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<!DOCTYPE html>
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<title>torch.optim — PyTorch 0.1.12_2 documentation</title>
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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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<p class="caption"><span class="caption-text">Package Reference</span></p>
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<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>
</ul>
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<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>
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<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>
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<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>
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<li class="toctree-l2"><a class="reference internal" href="nn.html#non-linear-activation-functions">Non-linear activation functions</a><ul>
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<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>
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<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>
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<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>
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<li class="toctree-l3"><a class="reference internal" href="nn.html#id31"><span class="hidden-section">linear</span></a></li>
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<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>
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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>
<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>
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<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>
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<li class="toctree-l1 current"><a class="current reference internal" href="#">torch.optim</a><ul>
<li class="toctree-l2"><a class="reference internal" href="#how-to-use-an-optimizer">How to use an optimizer</a><ul>
<li class="toctree-l3"><a class="reference internal" href="#constructing-it">Constructing it</a></li>
<li class="toctree-l3"><a class="reference internal" href="#per-parameter-options">Per-parameter options</a></li>
<li class="toctree-l3"><a class="reference internal" href="#taking-an-optimization-step">Taking an optimization step</a><ul>
<li class="toctree-l4"><a class="reference internal" href="#optimizer-step"><code class="docutils literal"><span class="pre">optimizer.step()</span></code></a></li>
<li class="toctree-l4"><a class="reference internal" href="#optimizer-step-closure"><code class="docutils literal"><span class="pre">optimizer.step(closure)</span></code></a></li>
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<p class="caption"><span class="caption-text">torchvision Reference</span></p>
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<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>
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<div class="section" id="module-torch.optim">
<span id="torch-optim"></span><h1>torch.optim<a class="headerlink" href="#module-torch.optim" title="Permalink to this headline">¶</a></h1>
<p><a class="reference internal" href="#module-torch.optim" title="torch.optim"><code class="xref py py-mod docutils literal"><span class="pre">torch.optim</span></code></a> is a package implementing various optimization algorithms.
Most commonly used methods are already supported, and the interface is general
enough, so that more sophisticated ones can be also easily integrated in the
future.</p>
<div class="section" id="how-to-use-an-optimizer">
<h2>How to use an optimizer<a class="headerlink" href="#how-to-use-an-optimizer" title="Permalink to this headline">¶</a></h2>
<p>To use <a class="reference internal" href="#module-torch.optim" title="torch.optim"><code class="xref py py-mod docutils literal"><span class="pre">torch.optim</span></code></a> you have to construct an optimizer object, that will hold
the current state and will update the parameters based on the computed gradients.</p>
<div class="section" id="constructing-it">
<h3>Constructing it<a class="headerlink" href="#constructing-it" title="Permalink to this headline">¶</a></h3>
<p>To construct an <a class="reference internal" href="#torch.optim.Optimizer" title="torch.optim.Optimizer"><code class="xref py py-class docutils literal"><span class="pre">Optimizer</span></code></a> you have to give it an iterable containing the
parameters (all should be <a class="reference internal" href="autograd.html#torch.autograd.Variable" title="torch.autograd.Variable"><code class="xref py py-class docutils literal"><span class="pre">Variable</span></code></a> s) to optimize. Then,
you can specify optimizer-specific options such as the learning rate, weight decay, etc.</p>
<p>Example:</p>
<div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">optimizer</span> <span class="o">=</span> <span class="n">optim</span><span class="o">.</span><span class="n">SGD</span><span class="p">(</span><span class="n">model</span><span class="o">.</span><span class="n">parameters</span><span class="p">(),</span> <span class="n">lr</span> <span class="o">=</span> <span class="mf">0.01</span><span class="p">,</span> <span class="n">momentum</span><span class="o">=</span><span class="mf">0.9</span><span class="p">)</span>
<span class="n">optimizer</span> <span class="o">=</span> <span class="n">optim</span><span class="o">.</span><span class="n">Adam</span><span class="p">([</span><span class="n">var1</span><span class="p">,</span> <span class="n">var2</span><span class="p">],</span> <span class="n">lr</span> <span class="o">=</span> <span class="mf">0.0001</span><span class="p">)</span>
</pre></div>
</div>
</div>
<div class="section" id="per-parameter-options">
<h3>Per-parameter options<a class="headerlink" href="#per-parameter-options" title="Permalink to this headline">¶</a></h3>
<p><a class="reference internal" href="#torch.optim.Optimizer" title="torch.optim.Optimizer"><code class="xref py py-class docutils literal"><span class="pre">Optimizer</span></code></a> s also support specifying per-parameter options. To do this, instead
of passing an iterable of <a class="reference internal" href="autograd.html#torch.autograd.Variable" title="torch.autograd.Variable"><code class="xref py py-class docutils literal"><span class="pre">Variable</span></code></a> s, pass in an iterable of
<a class="reference external" href="https://docs.python.org/2/library/stdtypes.html#dict" title="(in Python v2.7)"><code class="xref py py-class docutils literal"><span class="pre">dict</span></code></a> s. Each of them will define a separate parameter group, and should contain
a <code class="docutils literal"><span class="pre">params</span></code> key, containing a list of parameters belonging to it. Other keys
should match the keyword arguments accepted by the optimizers, and will be used
as optimization options for this group.</p>
<div class="admonition note">
<p class="first admonition-title">Note</p>
<p class="last">You can still pass options as keyword arguments. They will be used as
defaults, in the groups that didn’t override them. This is useful when you
only want to vary a single option, while keeping all others consistent
between parameter groups.</p>
</div>
<p>For example, this is very useful when one wants to specify per-layer learning rates:</p>
<div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">optim</span><span class="o">.</span><span class="n">SGD</span><span class="p">([</span>
<span class="p">{</span><span class="s1">'params'</span><span class="p">:</span> <span class="n">model</span><span class="o">.</span><span class="n">base</span><span class="o">.</span><span class="n">parameters</span><span class="p">()},</span>
<span class="p">{</span><span class="s1">'params'</span><span class="p">:</span> <span class="n">model</span><span class="o">.</span><span class="n">classifier</span><span class="o">.</span><span class="n">parameters</span><span class="p">(),</span> <span class="s1">'lr'</span><span class="p">:</span> <span class="mf">1e-3</span><span class="p">}</span>
<span class="p">],</span> <span class="n">lr</span><span class="o">=</span><span class="mf">1e-2</span><span class="p">,</span> <span class="n">momentum</span><span class="o">=</span><span class="mf">0.9</span><span class="p">)</span>
</pre></div>
</div>
<p>This means that <code class="docutils literal"><span class="pre">model.base</span></code>’s parameters will use the default learning rate of <code class="docutils literal"><span class="pre">1e-2</span></code>,
<code class="docutils literal"><span class="pre">model.classifier</span></code>’s parameters will use a learning rate of <code class="docutils literal"><span class="pre">1e-3</span></code>, and a momentum of
<code class="docutils literal"><span class="pre">0.9</span></code> will be used for all parameters</p>
</div>
<div class="section" id="taking-an-optimization-step">
<h3>Taking an optimization step<a class="headerlink" href="#taking-an-optimization-step" title="Permalink to this headline">¶</a></h3>
<p>All optimizers implement a <a class="reference internal" href="#torch.optim.Optimizer.step" title="torch.optim.Optimizer.step"><code class="xref py py-func docutils literal"><span class="pre">step()</span></code></a> method, that updates the
parameters. It can be used in two ways:</p>
<div class="section" id="optimizer-step">
<h4><code class="docutils literal"><span class="pre">optimizer.step()</span></code><a class="headerlink" href="#optimizer-step" title="Permalink to this headline">¶</a></h4>
<p>This is a simplified version supported by most optimizers. The function can be
called once the gradients are computed using e.g.
<a class="reference internal" href="autograd.html#torch.autograd.Variable.backward" title="torch.autograd.Variable.backward"><code class="xref py py-func docutils literal"><span class="pre">backward()</span></code></a>.</p>
<p>Example:</p>
<div class="highlight-default"><div class="highlight"><pre><span></span><span class="k">for</span> <span class="nb">input</span><span class="p">,</span> <span class="n">target</span> <span class="ow">in</span> <span class="n">dataset</span><span class="p">:</span>
<span class="n">optimizer</span><span class="o">.</span><span class="n">zero_grad</span><span class="p">()</span>
<span class="n">output</span> <span class="o">=</span> <span class="n">model</span><span class="p">(</span><span class="nb">input</span><span class="p">)</span>
<span class="n">loss</span> <span class="o">=</span> <span class="n">loss_fn</span><span class="p">(</span><span class="n">output</span><span class="p">,</span> <span class="n">target</span><span class="p">)</span>
<span class="n">loss</span><span class="o">.</span><span class="n">backward</span><span class="p">()</span>
<span class="n">optimizer</span><span class="o">.</span><span class="n">step</span><span class="p">()</span>
</pre></div>
</div>
</div>
<div class="section" id="optimizer-step-closure">
<h4><code class="docutils literal"><span class="pre">optimizer.step(closure)</span></code><a class="headerlink" href="#optimizer-step-closure" title="Permalink to this headline">¶</a></h4>
<p>Some optimization algorithms such as Conjugate Gradient and LBFGS need to
reevaluate the function multiple times, so you have to pass in a closure that
allows them to recompute your model. The closure should clear the gradients,
compute the loss, and return it.</p>
<p>Example:</p>
<div class="highlight-default"><div class="highlight"><pre><span></span><span class="k">for</span> <span class="nb">input</span><span class="p">,</span> <span class="n">target</span> <span class="ow">in</span> <span class="n">dataset</span><span class="p">:</span>
<span class="k">def</span> <span class="nf">closure</span><span class="p">():</span>
<span class="n">optimizer</span><span class="o">.</span><span class="n">zero_grad</span><span class="p">()</span>
<span class="n">output</span> <span class="o">=</span> <span class="n">model</span><span class="p">(</span><span class="nb">input</span><span class="p">)</span>
<span class="n">loss</span> <span class="o">=</span> <span class="n">loss_fn</span><span class="p">(</span><span class="n">output</span><span class="p">,</span> <span class="n">target</span><span class="p">)</span>
<span class="n">loss</span><span class="o">.</span><span class="n">backward</span><span class="p">()</span>
<span class="k">return</span> <span class="n">loss</span>
<span class="n">optimizer</span><span class="o">.</span><span class="n">step</span><span class="p">(</span><span class="n">closure</span><span class="p">)</span>
</pre></div>
</div>
</div>
</div>
</div>
<div class="section" id="algorithms">
<h2>Algorithms<a class="headerlink" href="#algorithms" title="Permalink to this headline">¶</a></h2>
<dl class="class">
<dt id="torch.optim.Optimizer">
<em class="property">class </em><code class="descclassname">torch.optim.</code><code class="descname">Optimizer</code><span class="sig-paren">(</span><em>params</em>, <em>defaults</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/optim/optimizer.html#Optimizer"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torch.optim.Optimizer" title="Permalink to this definition">¶</a></dt>
<dd><p>Base class for all optimizers.</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>params</strong> (<em>iterable</em>) – an iterable of <code class="xref py py-class docutils literal"><span class="pre">Variable</span></code> s or
<a class="reference external" href="https://docs.python.org/2/library/stdtypes.html#dict" title="(in Python v2.7)"><code class="xref py py-class docutils literal"><span class="pre">dict</span></code></a> s. Specifies what Variables should be optimized.</li>
<li><strong>defaults</strong> – (dict): a dict containing default values of optimization
options (used when a parameter group doesn’t specify them).</li>
</ul>
</td>
</tr>
</tbody>
</table>
<dl class="method">
<dt id="torch.optim.Optimizer.load_state_dict">
<code class="descname">load_state_dict</code><span class="sig-paren">(</span><em>state_dict</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/optim/optimizer.html#Optimizer.load_state_dict"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torch.optim.Optimizer.load_state_dict" title="Permalink to this definition">¶</a></dt>
<dd><p>Loads the optimizer state.</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>state_dict</strong> (<a class="reference external" href="https://docs.python.org/2/library/stdtypes.html#dict" title="(in Python v2.7)"><em>dict</em></a>) – optimizer state. Should be an object returned
from a call to <a class="reference internal" href="#torch.optim.Optimizer.state_dict" title="torch.optim.Optimizer.state_dict"><code class="xref py py-meth docutils literal"><span class="pre">state_dict()</span></code></a>.</td>
</tr>
</tbody>
</table>
</dd></dl>
<dl class="method">
<dt id="torch.optim.Optimizer.state_dict">
<code class="descname">state_dict</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/optim/optimizer.html#Optimizer.state_dict"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torch.optim.Optimizer.state_dict" title="Permalink to this definition">¶</a></dt>
<dd><p>Returns the state of the optimizer as a <a class="reference external" href="https://docs.python.org/2/library/stdtypes.html#dict" title="(in Python v2.7)"><code class="xref py py-class docutils literal"><span class="pre">dict</span></code></a>.</p>
<p>It contains two entries:</p>
<ul class="simple">
<li><dl class="first docutils">
<dt>state - a dict holding current optimization state. Its content</dt>
<dd>differs between optimizer classes.</dd>
</dl>
</li>
<li>param_groups - a dict containig all parameter groups</li>
</ul>
</dd></dl>
<dl class="method">
<dt id="torch.optim.Optimizer.step">
<code class="descname">step</code><span class="sig-paren">(</span><em>closure</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/optim/optimizer.html#Optimizer.step"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torch.optim.Optimizer.step" title="Permalink to this definition">¶</a></dt>
<dd><p>Performs a single optimization step (parameter update).</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>closure</strong> (<a class="reference external" href="https://docs.python.org/2/library/functions.html#callable" title="(in Python v2.7)"><em>callable</em></a>) – A closure that reevaluates the model and
returns the loss. Optional for most optimizers.</td>
</tr>
</tbody>
</table>
</dd></dl>
<dl class="method">
<dt id="torch.optim.Optimizer.zero_grad">
<code class="descname">zero_grad</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/optim/optimizer.html#Optimizer.zero_grad"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torch.optim.Optimizer.zero_grad" title="Permalink to this definition">¶</a></dt>
<dd><p>Clears the gradients of all optimized <code class="xref py py-class docutils literal"><span class="pre">Variable</span></code> s.</p>
</dd></dl>
</dd></dl>
<dl class="class">
<dt id="torch.optim.Adadelta">
<em class="property">class </em><code class="descclassname">torch.optim.</code><code class="descname">Adadelta</code><span class="sig-paren">(</span><em>params</em>, <em>lr=1.0</em>, <em>rho=0.9</em>, <em>eps=1e-06</em>, <em>weight_decay=0</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/optim/adadelta.html#Adadelta"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torch.optim.Adadelta" title="Permalink to this definition">¶</a></dt>
<dd><p>Implements Adadelta algorithm.</p>
<p>It has been proposed in <a class="reference external" href="https://arxiv.org/abs/1212.5701">ADADELTA: An Adaptive Learning Rate Method</a>.</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>params</strong> (<em>iterable</em>) – iterable of parameters to optimize or dicts defining
parameter groups</li>
<li><strong>rho</strong> (<a class="reference external" href="https://docs.python.org/2/library/functions.html#float" title="(in Python v2.7)"><em>float</em></a><em>, </em><em>optional</em>) – coefficient used for computing a running average
of squared gradients (default: 0.9)</li>
<li><strong>eps</strong> (<a class="reference external" href="https://docs.python.org/2/library/functions.html#float" title="(in Python v2.7)"><em>float</em></a><em>, </em><em>optional</em>) – term added to the denominator to improve
numerical stability (default: 1e-6)</li>
<li><strong>lr</strong> (<a class="reference external" href="https://docs.python.org/2/library/functions.html#float" title="(in Python v2.7)"><em>float</em></a><em>, </em><em>optional</em>) – coefficient that scale delta before it is applied to the
parameters (default: 1.0)</li>
<li><strong>weight_decay</strong> (<a class="reference external" href="https://docs.python.org/2/library/functions.html#float" title="(in Python v2.7)"><em>float</em></a><em>, </em><em>optional</em>) – weight decay (L2 penalty) (default: 0)</li>
</ul>
</td>
</tr>
</tbody>
</table>
<dl class="method">
<dt id="torch.optim.Adadelta.step">
<code class="descname">step</code><span class="sig-paren">(</span><em>closure=None</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/optim/adadelta.html#Adadelta.step"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torch.optim.Adadelta.step" title="Permalink to this definition">¶</a></dt>
<dd><p>Performs a single optimization step.</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>closure</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>) – A closure that reevaluates the model
and returns the loss.</td>
</tr>
</tbody>
</table>
</dd></dl>
</dd></dl>
<dl class="class">
<dt id="torch.optim.Adagrad">
<em class="property">class </em><code class="descclassname">torch.optim.</code><code class="descname">Adagrad</code><span class="sig-paren">(</span><em>params</em>, <em>lr=0.01</em>, <em>lr_decay=0</em>, <em>weight_decay=0</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/optim/adagrad.html#Adagrad"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torch.optim.Adagrad" title="Permalink to this definition">¶</a></dt>
<dd><p>Implements Adagrad algorithm.</p>
<p>It has been proposed in <a class="reference external" href="http://jmlr.org/papers/v12/duchi11a.html">Adaptive Subgradient Methods for Online Learning and Stochastic Optimization</a>.</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>params</strong> (<em>iterable</em>) – iterable of parameters to optimize or dicts defining
parameter groups</li>
<li><strong>lr</strong> (<a class="reference external" href="https://docs.python.org/2/library/functions.html#float" title="(in Python v2.7)"><em>float</em></a><em>, </em><em>optional</em>) – learning rate (default: 1e-2)</li>
<li><strong>lr_decay</strong> (<a class="reference external" href="https://docs.python.org/2/library/functions.html#float" title="(in Python v2.7)"><em>float</em></a><em>, </em><em>optional</em>) – learning rate decay (default: 0)</li>
<li><strong>weight_decay</strong> (<a class="reference external" href="https://docs.python.org/2/library/functions.html#float" title="(in Python v2.7)"><em>float</em></a><em>, </em><em>optional</em>) – weight decay (L2 penalty) (default: 0)</li>
</ul>
</td>
</tr>
</tbody>
</table>
<dl class="method">
<dt id="torch.optim.Adagrad.step">
<code class="descname">step</code><span class="sig-paren">(</span><em>closure=None</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/optim/adagrad.html#Adagrad.step"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torch.optim.Adagrad.step" title="Permalink to this definition">¶</a></dt>
<dd><p>Performs a single optimization step.</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>closure</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>) – A closure that reevaluates the model
and returns the loss.</td>
</tr>
</tbody>
</table>
</dd></dl>
</dd></dl>
<dl class="class">
<dt id="torch.optim.Adam">
<em class="property">class </em><code class="descclassname">torch.optim.</code><code class="descname">Adam</code><span class="sig-paren">(</span><em>params</em>, <em>lr=0.001</em>, <em>betas=(0.9</em>, <em>0.999)</em>, <em>eps=1e-08</em>, <em>weight_decay=0</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/optim/adam.html#Adam"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torch.optim.Adam" title="Permalink to this definition">¶</a></dt>
<dd><p>Implements Adam algorithm.</p>
<p>It has been proposed in <a class="reference external" href="https://arxiv.org/abs/1412.6980">Adam: A Method for Stochastic Optimization</a>.</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>params</strong> (<em>iterable</em>) – iterable of parameters to optimize or dicts defining
parameter groups</li>
<li><strong>lr</strong> (<a class="reference external" href="https://docs.python.org/2/library/functions.html#float" title="(in Python v2.7)"><em>float</em></a><em>, </em><em>optional</em>) – learning rate (default: 1e-3)</li>
<li><strong>betas</strong> (<em>Tuple</em><em>[</em><a class="reference external" href="https://docs.python.org/2/library/functions.html#float" title="(in Python v2.7)"><em>float</em></a><em>, </em><a class="reference external" href="https://docs.python.org/2/library/functions.html#float" title="(in Python v2.7)"><em>float</em></a><em>]</em><em>, </em><em>optional</em>) – coefficients used for computing
running averages of gradient and its square (default: (0.9, 0.999))</li>
<li><strong>eps</strong> (<a class="reference external" href="https://docs.python.org/2/library/functions.html#float" title="(in Python v2.7)"><em>float</em></a><em>, </em><em>optional</em>) – term added to the denominator to improve
numerical stability (default: 1e-8)</li>
<li><strong>weight_decay</strong> (<a class="reference external" href="https://docs.python.org/2/library/functions.html#float" title="(in Python v2.7)"><em>float</em></a><em>, </em><em>optional</em>) – weight decay (L2 penalty) (default: 0)</li>
</ul>
</td>
</tr>
</tbody>
</table>
<dl class="method">
<dt id="torch.optim.Adam.step">
<code class="descname">step</code><span class="sig-paren">(</span><em>closure=None</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/optim/adam.html#Adam.step"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torch.optim.Adam.step" title="Permalink to this definition">¶</a></dt>
<dd><p>Performs a single optimization step.</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>closure</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>) – A closure that reevaluates the model
and returns the loss.</td>
</tr>
</tbody>
</table>
</dd></dl>
</dd></dl>
<dl class="class">
<dt id="torch.optim.Adamax">
<em class="property">class </em><code class="descclassname">torch.optim.</code><code class="descname">Adamax</code><span class="sig-paren">(</span><em>params</em>, <em>lr=0.002</em>, <em>betas=(0.9</em>, <em>0.999)</em>, <em>eps=1e-08</em>, <em>weight_decay=0</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/optim/adamax.html#Adamax"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torch.optim.Adamax" title="Permalink to this definition">¶</a></dt>
<dd><p>Implements Adamax algorithm (a variant of Adam based on infinity norm).</p>
<p>It has been proposed in <a class="reference external" href="https://arxiv.org/abs/1412.6980">Adam: A Method for Stochastic Optimization</a>.</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>params</strong> (<em>iterable</em>) – iterable of parameters to optimize or dicts defining
parameter groups</li>
<li><strong>lr</strong> (<a class="reference external" href="https://docs.python.org/2/library/functions.html#float" title="(in Python v2.7)"><em>float</em></a><em>, </em><em>optional</em>) – learning rate (default: 2e-3)</li>
<li><strong>betas</strong> (<em>Tuple</em><em>[</em><a class="reference external" href="https://docs.python.org/2/library/functions.html#float" title="(in Python v2.7)"><em>float</em></a><em>, </em><a class="reference external" href="https://docs.python.org/2/library/functions.html#float" title="(in Python v2.7)"><em>float</em></a><em>]</em><em>, </em><em>optional</em>) – coefficients used for computing
running averages of gradient and its square</li>
<li><strong>eps</strong> (<a class="reference external" href="https://docs.python.org/2/library/functions.html#float" title="(in Python v2.7)"><em>float</em></a><em>, </em><em>optional</em>) – term added to the denominator to improve
numerical stability (default: 1e-8)</li>
<li><strong>weight_decay</strong> (<a class="reference external" href="https://docs.python.org/2/library/functions.html#float" title="(in Python v2.7)"><em>float</em></a><em>, </em><em>optional</em>) – weight decay (L2 penalty) (default: 0)</li>
</ul>
</td>
</tr>
</tbody>
</table>
<dl class="method">
<dt id="torch.optim.Adamax.step">
<code class="descname">step</code><span class="sig-paren">(</span><em>closure=None</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/optim/adamax.html#Adamax.step"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torch.optim.Adamax.step" title="Permalink to this definition">¶</a></dt>
<dd><p>Performs a single optimization step.</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>closure</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>) – A closure that reevaluates the model
and returns the loss.</td>
</tr>
</tbody>
</table>
</dd></dl>
</dd></dl>
<dl class="class">
<dt id="torch.optim.ASGD">
<em class="property">class </em><code class="descclassname">torch.optim.</code><code class="descname">ASGD</code><span class="sig-paren">(</span><em>params</em>, <em>lr=0.01</em>, <em>lambd=0.0001</em>, <em>alpha=0.75</em>, <em>t0=1000000.0</em>, <em>weight_decay=0</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/optim/asgd.html#ASGD"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torch.optim.ASGD" title="Permalink to this definition">¶</a></dt>
<dd><p>Implements Averaged Stochastic Gradient Descent.</p>
<p>It has been proposed in <a class="reference external" href="http://dl.acm.org/citation.cfm?id=131098">Acceleration of stochastic approximation by averaging</a>.</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>params</strong> (<em>iterable</em>) – iterable of parameters to optimize or dicts defining
parameter groups</li>
<li><strong>lr</strong> (<a class="reference external" href="https://docs.python.org/2/library/functions.html#float" title="(in Python v2.7)"><em>float</em></a><em>, </em><em>optional</em>) – learning rate (default: 1e-2)</li>
<li><strong>lambd</strong> (<a class="reference external" href="https://docs.python.org/2/library/functions.html#float" title="(in Python v2.7)"><em>float</em></a><em>, </em><em>optional</em>) – decay term (default: 1e-4)</li>
<li><strong>alpha</strong> (<a class="reference external" href="https://docs.python.org/2/library/functions.html#float" title="(in Python v2.7)"><em>float</em></a><em>, </em><em>optional</em>) – power for eta update (default: 0.75)</li>
<li><strong>t0</strong> (<a class="reference external" href="https://docs.python.org/2/library/functions.html#float" title="(in Python v2.7)"><em>float</em></a><em>, </em><em>optional</em>) – point at which to start averaging (default: 1e6)</li>
<li><strong>weight_decay</strong> (<a class="reference external" href="https://docs.python.org/2/library/functions.html#float" title="(in Python v2.7)"><em>float</em></a><em>, </em><em>optional</em>) – weight decay (L2 penalty) (default: 0)</li>
</ul>
</td>
</tr>
</tbody>
</table>
<dl class="method">
<dt id="torch.optim.ASGD.step">
<code class="descname">step</code><span class="sig-paren">(</span><em>closure=None</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/optim/asgd.html#ASGD.step"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torch.optim.ASGD.step" title="Permalink to this definition">¶</a></dt>
<dd><p>Performs a single optimization step.</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>closure</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>) – A closure that reevaluates the model
and returns the loss.</td>
</tr>
</tbody>
</table>
</dd></dl>
</dd></dl>
<dl class="class">
<dt id="torch.optim.LBFGS">
<em class="property">class </em><code class="descclassname">torch.optim.</code><code class="descname">LBFGS</code><span class="sig-paren">(</span><em>params</em>, <em>lr=1</em>, <em>max_iter=20</em>, <em>max_eval=None</em>, <em>tolerance_grad=1e-05</em>, <em>tolerance_change=1e-09</em>, <em>history_size=100</em>, <em>line_search_fn=None</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/optim/lbfgs.html#LBFGS"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torch.optim.LBFGS" title="Permalink to this definition">¶</a></dt>
<dd><p>Implements L-BFGS algorithm.</p>
<div class="admonition warning">
<p class="first admonition-title">Warning</p>
<p class="last">This optimizer doesn’t support per-parameter options and parameter
groups (there can be only one).</p>
</div>
<div class="admonition warning">
<p class="first admonition-title">Warning</p>
<p class="last">Right now all parameters have to be on a single device. This will be
improved in the future.</p>
</div>
<div class="admonition note">
<p class="first admonition-title">Note</p>
<p class="last">This is a very memory intensive optimizer (it requires additional
<code class="docutils literal"><span class="pre">param_bytes</span> <span class="pre">*</span> <span class="pre">(history_size</span> <span class="pre">+</span> <span class="pre">1)</span></code> bytes). If it doesn’t fit in memory
try reducing the history size, or use a different algorithm.</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>lr</strong> (<a class="reference external" href="https://docs.python.org/2/library/functions.html#float" title="(in Python v2.7)"><em>float</em></a>) – learning rate (default: 1)</li>
<li><strong>max_iter</strong> (<a class="reference external" href="https://docs.python.org/2/library/functions.html#int" title="(in Python v2.7)"><em>int</em></a>) – maximal number of iterations per optimization step
(default: 20)</li>
<li><strong>max_eval</strong> (<a class="reference external" href="https://docs.python.org/2/library/functions.html#int" title="(in Python v2.7)"><em>int</em></a>) – maximal number of function evaluations per optimization
step (default: max_iter * 1.25).</li>
<li><strong>tolerance_grad</strong> (<a class="reference external" href="https://docs.python.org/2/library/functions.html#float" title="(in Python v2.7)"><em>float</em></a>) – termination tolerance on first order optimality
(default: 1e-5).</li>
<li><strong>tolerance_change</strong> (<a class="reference external" href="https://docs.python.org/2/library/functions.html#float" title="(in Python v2.7)"><em>float</em></a>) – termination tolerance on function value/parameter
changes (default: 1e-9).</li>
<li><strong>history_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>) – update history size (default: 100).</li>
</ul>
</td>
</tr>
</tbody>
</table>
<dl class="method">
<dt id="torch.optim.LBFGS.step">
<code class="descname">step</code><span class="sig-paren">(</span><em>closure</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/optim/lbfgs.html#LBFGS.step"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torch.optim.LBFGS.step" title="Permalink to this definition">¶</a></dt>
<dd><p>Performs a single optimization step.</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>closure</strong> (<a class="reference external" href="https://docs.python.org/2/library/functions.html#callable" title="(in Python v2.7)"><em>callable</em></a>) – A closure that reevaluates the model
and returns the loss.</td>
</tr>
</tbody>
</table>
</dd></dl>
</dd></dl>
<dl class="class">
<dt id="torch.optim.RMSprop">
<em class="property">class </em><code class="descclassname">torch.optim.</code><code class="descname">RMSprop</code><span class="sig-paren">(</span><em>params</em>, <em>lr=0.01</em>, <em>alpha=0.99</em>, <em>eps=1e-08</em>, <em>weight_decay=0</em>, <em>momentum=0</em>, <em>centered=False</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/optim/rmsprop.html#RMSprop"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torch.optim.RMSprop" title="Permalink to this definition">¶</a></dt>
<dd><p>Implements RMSprop algorithm.</p>
<p>Proposed by G. Hinton in his <a class="reference external" href="http://www.cs.toronto.edu/~tijmen/csc321/slides/lecture_slides_lec6.pdf">course</a>.</p>
<p>The centered version first appears in <a class="reference external" href="https://arxiv.org/pdf/1308.0850v5.pdf">Generating Sequences
With Recurrent Neural Networks</a>.</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>params</strong> (<em>iterable</em>) – iterable of parameters to optimize or dicts defining
parameter groups</li>
<li><strong>lr</strong> (<a class="reference external" href="https://docs.python.org/2/library/functions.html#float" title="(in Python v2.7)"><em>float</em></a><em>, </em><em>optional</em>) – learning rate (default: 1e-2)</li>
<li><strong>momentum</strong> (<a class="reference external" href="https://docs.python.org/2/library/functions.html#float" title="(in Python v2.7)"><em>float</em></a><em>, </em><em>optional</em>) – momentum factor (default: 0)</li>
<li><strong>alpha</strong> (<a class="reference external" href="https://docs.python.org/2/library/functions.html#float" title="(in Python v2.7)"><em>float</em></a><em>, </em><em>optional</em>) – smoothing constant (default: 0.99)</li>
<li><strong>eps</strong> (<a class="reference external" href="https://docs.python.org/2/library/functions.html#float" title="(in Python v2.7)"><em>float</em></a><em>, </em><em>optional</em>) – term added to the denominator to improve
numerical stability (default: 1e-8)</li>
<li><strong>centered</strong> (<a class="reference external" href="https://docs.python.org/2/library/functions.html#bool" title="(in Python v2.7)"><em>bool</em></a><em>, </em><em>optional</em>) – if True, compute the centered RMSProp,
the gradient is normalized by an estimation of its variance</li>
<li><strong>weight_decay</strong> (<a class="reference external" href="https://docs.python.org/2/library/functions.html#float" title="(in Python v2.7)"><em>float</em></a><em>, </em><em>optional</em>) – weight decay (L2 penalty) (default: 0)</li>
</ul>
</td>
</tr>
</tbody>
</table>
<dl class="method">
<dt id="torch.optim.RMSprop.step">
<code class="descname">step</code><span class="sig-paren">(</span><em>closure=None</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/optim/rmsprop.html#RMSprop.step"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torch.optim.RMSprop.step" title="Permalink to this definition">¶</a></dt>
<dd><p>Performs a single optimization step.</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>closure</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>) – A closure that reevaluates the model
and returns the loss.</td>
</tr>
</tbody>
</table>
</dd></dl>
</dd></dl>
<dl class="class">
<dt id="torch.optim.Rprop">
<em class="property">class </em><code class="descclassname">torch.optim.</code><code class="descname">Rprop</code><span class="sig-paren">(</span><em>params</em>, <em>lr=0.01</em>, <em>etas=(0.5</em>, <em>1.2)</em>, <em>step_sizes=(1e-06</em>, <em>50)</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/optim/rprop.html#Rprop"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torch.optim.Rprop" title="Permalink to this definition">¶</a></dt>
<dd><p>Implements the resilient backpropagation algorithm.</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>params</strong> (<em>iterable</em>) – iterable of parameters to optimize or dicts defining
parameter groups</li>
<li><strong>lr</strong> (<a class="reference external" href="https://docs.python.org/2/library/functions.html#float" title="(in Python v2.7)"><em>float</em></a><em>, </em><em>optional</em>) – learning rate (default: 1e-2)</li>
<li><strong>etas</strong> (<em>Tuple</em><em>[</em><a class="reference external" href="https://docs.python.org/2/library/functions.html#float" title="(in Python v2.7)"><em>float</em></a><em>, </em><a class="reference external" href="https://docs.python.org/2/library/functions.html#float" title="(in Python v2.7)"><em>float</em></a><em>]</em><em>, </em><em>optional</em>) – pair of (etaminus, etaplis), that
are multiplicative increase and decrease factors (default: (0.5, 1.2))</li>
<li><strong>step_sizes</strong> (<em>Tuple</em><em>[</em><a class="reference external" href="https://docs.python.org/2/library/functions.html#float" title="(in Python v2.7)"><em>float</em></a><em>, </em><a class="reference external" href="https://docs.python.org/2/library/functions.html#float" title="(in Python v2.7)"><em>float</em></a><em>]</em><em>, </em><em>optional</em>) – a pair of minimal and
maximal allowed step sizes (default: (1e-6, 50))</li>
</ul>
</td>
</tr>
</tbody>
</table>
<dl class="method">
<dt id="torch.optim.Rprop.step">
<code class="descname">step</code><span class="sig-paren">(</span><em>closure=None</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/optim/rprop.html#Rprop.step"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torch.optim.Rprop.step" title="Permalink to this definition">¶</a></dt>
<dd><p>Performs a single optimization step.</p>
<table class="docutils field-list" frame="void" rules="none">