_type" title="torch.set_default_tensor_type"><code class="xref py py-func docutils literal"><span class="pre">torch.set_default_tensor_type()</span></code></a>). <code class="xref py py-attr docutils literal"><span class="pre">device</span></code> will be the CPU
for CPU tensor types and the current CUDA device for CUDA tensor types.</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 autograd should record operations on the
returned tensor. Default: <code class="docutils literal"><span class="pre">False</span></code>.</li>
</ul>
</td>
</tr>
</tbody>
</table>
<p>Example:</p>
<div class="highlight-default"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">torch</span><span class="o">.</span><span class="n">tensor</span><span class="p">([[</span><span class="mf">0.1</span><span class="p">,</span> <span class="mf">1.2</span><span class="p">],</span> <span class="p">[</span><span class="mf">2.2</span><span class="p">,</span> <span class="mf">3.1</span><span class="p">],</span> <span class="p">[</span><span class="mf">4.9</span><span class="p">,</span> <span class="mf">5.2</span><span class="p">]])</span>
<span class="go">tensor([[ 0.1000, 1.2000],</span>
<span class="go"> [ 2.2000, 3.1000],</span>
<span class="go"> [ 4.9000, 5.2000]])</span>
<span class="gp">>>> </span><span class="n">torch</span><span class="o">.</span><span class="n">tensor</span><span class="p">([</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">])</span> <span class="c1"># Type inference on data</span>
<span class="go">tensor([ 0, 1])</span>
<span class="gp">>>> </span><span class="n">torch</span><span class="o">.</span><span class="n">tensor</span><span class="p">([[</span><span class="mf">0.11111</span><span class="p">,</span> <span class="mf">0.222222</span><span class="p">,</span> <span class="mf">0.3333333</span><span class="p">]],</span>
<span class="go"> dtype=torch.float64,</span>
<span class="go"> device=torch.device('cuda:0')) # creates a torch.cuda.DoubleTensor</span>
<span class="go">tensor([[ 0.1111, 0.2222, 0.3333]], dtype=torch.float64, device='cuda:0')</span>
<span class="gp">>>> </span><span class="n">torch</span><span class="o">.</span><span class="n">tensor</span><span class="p">(</span><span class="mf">3.14159</span><span class="p">)</span> <span class="c1"># Create a scalar (zero-dimensional tensor)</span>
<span class="go">tensor(3.1416)</span>
<span class="gp">>>> </span><span class="n">torch</span><span class="o">.</span><span class="n">tensor</span><span class="p">([])</span> <span class="c1"># Create an empty tensor (of size (0,))</span>
<span class="go">tensor([])</span>
</pre></div>
</div>
</dd></dl>
<dl class="function">
<dt id="torch.from_numpy">
<code class="descclassname">torch.</code><code class="descname">from_numpy</code><span class="sig-paren">(</span><em>ndarray</em><span class="sig-paren">)</span> → Tensor<a class="headerlink" href="#torch.from_numpy" title="Permalink to this definition">¶</a></dt>
<dd><p>Creates a <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> from a <a class="reference external" href="https://docs.scipy.org/doc/numpy/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.14)"><code class="xref py py-class docutils literal"><span class="pre">numpy.ndarray</span></code></a>.</p>
<p>The returned tensor and <code class="xref py py-attr docutils literal"><span class="pre">ndarray</span></code> share the same memory. Modifications to
the tensor will be reflected in the <code class="xref py py-attr docutils literal"><span class="pre">ndarray</span></code> and vice versa. The returned
tensor is not resizable.</p>
<p>Example:</p>
<div class="highlight-default"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">a</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">])</span>
<span class="gp">>>> </span><span class="n">t</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">from_numpy</span><span class="p">(</span><span class="n">a</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">t</span>
<span class="go">tensor([ 1, 2, 3])</span>
<span class="gp">>>> </span><span class="n">t</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">=</span> <span class="o">-</span><span class="mi">1</span>
<span class="gp">>>> </span><span class="n">a</span>
<span class="go">array([-1, 2, 3])</span>
</pre></div>
</div>
</dd></dl>
<dl class="function">
<dt id="torch.zeros">
<code class="descclassname">torch.</code><code class="descname">zeros</code><span class="sig-paren">(</span><em>*sizes</em>, <em>out=None</em>, <em>dtype=None</em>, <em>layout=torch.strided</em>, <em>device=None</em>, <em>requires_grad=False</em><span class="sig-paren">)</span> → Tensor<a class="headerlink" href="#torch.zeros" title="Permalink to this definition">¶</a></dt>
<dd><p>Returns a tensor filled with the scalar value <cite>0</cite>, with the shape defined
by the variable argument <code class="xref py py-attr docutils literal"><span class="pre">sizes</span></code>.</p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
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<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">