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<!DOCTYPE html>
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<title>torch — PyTorch 0.1.12_2 documentation</title>
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<p class="caption"><span class="caption-text">Notes</span></p>
<ul>
<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>
<li class="toctree-l3"><a class="reference internal" href="notes/autograd.html#requires-grad"><code class="docutils literal"><span class="pre">requires_grad</span></code></a></li>
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<li class="toctree-l2"><a class="reference internal" href="notes/autograd.html#how-autograd-encodes-the-history">How autograd encodes the history</a></li>
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<li class="toctree-l1"><a class="reference internal" href="notes/cuda.html">CUDA semantics</a><ul>
<li class="toctree-l2"><a class="reference internal" href="notes/cuda.html#best-practices">Best practices</a><ul>
<li class="toctree-l3"><a class="reference internal" href="notes/cuda.html#use-pinned-memory-buffers">Use pinned memory buffers</a></li>
<li class="toctree-l3"><a class="reference internal" href="notes/cuda.html#use-nn-dataparallel-instead-of-multiprocessing">Use nn.DataParallel instead of multiprocessing</a></li>
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<li class="toctree-l1"><a class="reference internal" href="notes/extending.html">Extending PyTorch</a><ul>
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<li class="toctree-l1"><a class="reference internal" href="notes/multiprocessing.html">Multiprocessing best practices</a><ul>
<li class="toctree-l2"><a class="reference internal" href="notes/multiprocessing.html#sharing-cuda-tensors">Sharing CUDA tensors</a></li>
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<li class="toctree-l3"><a class="reference internal" href="notes/multiprocessing.html#avoiding-and-fighting-deadlocks">Avoiding and fighting deadlocks</a></li>
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<li class="toctree-l3"><a class="reference internal" href="notes/multiprocessing.html#asynchronous-multiprocess-training-e-g-hogwild">Asynchronous multiprocess training (e.g. Hogwild)</a><ul>
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<li class="toctree-l1"><a class="reference internal" href="notes/serialization.html">Serialization semantics</a><ul>
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<li class="toctree-l3"><a class="reference internal" href="notes/serialization.html#recommended-approach-for-saving-a-model">Recommended approach for saving a model</a></li>
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<p class="caption"><span class="caption-text">Package Reference</span></p>
<ul class="current">
<li class="toctree-l1 current"><a class="current reference internal" href="#">torch</a><ul>
<li class="toctree-l2"><a class="reference internal" href="#tensors">Tensors</a><ul>
<li class="toctree-l3"><a class="reference internal" href="#creation-ops">Creation Ops</a></li>
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<li class="toctree-l2"><a class="reference internal" href="#random-sampling">Random sampling</a></li>
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</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>
</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>
</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>
</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>
</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#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#nllloss2d"><span class="hidden-section">NLLLoss2d</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#kldivloss"><span class="hidden-section">KLDivLoss</span></a></li>
<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#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>
</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#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#multi-gpu-layers">Multi-GPU layers</a><ul>
<li class="toctree-l3"><a class="reference internal" href="nn.html#dataparallel"><span class="hidden-section">DataParallel</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#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>
</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#id13"><span class="hidden-section">conv1d</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#id14"><span class="hidden-section">conv2d</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#id15"><span class="hidden-section">conv3d</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#conv-transpose1d"><span class="hidden-section">conv_transpose1d</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#conv-transpose2d"><span class="hidden-section">conv_transpose2d</span></a></li>
<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-pool2d"><span class="hidden-section">lp_pool2d</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#adaptive-max-pool1d"><span class="hidden-section">adaptive_max_pool1d</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#adaptive-max-pool2d"><span class="hidden-section">adaptive_max_pool2d</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#adaptive-avg-pool1d"><span class="hidden-section">adaptive_avg_pool1d</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#adaptive-avg-pool2d"><span class="hidden-section">adaptive_avg_pool2d</span></a></li>
</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#id16"><span class="hidden-section">threshold</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#id17"><span class="hidden-section">relu</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#id18"><span class="hidden-section">hardtanh</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#id19"><span class="hidden-section">relu6</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#id20"><span class="hidden-section">elu</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#leaky-relu"><span class="hidden-section">leaky_relu</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#id21"><span class="hidden-section">prelu</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#rrelu"><span class="hidden-section">rrelu</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#id22"><span class="hidden-section">logsigmoid</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#hardshrink"><span class="hidden-section">hardshrink</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#id23"><span class="hidden-section">tanhshrink</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#id24"><span class="hidden-section">softsign</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#id25"><span class="hidden-section">softplus</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#id26"><span class="hidden-section">softmin</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#id27"><span class="hidden-section">softmax</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#id28"><span class="hidden-section">softshrink</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#log-softmax"><span class="hidden-section">log_softmax</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#id29"><span class="hidden-section">tanh</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#id30"><span class="hidden-section">sigmoid</span></a></li>
</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>
</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#id31"><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#id32"><span class="hidden-section">dropout</span></a></li>
</ul>
</li>
<li class="toctree-l2"><a class="reference internal" href="nn.html#id33">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>
</ul>
</li>
<li class="toctree-l2"><a class="reference internal" href="nn.html#id34">Loss functions</a><ul>
<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#kl-div"><span class="hidden-section">kl_div</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#binary-cross-entropy"><span class="hidden-section">binary_cross_entropy</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>
</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>
</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>
</ul>
</li>
</ul>
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<li class="toctree-l2"><a class="reference internal" href="optim.html#algorithms">Algorithms</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>
</ul>
</li>
<li class="toctree-l1"><a class="reference internal" href="multiprocessing.html">torch.multiprocessing</a><ul>
<li class="toctree-l2"><a class="reference internal" href="multiprocessing.html#strategy-management">Strategy management</a></li>
<li class="toctree-l2"><a class="reference internal" href="multiprocessing.html#sharing-cuda-tensors">Sharing CUDA tensors</a></li>
<li class="toctree-l2"><a class="reference internal" href="multiprocessing.html#sharing-strategies">Sharing strategies</a><ul>
<li class="toctree-l3"><a class="reference internal" href="multiprocessing.html#file-descriptor-file-descriptor">File descriptor - <code class="docutils literal"><span class="pre">file_descriptor</span></code></a></li>
<li class="toctree-l3"><a class="reference internal" href="multiprocessing.html#file-system-file-system">File system - <code class="docutils literal"><span class="pre">file_system</span></code></a></li>
</ul>
</li>
</ul>
</li>
<li class="toctree-l1"><a class="reference internal" href="legacy.html">torch.legacy</a></li>
<li class="toctree-l1"><a class="reference internal" href="cuda.html">torch.cuda</a><ul>
<li class="toctree-l2"><a class="reference internal" href="cuda.html#communication-collectives">Communication collectives</a></li>
<li class="toctree-l2"><a class="reference internal" href="cuda.html#streams-and-events">Streams and events</a></li>
</ul>
</li>
<li class="toctree-l1"><a class="reference internal" href="ffi.html">torch.utils.ffi</a></li>
<li class="toctree-l1"><a class="reference internal" href="data.html">torch.utils.data</a></li>
<li class="toctree-l1"><a class="reference internal" href="model_zoo.html">torch.utils.model_zoo</a></li>
</ul>
<p class="caption"><span class="caption-text">torchvision Reference</span></p>
<ul>
<li class="toctree-l1"><a class="reference internal" href="torchvision/torchvision.html">torchvision</a></li>
<li class="toctree-l1"><a class="reference internal" href="torchvision/datasets.html">torchvision.datasets</a><ul>
<li class="toctree-l2"><a class="reference internal" href="torchvision/datasets.html#mnist">MNIST</a></li>
<li class="toctree-l2"><a class="reference internal" href="torchvision/datasets.html#coco">COCO</a><ul>
<li class="toctree-l3"><a class="reference internal" href="torchvision/datasets.html#captions">Captions:</a></li>
<li class="toctree-l3"><a class="reference internal" href="torchvision/datasets.html#detection">Detection:</a></li>
</ul>
</li>
<li class="toctree-l2"><a class="reference internal" href="torchvision/datasets.html#lsun">LSUN</a></li>
<li class="toctree-l2"><a class="reference internal" href="torchvision/datasets.html#imagefolder">ImageFolder</a></li>
<li class="toctree-l2"><a class="reference internal" href="torchvision/datasets.html#imagenet-12">Imagenet-12</a></li>
<li class="toctree-l2"><a class="reference internal" href="torchvision/datasets.html#cifar">CIFAR</a></li>
<li class="toctree-l2"><a class="reference internal" href="torchvision/datasets.html#stl10">STL10</a></li>
</ul>
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<li class="toctree-l1"><a class="reference internal" href="torchvision/models.html">torchvision.models</a></li>
<li class="toctree-l1"><a class="reference internal" href="torchvision/transforms.html">torchvision.transforms</a><ul>
<li class="toctree-l2"><a class="reference internal" href="torchvision/transforms.html#transforms-on-pil-image">Transforms on PIL.Image</a></li>
<li class="toctree-l2"><a class="reference internal" href="torchvision/transforms.html#transforms-on-torch-tensor">Transforms on torch.*Tensor</a></li>
<li class="toctree-l2"><a class="reference internal" href="torchvision/transforms.html#conversion-transforms">Conversion Transforms</a></li>
<li class="toctree-l2"><a class="reference internal" href="torchvision/transforms.html#generic-transforms">Generic Transforms</a></li>
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<li class="toctree-l1"><a class="reference internal" href="torchvision/utils.html">torchvision.utils</a></li>
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<div class="section" id="module-torch">
<span id="torch"></span><h1>torch<a class="headerlink" href="#module-torch" title="Permalink to this headline">¶</a></h1>
<p>The torch package contains data structures for multi-dimensional
tensors and mathematical operations over these are defined.
Additionally, it provides many utilities for efficient serializing of
Tensors and arbitrary types, and other useful utilities.</p>
<p>It has a CUDA counterpart, that enables you to run your tensor computations
on an NVIDIA GPU with compute capability >= 2.0.</p>
<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">
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<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_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></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 <cite>Tensor</cite></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 repr (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 matricies will
ignore this parameter.</li>
<li><strong>profile</strong> – Sane defaults for pretty printing. Can override with any of
the above options. (default, short, full)</li>
</ul>
</td>
</tr>
</tbody>
</table>
</dd></dl>
<div class="section" id="creation-ops">
<h3>Creation Ops<a class="headerlink" href="#creation-ops" title="Permalink to this headline">¶</a></h3>
<dl class="function">
<dt id="torch.eye">
<code class="descclassname">torch.</code><code class="descname">eye</code><span class="sig-paren">(</span><em>n</em>, <em>m=None</em>, <em>out=None</em><span class="sig-paren">)</span><a class="headerlink" href="#torch.eye" title="Permalink to this definition">¶</a></dt>
<dd><p>Returns a 2-D tensor with ones on the diagonal and zeros elsewhere.</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 simple">
<li><strong>n</strong> (<a class="reference external" href="https://docs.python.org/2/library/functions.html#int" title="(in Python v2.7)"><em>int</em></a>) – Number of rows</li>
<li><strong>m</strong> (<a class="reference external" href="https://docs.python.org/2/library/functions.html#int" title="(in Python v2.7)"><em>int</em></a><em>, </em><em>optional</em>) – Number of columns. If None, defaults to <cite>n</cite></li>
<li><strong>out</strong> (<a class="reference internal" href="tensors.html#torch.Tensor" title="torch.Tensor"><em>Tensor</em></a><em>, </em><em>optional</em>) – Output tensor</li>
</ul>
</td>
</tr>
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first">a 2-D tensor with ones on the diagonal and zeros elsewhere</p>
</td>
</tr>
<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last"><a class="reference internal" href="tensors.html#torch.Tensor" title="torch.Tensor">Tensor</a></p>
</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">eye</span><span class="p">(</span><span class="mi">3</span><span class="p">)</span>
<span class="go"> 1 0 0</span>
<span class="go"> 0 1 0</span>
<span class="go"> 0 0 1</span>
<span class="go">[torch.FloatTensor of size 3x3]</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 v1.13)"><code class="xref py py-class docutils literal"><span class="pre">numpy.ndarray</span></code></a>.</p>
<p>The returned tensor and <cite>ndarray</cite> share the same memory. Modifications to the
tensor will be reflected in the <cite>ndarray</cite> 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">torch.LongTensor([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.linspace">
<code class="descclassname">torch.</code><code class="descname">linspace</code><span class="sig-paren">(</span><em>start</em>, <em>end</em>, <em>steps=100</em>, <em>out=None</em><span class="sig-paren">)</span> → Tensor<a class="headerlink" href="#torch.linspace" title="Permalink to this definition">¶</a></dt>
<dd><p>Returns a one-dimensional Tensor of <code class="xref py py-attr docutils literal"><span class="pre">steps</span></code>
equally spaced points between <code class="xref py py-attr docutils literal"><span class="pre">start</span></code> and <code class="xref py py-attr docutils literal"><span class="pre">end</span></code></p>
<p>The output tensor is 1D of size <code class="xref py py-attr docutils literal"><span class="pre">steps</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"><ul class="first last simple">
<li><strong>start</strong> (<a class="reference external" href="https://docs.python.org/2/library/functions.html#float" title="(in Python v2.7)"><em>float</em></a>) – The starting value for the set of points</li>
<li><strong>end</strong> (<a class="reference external" href="https://docs.python.org/2/library/functions.html#float" title="(in Python v2.7)"><em>float</em></a>) – The ending value for the set of points</li>
<li><strong>steps</strong> (<a class="reference external" href="https://docs.python.org/2/library/functions.html#int" title="(in Python v2.7)"><em>int</em></a>) – Number of points to sample between <code class="xref py py-attr docutils literal"><span class="pre">start</span></code> and <code class="xref py py-attr docutils literal"><span class="pre">end</span></code></li>
<li><strong>out</strong> (<a class="reference internal" href="tensors.html#torch.Tensor" title="torch.Tensor"><em>Tensor</em></a><em>, </em><em>optional</em>) – The result <cite>Tensor</cite></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">linspace</span><span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="mi">10</span><span class="p">,</span> <span class="n">steps</span><span class="o">=</span><span class="mi">5</span><span class="p">)</span>
<span class="go"> 3.0000</span>
<span class="go"> 4.7500</span>
<span class="go"> 6.5000</span>
<span class="go"> 8.2500</span>
<span class="go"> 10.0000</span>
<span class="go">[torch.FloatTensor of size 5]</span>
<span class="gp">>>> </span><span class="n">torch</span><span class="o">.</span><span class="n">linspace</span><span class="p">(</span><span class="o">-</span><span class="mi">10</span><span class="p">,</span> <span class="mi">10</span><span class="p">,</span> <span class="n">steps</span><span class="o">=</span><span class="mi">5</span><span class="p">)</span>
<span class="go">-10</span>
<span class="go"> -5</span>
<span class="go"> 0</span>
<span class="go"> 5</span>
<span class="go"> 10</span>
<span class="go">[torch.FloatTensor of size 5]</span>
<span class="gp">>>> </span><span class="n">torch</span><span class="o">.</span><span class="n">linspace</span><span class="p">(</span><span class="n">start</span><span class="o">=-</span><span class="mi">10</span><span class="p">,</span> <span class="n">end</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span> <span class="n">steps</span><span class="o">=</span><span class="mi">5</span><span class="p">)</span>
<span class="go">-10</span>
<span class="go"> -5</span>
<span class="go"> 0</span>
<span class="go"> 5</span>
<span class="go"> 10</span>
<span class="go">[torch.FloatTensor of size 5]</span>
</pre></div>
</div>
</dd></dl>
<dl class="function">
<dt id="torch.logspace">
<code class="descclassname">torch.</code><code class="descname">logspace</code><span class="sig-paren">(</span><em>start</em>, <em>end</em>, <em>steps=100</em>, <em>out=None</em><span class="sig-paren">)</span> → Tensor<a class="headerlink" href="#torch.logspace" title="Permalink to this definition">¶</a></dt>
<dd><p>Returns a one-dimensional Tensor of <code class="xref py py-attr docutils literal"><span class="pre">steps</span></code> points
logarithmically spaced between <span class="math">\(10^{start}\)</span> and <span class="math">\(10^{end}\)</span></p>
<p>The output is a 1D tensor of size <code class="xref py py-attr docutils literal"><span class="pre">steps</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"><ul class="first last simple">
<li><strong>start</strong> (<a class="reference external" href="https://docs.python.org/2/library/functions.html#float" title="(in Python v2.7)"><em>float</em></a>) – The starting value for the set of points</li>
<li><strong>end</strong> (<a class="reference external" href="https://docs.python.org/2/library/functions.html#float" title="(in Python v2.7)"><em>float</em></a>) – The ending value for the set of points</li>
<li><strong>steps</strong> (<a class="reference external" href="https://docs.python.org/2/library/functions.html#int" title="(in Python v2.7)"><em>int</em></a>) – Number of points to sample between <code class="xref py py-attr docutils literal"><span class="pre">start</span></code> and <code class="xref py py-attr docutils literal"><span class="pre">end</span></code></li>
<li><strong>out</strong> (<a class="reference internal" href="tensors.html#torch.Tensor" title="torch.Tensor"><em>Tensor</em></a><em>, </em><em>optional</em>) – The result <cite>Tensor</cite></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">logspace</span><span class="p">(</span><span class="n">start</span><span class="o">=-</span><span class="mi">10</span><span class="p">,</span> <span class="n">end</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span> <span class="n">steps</span><span class="o">=</span><span class="mi">5</span><span class="p">)</span>
<span class="go"> 1.0000e-10</span>
<span class="go"> 1.0000e-05</span>
<span class="go"> 1.0000e+00</span>
<span class="go"> 1.0000e+05</span>
<span class="go"> 1.0000e+10</span>
<span class="go">[torch.FloatTensor of size 5]</span>
<span class="gp">>>> </span><span class="n">torch</span><span class="o">.</span><span class="n">logspace</span><span class="p">(</span><span class="n">start</span><span class="o">=</span><span class="mf">0.1</span><span class="p">,</span> <span class="n">end</span><span class="o">=</span><span class="mf">1.0</span><span class="p">,</span> <span class="n">steps</span><span class="o">=</span><span class="mi">5</span><span class="p">)</span>
<span class="go"> 1.2589</span>
<span class="go"> 2.1135</span>
<span class="go"> 3.5481</span>
<span class="go"> 5.9566</span>
<span class="go"> 10.0000</span>
<span class="go">[torch.FloatTensor of size 5]</span>
</pre></div>
</div>
</dd></dl>
<dl class="function">
<dt id="torch.ones">
<code class="descclassname">torch.</code><code class="descname">ones</code><span class="sig-paren">(</span><em>*sizes</em>, <em>out=None</em><span class="sig-paren">)</span> → Tensor<a class="headerlink" href="#torch.ones" title="Permalink to this definition">¶</a></dt>
<dd><p>Returns a Tensor filled with the scalar value <cite>1</cite>, with the shape defined
by the varargs <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" />
<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>sizes</strong> (<em>int...</em>) – a set of ints defining the shape of the output Tensor.</li>
<li><strong>out</strong> (<a class="reference internal" href="tensors.html#torch.Tensor" title="torch.Tensor"><em>Tensor</em></a><em>, </em><em>optional</em>) – the result Tensor</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">ones</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="go"> 1 1 1</span>
<span class="go"> 1 1 1</span>
<span class="go">[torch.FloatTensor of size 2x3]</span>
<span class="gp">>>> </span><span class="n">torch</span><span class="o">.</span><span class="n">ones</span><span class="p">(</span><span class="mi">5</span><span class="p">)</span>
<span class="go"> 1</span>
<span class="go"> 1</span>
<span class="go"> 1</span>
<span class="go"> 1</span>
<span class="go"> 1</span>
<span class="go">[torch.FloatTensor of size 5]</span>
</pre></div>
</div>
</dd></dl>
<dl class="function">
<dt id="torch.rand">
<code class="descclassname">torch.</code><code class="descname">rand</code><span class="sig-paren">(</span><em>*sizes</em>, <em>out=None</em><span class="sig-paren">)</span> → Tensor<a class="headerlink" href="#torch.rand" title="Permalink to this definition">¶</a></dt>
<dd><p>Returns a Tensor filled with random numbers from a uniform distribution
on the interval <span class="math">\([0, 1)\)</span></p>
<p>The shape of the Tensor is defined by the varargs <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" />
<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>sizes</strong> (<em>int...</em>) – a set of ints defining the shape of the output Tensor.</li>
<li><strong>out</strong> (<a class="reference internal" href="tensors.html#torch.Tensor" title="torch.Tensor"><em>Tensor</em></a><em>, </em><em>optional</em>) – the result Tensor</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">rand</span><span class="p">(</span><span class="mi">4</span><span class="p">)</span>
<span class="go"> 0.9193</span>
<span class="go"> 0.3347</span>
<span class="go"> 0.3232</span>
<span class="go"> 0.7715</span>
<span class="go">[torch.FloatTensor of size 4]</span>
<span class="gp">>>> </span><span class="n">torch</span><span class="o">.</span><span class="n">rand</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="go"> 0.5010 0.5140 0.0719</span>
<span class="go"> 0.1435 0.5636 0.0538</span>
<span class="go">[torch.FloatTensor of size 2x3]</span>
</pre></div>
</div>
</dd></dl>
<dl class="function">
<dt id="torch.randn">
<code class="descclassname">torch.</code><code class="descname">randn</code><span class="sig-paren">(</span><em>*sizes</em>, <em>out=None</em><span class="sig-paren">)</span> → Tensor<a class="headerlink" href="#torch.randn" title="Permalink to this definition">¶</a></dt>
<dd><p>Returns a Tensor filled with random numbers from a normal distribution
with zero mean and variance of one.</p>
<p>The shape of the Tensor is defined by the varargs <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" />
<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>sizes</strong> (<em>int...</em>) – a set of ints defining the shape of the output Tensor.</li>
<li><strong>out</strong> (<a class="reference internal" href="tensors.html#torch.Tensor" title="torch.Tensor"><em>Tensor</em></a><em>, </em><em>optional</em>) – the result Tensor</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">randn</span><span class="p">(</span><span class="mi">4</span><span class="p">)</span>
<span class="go">-0.1145</span>
<span class="go"> 0.0094</span>
<span class="go">-1.1717</span>
<span class="go"> 0.9846</span>
<span class="go">[torch.FloatTensor of size 4]</span>
<span class="gp">>>> </span><span class="n">torch</span><span class="o">.</span><span class="n">randn</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="go"> 1.4339 0.3351 -1.0999</span>
<span class="go"> 1.5458 -0.9643 -0.3558</span>
<span class="go">[torch.FloatTensor of size 2x3]</span>
</pre></div>
</div>
</dd></dl>
<dl class="function">
<dt id="torch.randperm">
<code class="descclassname">torch.</code><code class="descname">randperm</code><span class="sig-paren">(</span><em>n</em>, <em>out=None</em><span class="sig-paren">)</span> → LongTensor<a class="headerlink" href="#torch.randperm" title="Permalink to this definition">¶</a></dt>
<dd><p>Returns a random permutation of integers from <code class="docutils literal"><span class="pre">0</span></code> to <code class="docutils literal"><span class="pre">n</span> <span class="pre">-</span> <span class="pre">1</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>n</strong> (<a class="reference external" href="https://docs.python.org/2/library/functions.html#int" title="(in Python v2.7)"><em>int</em></a>) – the upper bound (exclusive)</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">randperm</span><span class="p">(</span><span class="mi">4</span><span class="p">)</span>
<span class="go"> 2</span>
<span class="go"> 1</span>
<span class="go"> 3</span>
<span class="go"> 0</span>
<span class="go">[torch.LongTensor of size 4]</span>
</pre></div>
</div>
</dd></dl>
<dl class="function">
<dt id="torch.arange">
<code class="descclassname">torch.</code><code class="descname">arange</code><span class="sig-paren">(</span><em>start</em>, <em>end</em>, <em>step=1</em>, <em>out=None</em><span class="sig-paren">)</span> → Tensor<a class="headerlink" href="#torch.arange" title="Permalink to this definition">¶</a></dt>
<dd><p>Teturns a 1D Tensor of size <span class="math">\(floor((end - start) / step)\)</span> with values
from the interval <code class="docutils literal"><span class="pre">[start,</span> <span class="pre">end)</span></code> taken with step <code class="xref py py-attr docutils literal"><span class="pre">step</span></code> starting from <cite>start</cite>.</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>start</strong> (<a class="reference external" href="https://docs.python.org/2/library/functions.html#float" title="(in Python v2.7)"><em>float</em></a>) – The starting value for the set of points</li>
<li><strong>end</strong> (<a class="reference external" href="https://docs.python.org/2/library/functions.html#float" title="(in Python v2.7)"><em>float</em></a>) – The ending value for the set of points</li>
<li><strong>step</strong> (<a class="reference external" href="https://docs.python.org/2/library/functions.html#float" title="(in Python v2.7)"><em>float</em></a>) – The gap between each pair of adjacent points</li>
<li><strong>out</strong> (<a class="reference internal" href="tensors.html#torch.Tensor" title="torch.Tensor"><em>Tensor</em></a><em>, </em><em>optional</em>) – The result <cite>Tensor</cite></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">arange</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">4</span><span class="p">)</span>
<span class="go"> 1</span>
<span class="go"> 2</span>
<span class="go"> 3</span>
<span class="go">[torch.FloatTensor of size 3]</span>
<span class="gp">>>> </span><span class="n">torch</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mf">2.5</span><span class="p">,</span> <span class="mf">0.5</span><span class="p">)</span>
<span class="go"> 1.0000</span>
<span class="go"> 1.5000</span>
<span class="go"> 2.0000</span>
<span class="go">[torch.FloatTensor of size 3]</span>
</pre></div>
</div>
</dd></dl>
<dl class="function">
<dt id="torch.range">
<code class="descclassname">torch.</code><code class="descname">range</code><span class="sig-paren">(</span><em>start</em>, <em>end</em>, <em>step=1</em>, <em>out=None</em><span class="sig-paren">)</span> → Tensor<a class="headerlink" href="#torch.range" title="Permalink to this definition">¶</a></dt>
<dd><p>returns a 1D Tensor of size <span class="math">\(floor((end - start) / step) + 1\)</span> with values
from <code class="xref py py-attr docutils literal"><span class="pre">start</span></code> to <code class="xref py py-attr docutils literal"><span class="pre">end</span></code> with step <code class="xref py py-attr docutils literal"><span class="pre">step</span></code>. Step is the gap between two values in the tensor.
<span class="math">\(x_{i+1} = x_i + step\)</span></p>
<div class="admonition warning">
<p class="first admonition-title">Warning</p>
<p class="last">This function is deprecated in favor of <a class="reference internal" href="#torch.arange" title="torch.arange"><code class="xref py py-func docutils literal"><span class="pre">torch.arange()</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>start</strong> (<a class="reference external" href="https://docs.python.org/2/library/functions.html#float" title="(in Python v2.7)"><em>float</em></a>) – The starting value for the set of points</li>
<li><strong>end</strong> (<a class="reference external" href="https://docs.python.org/2/library/functions.html#float" title="(in Python v2.7)"><em>float</em></a>) – The ending value for the set of points</li>
<li><strong>step</strong> (<a class="reference external" href="https://docs.python.org/2/library/functions.html#float" title="(in Python v2.7)"><em>float</em></a>) – The gap between each pair of adjacent points</li>
<li><strong>out</strong> (<a class="reference internal" href="tensors.html#torch.Tensor" title="torch.Tensor"><em>Tensor</em></a><em>, </em><em>optional</em>) – The result <cite>Tensor</cite></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">range</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">4</span><span class="p">)</span>
<span class="go"> 1</span>
<span class="go"> 2</span>
<span class="go"> 3</span>
<span class="go"> 4</span>
<span class="go">[torch.FloatTensor of size 4]</span>
<span class="gp">>>> </span><span class="n">torch</span><span class="o">.</span><span class="n">range</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="mf">0.5</span><span class="p">)</span>
<span class="go"> 1.0000</span>
<span class="go"> 1.5000</span>
<span class="go"> 2.0000</span>
<span class="go"> 2.5000</span>
<span class="go"> 3.0000</span>
<span class="go"> 3.5000</span>
<span class="go"> 4.0000</span>
<span class="go">[torch.FloatTensor of size 7]</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><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 varargs <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" />
<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>sizes</strong> (<em>int...</em>) – a set of ints defining the shape of the output Tensor.</li>
<li><strong>out</strong> (<a class="reference internal" href="tensors.html#torch.Tensor" title="torch.Tensor"><em>Tensor</em></a><em>, </em><em>optional</em>) – the result Tensor</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">zeros</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="go"> 0 0 0</span>
<span class="go"> 0 0 0</span>
<span class="go">[torch.FloatTensor of size 2x3]</span>
<span class="gp">>>> </span><span class="n">torch</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="mi">5</span><span class="p">)</span>
<span class="go"> 0</span>