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<!DOCTYPE html>
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<li class="toctree-l1"><a class="reference internal" href="notes/autograd.html">Autograd mechanics</a><ul>
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<li class="toctree-l3"><a class="reference internal" href="nn.html#dropout"><span class="hidden-section">Dropout</span></a></li>
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<li class="toctree-l3"><a class="reference internal" href="nn.html#dropout3d"><span class="hidden-section">Dropout3d</span></a></li>
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<li class="toctree-l3"><a class="reference internal" href="nn.html#pairwisedistance"><span class="hidden-section">PairwiseDistance</span></a></li>
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<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>
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<li class="toctree-l2"><a class="reference internal" href="nn.html#vision-layers">Vision layers</a><ul>
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<li class="toctree-l3"><a class="reference internal" href="nn.html#upsamplingnearest2d"><span class="hidden-section">UpsamplingNearest2d</span></a></li>
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<li class="toctree-l2"><a class="reference internal" href="nn.html#multi-gpu-layers">Multi-GPU layers</a><ul>
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<li class="toctree-l3"><a class="reference internal" href="nn.html#pad-packed-sequence"><span class="hidden-section">pad_packed_sequence</span></a></li>
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<li class="toctree-l3"><a class="reference internal" href="nn.html#avg-pool1d"><span class="hidden-section">avg_pool1d</span></a></li>
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<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>
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<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>
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<li class="toctree-l3"><a class="reference internal" href="nn.html#id17"><span class="hidden-section">relu</span></a></li>
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<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>
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<li class="toctree-l3"><a class="reference internal" href="nn.html#id32"><span class="hidden-section">dropout</span></a></li>
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<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>
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<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>
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<li class="toctree-l3"><a class="reference internal" href="nn.html#pixel-shuffle"><span class="hidden-section">pixel_shuffle</span></a></li>
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<div class="section" id="module-torch.utils.data">
<span id="torch-utils-data"></span><h1>torch.utils.data<a class="headerlink" href="#module-torch.utils.data" title="Permalink to this headline">¶</a></h1>
<dl class="class">
<dt id="torch.utils.data.Dataset">
<em class="property">class </em><code class="descclassname">torch.utils.data.</code><code class="descname">Dataset</code><a class="reference internal" href="_modules/torch/utils/data/dataset.html#Dataset"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torch.utils.data.Dataset" title="Permalink to this definition">¶</a></dt>
<dd><p>An abstract class representing a Dataset.</p>
<p>All other datasets should subclass it. All subclasses should override
<code class="docutils literal"><span class="pre">__len__</span></code>, that provides the size of the dataset, and <code class="docutils literal"><span class="pre">__getitem__</span></code>,
supporting integer indexing in range from 0 to len(self) exclusive.</p>
</dd></dl>
<dl class="class">
<dt id="torch.utils.data.TensorDataset">
<em class="property">class </em><code class="descclassname">torch.utils.data.</code><code class="descname">TensorDataset</code><span class="sig-paren">(</span><em>data_tensor</em>, <em>target_tensor</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/utils/data/dataset.html#TensorDataset"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torch.utils.data.TensorDataset" title="Permalink to this definition">¶</a></dt>
<dd><p>Dataset wrapping data and target tensors.</p>
<p>Each sample will be retrieved by indexing both tensors along the first
dimension.</p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
<li><strong>data_tensor</strong> (<a class="reference internal" href="tensors.html#torch.Tensor" title="torch.Tensor"><em>Tensor</em></a>) – contains sample data.</li>
<li><strong>target_tensor</strong> (<a class="reference internal" href="tensors.html#torch.Tensor" title="torch.Tensor"><em>Tensor</em></a>) – contains sample targets (labels).</li>
</ul>
</td>
</tr>
</tbody>
</table>
</dd></dl>
<dl class="class">
<dt id="torch.utils.data.DataLoader">
<em class="property">class </em><code class="descclassname">torch.utils.data.</code><code class="descname">DataLoader</code><span class="sig-paren">(</span><em>dataset</em>, <em>batch_size=1</em>, <em>shuffle=False</em>, <em>sampler=None</em>, <em>num_workers=0</em>, <em>collate_fn=<function default_collate></em>, <em>pin_memory=False</em>, <em>drop_last=False</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/utils/data/dataloader.html#DataLoader"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torch.utils.data.DataLoader" title="Permalink to this definition">¶</a></dt>
<dd><p>Data loader. Combines a dataset and a sampler, and provides
single- or multi-process iterators over the dataset.</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>dataset</strong> (<a class="reference internal" href="#torch.utils.data.Dataset" title="torch.utils.data.Dataset"><em>Dataset</em></a>) – dataset from which to load the data.</li>
<li><strong>batch_size</strong> (<a class="reference external" href="https://docs.python.org/2/library/functions.html#int" title="(in Python v2.7)"><em>int</em></a><em>, </em><em>optional</em>) – how many samples per batch to load
(default: 1).</li>
<li><strong>shuffle</strong> (<a class="reference external" href="https://docs.python.org/2/library/functions.html#bool" title="(in Python v2.7)"><em>bool</em></a><em>, </em><em>optional</em>) – set to <code class="docutils literal"><span class="pre">True</span></code> to have the data reshuffled
at every epoch (default: False).</li>
<li><strong>sampler</strong> (<a class="reference internal" href="#torch.utils.data.sampler.Sampler" title="torch.utils.data.sampler.Sampler"><em>Sampler</em></a><em>, </em><em>optional</em>) – defines the strategy to draw samples from
the dataset. If specified, the <code class="docutils literal"><span class="pre">shuffle</span></code> argument is ignored.</li>
<li><strong>num_workers</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>) – how many subprocesses to use for data
loading. 0 means that the data will be loaded in the main process
(default: 0)</li>
<li><strong>collate_fn</strong> (<a class="reference external" href="https://docs.python.org/2/library/functions.html#callable" title="(in Python v2.7)"><em>callable</em></a><em>, </em><em>optional</em>) – </li>
<li><strong>pin_memory</strong> (<a class="reference external" href="https://docs.python.org/2/library/functions.html#bool" title="(in Python v2.7)"><em>bool</em></a><em>, </em><em>optional</em>) – </li>
<li><strong>drop_last</strong> (<a class="reference external" href="https://docs.python.org/2/library/functions.html#bool" title="(in Python v2.7)"><em>bool</em></a><em>, </em><em>optional</em>) – set to <code class="docutils literal"><span class="pre">True</span></code> to drop the last incomplete batch,
if the dataset size is not divisible by the batch size. If False and
the size of dataset is not divisible by the batch size, then the last batch
will be smaller. (default: False)</li>
</ul>
</td>
</tr>
</tbody>
</table>
</dd></dl>
<dl class="class">
<dt id="torch.utils.data.sampler.Sampler">
<em class="property">class </em><code class="descclassname">torch.utils.data.sampler.</code><code class="descname">Sampler</code><span class="sig-paren">(</span><em>data_source</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/utils/data/sampler.html#Sampler"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torch.utils.data.sampler.Sampler" title="Permalink to this definition">¶</a></dt>
<dd><p>Base class for all Samplers.</p>
<p>Every Sampler subclass has to provide an __iter__ method, providing a way
to iterate over indices of dataset elements, and a __len__ method that
returns the length of the returned iterators.</p>
</dd></dl>
<dl class="class">
<dt id="torch.utils.data.sampler.SequentialSampler">
<em class="property">class </em><code class="descclassname">torch.utils.data.sampler.</code><code class="descname">SequentialSampler</code><span class="sig-paren">(</span><em>data_source</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/utils/data/sampler.html#SequentialSampler"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torch.utils.data.sampler.SequentialSampler" title="Permalink to this definition">¶</a></dt>
<dd><p>Samples elements sequentially, always in the same order.</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>data_source</strong> (<a class="reference internal" href="#torch.utils.data.Dataset" title="torch.utils.data.Dataset"><em>Dataset</em></a>) – dataset to sample from</td>
</tr>
</tbody>
</table>
</dd></dl>
<dl class="class">
<dt id="torch.utils.data.sampler.RandomSampler">
<em class="property">class </em><code class="descclassname">torch.utils.data.sampler.</code><code class="descname">RandomSampler</code><span class="sig-paren">(</span><em>data_source</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/utils/data/sampler.html#RandomSampler"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torch.utils.data.sampler.RandomSampler" title="Permalink to this definition">¶</a></dt>
<dd><p>Samples elements randomly, without replacement.</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>data_source</strong> (<a class="reference internal" href="#torch.utils.data.Dataset" title="torch.utils.data.Dataset"><em>Dataset</em></a>) – dataset to sample from</td>
</tr>
</tbody>
</table>
</dd></dl>
<dl class="class">
<dt id="torch.utils.data.sampler.SubsetRandomSampler">
<em class="property">class </em><code class="descclassname">torch.utils.data.sampler.</code><code class="descname">SubsetRandomSampler</code><span class="sig-paren">(</span><em>indices</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/utils/data/sampler.html#SubsetRandomSampler"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torch.utils.data.sampler.SubsetRandomSampler" title="Permalink to this definition">¶</a></dt>
<dd><p>Samples elements randomly from a given list of indices, without replacement.</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>indices</strong> (<em>list</em>) – a list of indices</td>
</tr>
</tbody>
</table>
</dd></dl>
<dl class="class">
<dt id="torch.utils.data.sampler.WeightedRandomSampler">
<em class="property">class </em><code class="descclassname">torch.utils.data.sampler.</code><code class="descname">WeightedRandomSampler</code><span class="sig-paren">(</span><em>weights</em>, <em>num_samples</em>, <em>replacement=True</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/utils/data/sampler.html#WeightedRandomSampler"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torch.utils.data.sampler.WeightedRandomSampler" title="Permalink to this definition">¶</a></dt>
<dd><p>Samples elements from [0,..,len(weights)-1] with given probabilities (weights).
:param weights: a list of weights, not necessary summing up to one
:type weights: list
:param num_samples: number of samples to draw
:type num_samples: int</p>
</dd></dl>
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