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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 heading">¶</a></h1>
<p><a class="reference internal" href="#module-torch.optim" title="torch.optim"><code class="xref py py-mod docutils literal notranslate"><span class="pre">torch.optim</span></code></a> is a package implementing various optimization algorithms.</p>
<p>Most commonly used methods are already supported, and the interface is general
enough, so that more sophisticated ones can also be 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 heading">¶</a></h2>
<p>To use <a class="reference internal" href="#module-torch.optim" title="torch.optim"><code class="xref py py-mod docutils literal notranslate"><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 heading">¶</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 notranslate"><span class="pre">Optimizer</span></code></a> you have to give it an iterable containing the
parameters (all should be <code class="xref py py-class docutils literal notranslate"><span class="pre">Variable</span></code> 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 notranslate"><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 heading">¶</a></h3>
<p><a class="reference internal" href="#torch.optim.Optimizer" title="torch.optim.Optimizer"><code class="xref py py-class docutils literal notranslate"><span class="pre">Optimizer</span></code></a> s also support specifying per-parameter options. To do this, instead
of passing an iterable of <code class="xref py py-class docutils literal notranslate"><span class="pre">Variable</span></code> s, pass in an iterable of
<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.13)"><code class="xref py py-class docutils literal notranslate"><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 notranslate"><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>
<p>For example, this is very useful when one wants to specify per-layer learning rates:</p>
<div class="highlight-default notranslate"><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="s1">'lr'</span><span class="p">:</span> <span class="mf">1e-2</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="p">],</span> <span class="n">lr</span><span class="o">=</span><span class="mf">1e-3</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 notranslate"><span class="pre">model.base</span></code>’s parameters will use a learning rate of <code class="docutils literal notranslate"><span class="pre">1e-2</span></code>, whereas
<code class="docutils literal notranslate"><span class="pre">model.classifier</span></code>’s parameters will stick to the default learning rate of <code class="docutils literal notranslate"><span class="pre">1e-3</span></code>.
Finally a momentum of <code class="docutils literal notranslate"><span class="pre">0.9</span></code> will be used for all parameters.</p>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p>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>Also consider the following example related to the distinct penalization of parameters.
Remember that <a class="reference internal" href="generated/torch.nn.Module.html#torch.nn.Module.parameters" title="torch.nn.Module.parameters"><code class="xref py py-func docutils literal notranslate"><span class="pre">parameters()</span></code></a> returns an iterable that
contains all learnable parameters, including biases and other
parameters that may prefer distinct penalization. To address this, one can specify
individual penalization weights for each parameter group:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">bias_params</span> <span class="o">=</span> <span class="p">[</span><span class="n">p</span> <span class="k">for</span> <span class="n">name</span><span class="p">,</span> <span class="n">p</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">named_parameters</span><span class="p">()</span> <span class="k">if</span> <span class="s1">'bias'</span> <span class="ow">in</span> <span class="n">name</span><span class="p">]</span>
<span class="n">others</span> <span class="o">=</span> <span class="p">[</span><span class="n">p</span> <span class="k">for</span> <span class="n">name</span><span class="p">,</span> <span class="n">p</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">named_parameters</span><span class="p">()</span> <span class="k">if</span> <span class="s1">'bias'</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">name</span><span class="p">]</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">others</span><span class="p">},</span>
<span class="p">{</span><span class="s1">'params'</span><span class="p">:</span> <span class="n">bias_params</span><span class="p">,</span> <span class="s1">'weight_decay'</span><span class="p">:</span> <span class="mi">0</span><span class="p">}</span>
<span class="p">],</span> <span class="n">weight_decay</span><span class="o">=</span><span class="mf">1e-2</span><span class="p">,</span> <span class="n">lr</span><span class="o">=</span><span class="mf">1e-2</span><span class="p">)</span>
</pre></div>
</div>
<p>In this manner, bias terms are isolated from non-bias terms, and a <code class="docutils literal notranslate"><span class="pre">weight_decay</span></code>
of <code class="docutils literal notranslate"><span class="pre">0</span></code> is set specifically for the bias terms, as to avoid any penalization for
this group.</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 heading">¶</a></h3>
<p>All optimizers implement a <a class="reference internal" href="generated/torch.optim.Optimizer.step.html#torch.optim.Optimizer.step" title="torch.optim.Optimizer.step"><code class="xref py py-func docutils literal notranslate"><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 notranslate"><span class="pre">optimizer.step()</span></code><a class="headerlink" href="#optimizer-step" title="Permalink to this heading">¶</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.
<code class="xref py py-func docutils literal notranslate"><span class="pre">backward()</span></code>.</p>
<p>Example:</p>
<div class="highlight-default notranslate"><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 notranslate"><span class="pre">optimizer.step(closure)</span></code><a class="headerlink" href="#optimizer-step-closure" title="Permalink to this heading">¶</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 notranslate"><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="base-class">
<span id="optimizer-algorithms"></span><h2>Base class<a class="headerlink" href="#base-class" title="Permalink to this heading">¶</a></h2>
<dl class="py class">
<dt class="sig sig-object py" id="torch.optim.Optimizer">
<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.optim.</span></span><span class="sig-name descname"><span class="pre">Optimizer</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">params</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">defaults</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/optim/optimizer.html#Optimizer"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.Optimizer" title="Permalink to this definition">¶</a></dt>
<dd><p>Base class for all optimizers.</p>
<div class="admonition warning">
<p class="admonition-title">Warning</p>
<p>Parameters need to be specified as collections that have a deterministic
ordering that is consistent between runs. Examples of objects that don’t
satisfy those properties are sets and iterators over values of dictionaries.</p>
</div>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>params</strong> (<em>iterable</em>) – an iterable of <a class="reference internal" href="tensors.html#torch.Tensor" title="torch.Tensor"><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.Tensor</span></code></a> s or
<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.13)"><code class="xref py py-class docutils literal notranslate"><span class="pre">dict</span></code></a> s. Specifies what Tensors should be optimized.</p></li>
<li><p><strong>defaults</strong> (<a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Dict" title="(in Python v3.13)"><em>Dict</em></a><em>[</em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><em>str</em></a><em>, </em><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Any" title="(in Python v3.13)"><em>Any</em></a><em>]</em>) – (dict): a dict containing default values of optimization
options (used when a parameter group doesn’t specify them).</p></li>
</ul>
</dd>
</dl>
</dd></dl>
<table class="autosummary longtable docutils colwidths-auto align-default">
<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/torch.optim.Optimizer.add_param_group.html#torch.optim.Optimizer.add_param_group" title="torch.optim.Optimizer.add_param_group"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Optimizer.add_param_group</span></code></a></p></td>
<td><p>Add a param group to the <a class="reference internal" href="#torch.optim.Optimizer" title="torch.optim.Optimizer"><code class="xref py py-class docutils literal notranslate"><span class="pre">Optimizer</span></code></a> s <cite>param_groups</cite>.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/torch.optim.Optimizer.load_state_dict.html#torch.optim.Optimizer.load_state_dict" title="torch.optim.Optimizer.load_state_dict"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Optimizer.load_state_dict</span></code></a></p></td>
<td><p>Load the optimizer state.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/torch.optim.Optimizer.register_load_state_dict_pre_hook.html#torch.optim.Optimizer.register_load_state_dict_pre_hook" title="torch.optim.Optimizer.register_load_state_dict_pre_hook"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Optimizer.register_load_state_dict_pre_hook</span></code></a></p></td>
<td><p>Register a load_state_dict pre-hook which will be called before <a class="reference internal" href="generated/torch.optim.Optimizer.load_state_dict.html#torch.optim.Optimizer.load_state_dict" title="torch.optim.Optimizer.load_state_dict"><code class="xref py py-meth docutils literal notranslate"><span class="pre">load_state_dict()</span></code></a> is called. It should have the following signature::.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/torch.optim.Optimizer.register_load_state_dict_post_hook.html#torch.optim.Optimizer.register_load_state_dict_post_hook" title="torch.optim.Optimizer.register_load_state_dict_post_hook"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Optimizer.register_load_state_dict_post_hook</span></code></a></p></td>
<td><p>Register a load_state_dict post-hook which will be called after <a class="reference internal" href="generated/torch.optim.Optimizer.load_state_dict.html#torch.optim.Optimizer.load_state_dict" title="torch.optim.Optimizer.load_state_dict"><code class="xref py py-meth docutils literal notranslate"><span class="pre">load_state_dict()</span></code></a> is called. It should have the following signature::.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/torch.optim.Optimizer.state_dict.html#torch.optim.Optimizer.state_dict" title="torch.optim.Optimizer.state_dict"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Optimizer.state_dict</span></code></a></p></td>
<td><p>Return the state of the optimizer as a <a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.13)"><code class="xref py py-class docutils literal notranslate"><span class="pre">dict</span></code></a>.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/torch.optim.Optimizer.register_state_dict_pre_hook.html#torch.optim.Optimizer.register_state_dict_pre_hook" title="torch.optim.Optimizer.register_state_dict_pre_hook"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Optimizer.register_state_dict_pre_hook</span></code></a></p></td>
<td><p>Register a state dict pre-hook which will be called before <a class="reference internal" href="generated/torch.optim.Optimizer.state_dict.html#torch.optim.Optimizer.state_dict" title="torch.optim.Optimizer.state_dict"><code class="xref py py-meth docutils literal notranslate"><span class="pre">state_dict()</span></code></a> is called.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/torch.optim.Optimizer.register_state_dict_post_hook.html#torch.optim.Optimizer.register_state_dict_post_hook" title="torch.optim.Optimizer.register_state_dict_post_hook"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Optimizer.register_state_dict_post_hook</span></code></a></p></td>
<td><p>Register a state dict post-hook which will be called after <a class="reference internal" href="generated/torch.optim.Optimizer.state_dict.html#torch.optim.Optimizer.state_dict" title="torch.optim.Optimizer.state_dict"><code class="xref py py-meth docutils literal notranslate"><span class="pre">state_dict()</span></code></a> is called.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/torch.optim.Optimizer.step.html#torch.optim.Optimizer.step" title="torch.optim.Optimizer.step"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Optimizer.step</span></code></a></p></td>
<td><p>Perform a single optimization step to update parameter.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/torch.optim.Optimizer.register_step_pre_hook.html#torch.optim.Optimizer.register_step_pre_hook" title="torch.optim.Optimizer.register_step_pre_hook"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Optimizer.register_step_pre_hook</span></code></a></p></td>
<td><p>Register an optimizer step pre hook which will be called before optimizer step.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/torch.optim.Optimizer.register_step_post_hook.html#torch.optim.Optimizer.register_step_post_hook" title="torch.optim.Optimizer.register_step_post_hook"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Optimizer.register_step_post_hook</span></code></a></p></td>
<td><p>Register an optimizer step post hook which will be called after optimizer step.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/torch.optim.Optimizer.zero_grad.html#torch.optim.Optimizer.zero_grad" title="torch.optim.Optimizer.zero_grad"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Optimizer.zero_grad</span></code></a></p></td>
<td><p>Reset the gradients of all optimized <a class="reference internal" href="tensors.html#torch.Tensor" title="torch.Tensor"><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.Tensor</span></code></a> s.</p></td>
</tr>
</tbody>
</table>
</div>
<div class="section" id="algorithms">
<h2>Algorithms<a class="headerlink" href="#algorithms" title="Permalink to this heading">¶</a></h2>
<table class="autosummary longtable docutils colwidths-auto align-default">
<tbody>
<tr class="row-odd"><td><p><p id="torch.optim.Adadelta"/><a class="reference internal" href="generated/torch.optim.Adadelta.html#torch.optim.Adadelta" title="torch.optim.Adadelta"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Adadelta</span></code></a></p></td>
<td><p>Implements Adadelta algorithm.</p></td>
</tr>
<tr class="row-even"><td><p><p id="torch.optim.Adafactor"/><a class="reference internal" href="generated/torch.optim.Adafactor.html#torch.optim.Adafactor" title="torch.optim.Adafactor"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Adafactor</span></code></a></p></td>
<td><p>Implements Adafactor algorithm.</p></td>
</tr>
<tr class="row-odd"><td><p><p id="torch.optim.Adagrad"/><a class="reference internal" href="generated/torch.optim.Adagrad.html#torch.optim.Adagrad" title="torch.optim.Adagrad"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Adagrad</span></code></a></p></td>
<td><p>Implements Adagrad algorithm.</p></td>
</tr>
<tr class="row-even"><td><p><p id="torch.optim.Adam"/><a class="reference internal" href="generated/torch.optim.Adam.html#torch.optim.Adam" title="torch.optim.Adam"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Adam</span></code></a></p></td>
<td><p>Implements Adam algorithm.</p></td>
</tr>
<tr class="row-odd"><td><p><p id="torch.optim.AdamW"/><a class="reference internal" href="generated/torch.optim.AdamW.html#torch.optim.AdamW" title="torch.optim.AdamW"><code class="xref py py-obj docutils literal notranslate"><span class="pre">AdamW</span></code></a></p></td>
<td><p>Implements AdamW algorithm.</p></td>
</tr>
<tr class="row-even"><td><p><p id="torch.optim.SparseAdam"/><a class="reference internal" href="generated/torch.optim.SparseAdam.html#torch.optim.SparseAdam" title="torch.optim.SparseAdam"><code class="xref py py-obj docutils literal notranslate"><span class="pre">SparseAdam</span></code></a></p></td>
<td><p>SparseAdam implements a masked version of the Adam algorithm suitable for sparse gradients.</p></td>
</tr>
<tr class="row-odd"><td><p><p id="torch.optim.Adamax"/><a class="reference internal" href="generated/torch.optim.Adamax.html#torch.optim.Adamax" title="torch.optim.Adamax"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Adamax</span></code></a></p></td>
<td><p>Implements Adamax algorithm (a variant of Adam based on infinity norm).</p></td>
</tr>
<tr class="row-even"><td><p><p id="torch.optim.ASGD"/><a class="reference internal" href="generated/torch.optim.ASGD.html#torch.optim.ASGD" title="torch.optim.ASGD"><code class="xref py py-obj docutils literal notranslate"><span class="pre">ASGD</span></code></a></p></td>
<td><p>Implements Averaged Stochastic Gradient Descent.</p></td>
</tr>
<tr class="row-odd"><td><p><p id="torch.optim.LBFGS"/><a class="reference internal" href="generated/torch.optim.LBFGS.html#torch.optim.LBFGS" title="torch.optim.LBFGS"><code class="xref py py-obj docutils literal notranslate"><span class="pre">LBFGS</span></code></a></p></td>
<td><p>Implements L-BFGS algorithm.</p></td>
</tr>
<tr class="row-even"><td><p><p id="torch.optim.NAdam"/><a class="reference internal" href="generated/torch.optim.NAdam.html#torch.optim.NAdam" title="torch.optim.NAdam"><code class="xref py py-obj docutils literal notranslate"><span class="pre">NAdam</span></code></a></p></td>
<td><p>Implements NAdam algorithm.</p></td>
</tr>
<tr class="row-odd"><td><p><p id="torch.optim.RAdam"/><a class="reference internal" href="generated/torch.optim.RAdam.html#torch.optim.RAdam" title="torch.optim.RAdam"><code class="xref py py-obj docutils literal notranslate"><span class="pre">RAdam</span></code></a></p></td>
<td><p>Implements RAdam algorithm.</p></td>
</tr>
<tr class="row-even"><td><p><p id="torch.optim.RMSprop"/><a class="reference internal" href="generated/torch.optim.RMSprop.html#torch.optim.RMSprop" title="torch.optim.RMSprop"><code class="xref py py-obj docutils literal notranslate"><span class="pre">RMSprop</span></code></a></p></td>
<td><p>Implements RMSprop algorithm.</p></td>
</tr>
<tr class="row-odd"><td><p><p id="torch.optim.Rprop"/><a class="reference internal" href="generated/torch.optim.Rprop.html#torch.optim.Rprop" title="torch.optim.Rprop"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Rprop</span></code></a></p></td>
<td><p>Implements the resilient backpropagation algorithm.</p></td>
</tr>
<tr class="row-even"><td><p><p id="torch.optim.SGD"/><a class="reference internal" href="generated/torch.optim.SGD.html#torch.optim.SGD" title="torch.optim.SGD"><code class="xref py py-obj docutils literal notranslate"><span class="pre">SGD</span></code></a></p></td>
<td><p>Implements stochastic gradient descent (optionally with momentum).</p></td>
</tr>
</tbody>
</table>
<p>Many of our algorithms have various implementations optimized for performance,
readability and/or generality, so we attempt to default to the generally fastest
implementation for the current device if no particular implementation has been
specified by the user.</p>
<p>We have 3 major categories of implementations: for-loop, foreach (multi-tensor), and
fused. The most straightforward implementations are for-loops over the parameters with
big chunks of computation. For-looping is usually slower than our foreach
implementations, which combine parameters into a multi-tensor and run the big chunks
of computation all at once, thereby saving many sequential kernel calls. A few of our
optimizers have even faster fused implementations, which fuse the big chunks of
computation into one kernel. We can think of foreach implementations as fusing
horizontally and fused implementations as fusing vertically on top of that.</p>
<p>In general, the performance ordering of the 3 implementations is fused > foreach > for-loop.
So when applicable, we default to foreach over for-loop. Applicable means the foreach
implementation is available, the user has not specified any implementation-specific kwargs
(e.g., fused, foreach, differentiable), and all tensors are native. Note that while fused
should be even faster than foreach, the implementations are newer and we would like to give
them more bake-in time before flipping the switch everywhere. We summarize the stability status
for each implementation on the second table below, you are welcome to try them out though!</p>
<p>Below is a table showing the available and default implementations of each algorithm:</p>
<table class="colwidths-given docutils colwidths-auto align-default">
<colgroup>
<col style="width: 25%" />
<col style="width: 25%" />
<col style="width: 25%" />
<col style="width: 25%" />
</colgroup>
<thead>
<tr class="row-odd"><th class="head"><p>Algorithm</p></th>
<th class="head"><p>Default</p></th>
<th class="head"><p>Has foreach?</p></th>
<th class="head"><p>Has fused?</p></th>
</tr>
</thead>
<tbody>
<tr class="row-even"><td><p><p id="torch.optim.Adadelta"/><a class="reference internal" href="generated/torch.optim.Adadelta.html#torch.optim.Adadelta" title="torch.optim.Adadelta"><code class="xref py py-class docutils literal notranslate"><span class="pre">Adadelta</span></code></a></p></td>
<td><p>foreach</p></td>
<td><p>yes</p></td>
<td><p>no</p></td>
</tr>
<tr class="row-odd"><td><p><p id="torch.optim.Adafactor"/><a class="reference internal" href="generated/torch.optim.Adafactor.html#torch.optim.Adafactor" title="torch.optim.Adafactor"><code class="xref py py-class docutils literal notranslate"><span class="pre">Adafactor</span></code></a></p></td>
<td><p>for-loop</p></td>
<td><p>no</p></td>
<td><p>no</p></td>
</tr>
<tr class="row-even"><td><p><p id="torch.optim.Adagrad"/><a class="reference internal" href="generated/torch.optim.Adagrad.html#torch.optim.Adagrad" title="torch.optim.Adagrad"><code class="xref py py-class docutils literal notranslate"><span class="pre">Adagrad</span></code></a></p></td>
<td><p>foreach</p></td>
<td><p>yes</p></td>
<td><p>yes (cpu only)</p></td>
</tr>
<tr class="row-odd"><td><p><p id="torch.optim.Adam"/><a class="reference internal" href="generated/torch.optim.Adam.html#torch.optim.Adam" title="torch.optim.Adam"><code class="xref py py-class docutils literal notranslate"><span class="pre">Adam</span></code></a></p></td>
<td><p>foreach</p></td>
<td><p>yes</p></td>
<td><p>yes</p></td>
</tr>
<tr class="row-even"><td><p><p id="torch.optim.AdamW"/><a class="reference internal" href="generated/torch.optim.AdamW.html#torch.optim.AdamW" title="torch.optim.AdamW"><code class="xref py py-class docutils literal notranslate"><span class="pre">AdamW</span></code></a></p></td>
<td><p>foreach</p></td>
<td><p>yes</p></td>
<td><p>yes</p></td>
</tr>
<tr class="row-odd"><td><p><p id="torch.optim.SparseAdam"/><a class="reference internal" href="generated/torch.optim.SparseAdam.html#torch.optim.SparseAdam" title="torch.optim.SparseAdam"><code class="xref py py-class docutils literal notranslate"><span class="pre">SparseAdam</span></code></a></p></td>
<td><p>for-loop</p></td>
<td><p>no</p></td>
<td><p>no</p></td>
</tr>
<tr class="row-even"><td><p><p id="torch.optim.Adamax"/><a class="reference internal" href="generated/torch.optim.Adamax.html#torch.optim.Adamax" title="torch.optim.Adamax"><code class="xref py py-class docutils literal notranslate"><span class="pre">Adamax</span></code></a></p></td>
<td><p>foreach</p></td>
<td><p>yes</p></td>
<td><p>no</p></td>
</tr>
<tr class="row-odd"><td><p><p id="torch.optim.ASGD"/><a class="reference internal" href="generated/torch.optim.ASGD.html#torch.optim.ASGD" title="torch.optim.ASGD"><code class="xref py py-class docutils literal notranslate"><span class="pre">ASGD</span></code></a></p></td>
<td><p>foreach</p></td>
<td><p>yes</p></td>
<td><p>no</p></td>
</tr>
<tr class="row-even"><td><p><p id="torch.optim.LBFGS"/><a class="reference internal" href="generated/torch.optim.LBFGS.html#torch.optim.LBFGS" title="torch.optim.LBFGS"><code class="xref py py-class docutils literal notranslate"><span class="pre">LBFGS</span></code></a></p></td>
<td><p>for-loop</p></td>
<td><p>no</p></td>
<td><p>no</p></td>
</tr>
<tr class="row-odd"><td><p><p id="torch.optim.NAdam"/><a class="reference internal" href="generated/torch.optim.NAdam.html#torch.optim.NAdam" title="torch.optim.NAdam"><code class="xref py py-class docutils literal notranslate"><span class="pre">NAdam</span></code></a></p></td>
<td><p>foreach</p></td>
<td><p>yes</p></td>
<td><p>no</p></td>
</tr>
<tr class="row-even"><td><p><p id="torch.optim.RAdam"/><a class="reference internal" href="generated/torch.optim.RAdam.html#torch.optim.RAdam" title="torch.optim.RAdam"><code class="xref py py-class docutils literal notranslate"><span class="pre">RAdam</span></code></a></p></td>
<td><p>foreach</p></td>
<td><p>yes</p></td>
<td><p>no</p></td>
</tr>
<tr class="row-odd"><td><p><p id="torch.optim.RMSprop"/><a class="reference internal" href="generated/torch.optim.RMSprop.html#torch.optim.RMSprop" title="torch.optim.RMSprop"><code class="xref py py-class docutils literal notranslate"><span class="pre">RMSprop</span></code></a></p></td>
<td><p>foreach</p></td>
<td><p>yes</p></td>
<td><p>no</p></td>
</tr>
<tr class="row-even"><td><p><p id="torch.optim.Rprop"/><a class="reference internal" href="generated/torch.optim.Rprop.html#torch.optim.Rprop" title="torch.optim.Rprop"><code class="xref py py-class docutils literal notranslate"><span class="pre">Rprop</span></code></a></p></td>
<td><p>foreach</p></td>
<td><p>yes</p></td>
<td><p>no</p></td>
</tr>
<tr class="row-odd"><td><p><p id="torch.optim.SGD"/><a class="reference internal" href="generated/torch.optim.SGD.html#torch.optim.SGD" title="torch.optim.SGD"><code class="xref py py-class docutils literal notranslate"><span class="pre">SGD</span></code></a></p></td>
<td><p>foreach</p></td>
<td><p>yes</p></td>
<td><p>yes</p></td>
</tr>
</tbody>
</table>
<p>Below table is showing the stability status for fused implementations:</p>
<table class="colwidths-given docutils colwidths-auto align-default">
<colgroup>
<col style="width: 25%" />
<col style="width: 25%" />
<col style="width: 25%" />
<col style="width: 25%" />
</colgroup>
<thead>
<tr class="row-odd"><th class="head"><p>Algorithm</p></th>
<th class="head"><p>CPU</p></th>
<th class="head"><p>CUDA</p></th>
<th class="head"><p>MPS</p></th>
</tr>
</thead>
<tbody>
<tr class="row-even"><td><p><p id="torch.optim.Adadelta"/><a class="reference internal" href="generated/torch.optim.Adadelta.html#torch.optim.Adadelta" title="torch.optim.Adadelta"><code class="xref py py-class docutils literal notranslate"><span class="pre">Adadelta</span></code></a></p></td>
<td><p>unsupported</p></td>
<td><p>unsupported</p></td>
<td><p>unsupported</p></td>
</tr>
<tr class="row-odd"><td><p><p id="torch.optim.Adafactor"/><a class="reference internal" href="generated/torch.optim.Adafactor.html#torch.optim.Adafactor" title="torch.optim.Adafactor"><code class="xref py py-class docutils literal notranslate"><span class="pre">Adafactor</span></code></a></p></td>
<td><p>unsupported</p></td>
<td><p>unsupported</p></td>
<td><p>unsupported</p></td>
</tr>
<tr class="row-even"><td><p><p id="torch.optim.Adagrad"/><a class="reference internal" href="generated/torch.optim.Adagrad.html#torch.optim.Adagrad" title="torch.optim.Adagrad"><code class="xref py py-class docutils literal notranslate"><span class="pre">Adagrad</span></code></a></p></td>
<td><p>beta</p></td>
<td><p>unsupported</p></td>
<td><p>unsupported</p></td>
</tr>
<tr class="row-odd"><td><p><p id="torch.optim.Adam"/><a class="reference internal" href="generated/torch.optim.Adam.html#torch.optim.Adam" title="torch.optim.Adam"><code class="xref py py-class docutils literal notranslate"><span class="pre">Adam</span></code></a></p></td>
<td><p>beta</p></td>
<td><p>stable</p></td>
<td><p>beta</p></td>
</tr>
<tr class="row-even"><td><p><p id="torch.optim.AdamW"/><a class="reference internal" href="generated/torch.optim.AdamW.html#torch.optim.AdamW" title="torch.optim.AdamW"><code class="xref py py-class docutils literal notranslate"><span class="pre">AdamW</span></code></a></p></td>
<td><p>beta</p></td>
<td><p>stable</p></td>
<td><p>beta</p></td>
</tr>
<tr class="row-odd"><td><p><p id="torch.optim.SparseAdam"/><a class="reference internal" href="generated/torch.optim.SparseAdam.html#torch.optim.SparseAdam" title="torch.optim.SparseAdam"><code class="xref py py-class docutils literal notranslate"><span class="pre">SparseAdam</span></code></a></p></td>
<td><p>unsupported</p></td>
<td><p>unsupported</p></td>
<td><p>unsupported</p></td>
</tr>
<tr class="row-even"><td><p><p id="torch.optim.Adamax"/><a class="reference internal" href="generated/torch.optim.Adamax.html#torch.optim.Adamax" title="torch.optim.Adamax"><code class="xref py py-class docutils literal notranslate"><span class="pre">Adamax</span></code></a></p></td>
<td><p>unsupported</p></td>
<td><p>unsupported</p></td>
<td><p>unsupported</p></td>
</tr>
<tr class="row-odd"><td><p><p id="torch.optim.ASGD"/><a class="reference internal" href="generated/torch.optim.ASGD.html#torch.optim.ASGD" title="torch.optim.ASGD"><code class="xref py py-class docutils literal notranslate"><span class="pre">ASGD</span></code></a></p></td>
<td><p>unsupported</p></td>
<td><p>unsupported</p></td>
<td><p>unsupported</p></td>
</tr>
<tr class="row-even"><td><p><p id="torch.optim.LBFGS"/><a class="reference internal" href="generated/torch.optim.LBFGS.html#torch.optim.LBFGS" title="torch.optim.LBFGS"><code class="xref py py-class docutils literal notranslate"><span class="pre">LBFGS</span></code></a></p></td>
<td><p>unsupported</p></td>
<td><p>unsupported</p></td>
<td><p>unsupported</p></td>
</tr>
<tr class="row-odd"><td><p><p id="torch.optim.NAdam"/><a class="reference internal" href="generated/torch.optim.NAdam.html#torch.optim.NAdam" title="torch.optim.NAdam"><code class="xref py py-class docutils literal notranslate"><span class="pre">NAdam</span></code></a></p></td>
<td><p>unsupported</p></td>
<td><p>unsupported</p></td>
<td><p>unsupported</p></td>
</tr>
<tr class="row-even"><td><p><p id="torch.optim.RAdam"/><a class="reference internal" href="generated/torch.optim.RAdam.html#torch.optim.RAdam" title="torch.optim.RAdam"><code class="xref py py-class docutils literal notranslate"><span class="pre">RAdam</span></code></a></p></td>
<td><p>unsupported</p></td>
<td><p>unsupported</p></td>
<td><p>unsupported</p></td>
</tr>
<tr class="row-odd"><td><p><p id="torch.optim.RMSprop"/><a class="reference internal" href="generated/torch.optim.RMSprop.html#torch.optim.RMSprop" title="torch.optim.RMSprop"><code class="xref py py-class docutils literal notranslate"><span class="pre">RMSprop</span></code></a></p></td>
<td><p>unsupported</p></td>
<td><p>unsupported</p></td>
<td><p>unsupported</p></td>
</tr>
<tr class="row-even"><td><p><p id="torch.optim.Rprop"/><a class="reference internal" href="generated/torch.optim.Rprop.html#torch.optim.Rprop" title="torch.optim.Rprop"><code class="xref py py-class docutils literal notranslate"><span class="pre">Rprop</span></code></a></p></td>
<td><p>unsupported</p></td>
<td><p>unsupported</p></td>
<td><p>unsupported</p></td>
</tr>
<tr class="row-odd"><td><p><p id="torch.optim.SGD"/><a class="reference internal" href="generated/torch.optim.SGD.html#torch.optim.SGD" title="torch.optim.SGD"><code class="xref py py-class docutils literal notranslate"><span class="pre">SGD</span></code></a></p></td>
<td><p>beta</p></td>
<td><p>beta</p></td>
<td><p>beta</p></td>
</tr>
</tbody>
</table>
</div>
<div class="section" id="how-to-adjust-learning-rate">
<h2>How to adjust learning rate<a class="headerlink" href="#how-to-adjust-learning-rate" title="Permalink to this heading">¶</a></h2>
<p><a class="reference internal" href="generated/torch.optim.lr_scheduler.LRScheduler.html#torch.optim.lr_scheduler.LRScheduler" title="torch.optim.lr_scheduler.LRScheduler"><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.optim.lr_scheduler.LRScheduler</span></code></a> provides several methods to adjust the learning
rate based on the number of epochs. <a class="reference internal" href="generated/torch.optim.lr_scheduler.ReduceLROnPlateau.html#torch.optim.lr_scheduler.ReduceLROnPlateau" title="torch.optim.lr_scheduler.ReduceLROnPlateau"><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.optim.lr_scheduler.ReduceLROnPlateau</span></code></a>
allows dynamic learning rate reducing based on some validation measurements.</p>
<p>Learning rate scheduling should be applied after optimizer’s update; e.g., you
should write your code this way:</p>
<p>Example:</p>
<div class="highlight-default notranslate"><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">scheduler</span> <span class="o">=</span> <span class="n">ExponentialLR</span><span class="p">(</span><span class="n">optimizer</span><span class="p">,</span> <span class="n">gamma</span><span class="o">=</span><span class="mf">0.9</span><span class="p">)</span>
<span class="k">for</span> <span class="n">epoch</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">20</span><span class="p">):</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>
<span class="n">scheduler</span><span class="o">.</span><span class="n">step</span><span class="p">()</span>
</pre></div>
</div>
<p>Most learning rate schedulers can be called back-to-back (also referred to as
chaining schedulers). The result is that each scheduler is applied one after the
other on the learning rate obtained by the one preceding it.</p>
<p>Example:</p>
<div class="highlight-default notranslate"><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">scheduler1</span> <span class="o">=</span> <span class="n">ExponentialLR</span><span class="p">(</span><span class="n">optimizer</span><span class="p">,</span> <span class="n">gamma</span><span class="o">=</span><span class="mf">0.9</span><span class="p">)</span>
<span class="n">scheduler2</span> <span class="o">=</span> <span class="n">MultiStepLR</span><span class="p">(</span><span class="n">optimizer</span><span class="p">,</span> <span class="n">milestones</span><span class="o">=</span><span class="p">[</span><span class="mi">30</span><span class="p">,</span><span class="mi">80</span><span class="p">],</span> <span class="n">gamma</span><span class="o">=</span><span class="mf">0.1</span><span class="p">)</span>
<span class="k">for</span> <span class="n">epoch</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">20</span><span class="p">):</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>
<span class="n">scheduler1</span><span class="o">.</span><span class="n">step</span><span class="p">()</span>
<span class="n">scheduler2</span><span class="o">.</span><span class="n">step</span><span class="p">()</span>
</pre></div>
</div>
<p>In many places in the documentation, we will use the following template to refer to schedulers
algorithms.</p>
<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">scheduler</span> <span class="o">=</span> <span class="o">...</span>
<span class="gp">>>> </span><span class="k">for</span> <span class="n">epoch</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">100</span><span class="p">):</span>
<span class="gp">>>> </span> <span class="n">train</span><span class="p">(</span><span class="o">...</span><span class="p">)</span>
<span class="gp">>>> </span> <span class="n">validate</span><span class="p">(</span><span class="o">...</span><span class="p">)</span>
<span class="gp">>>> </span> <span class="n">scheduler</span><span class="o">.</span><span class="n">step</span><span class="p">()</span>
</pre></div>
</div>
<div class="admonition warning">
<p class="admonition-title">Warning</p>
<p>Prior to PyTorch 1.1.0, the learning rate scheduler was expected to be called before
the optimizer’s update; 1.1.0 changed this behavior in a BC-breaking way. If you use
the learning rate scheduler (calling <code class="docutils literal notranslate"><span class="pre">scheduler.step()</span></code>) before the optimizer’s update
(calling <code class="docutils literal notranslate"><span class="pre">optimizer.step()</span></code>), this will skip the first value of the learning rate schedule.
If you are unable to reproduce results after upgrading to PyTorch 1.1.0, please check
if you are calling <code class="docutils literal notranslate"><span class="pre">scheduler.step()</span></code> at the wrong time.</p>
</div>
<table class="autosummary longtable docutils colwidths-auto align-default">
<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/torch.optim.lr_scheduler.LRScheduler.html#torch.optim.lr_scheduler.LRScheduler" title="torch.optim.lr_scheduler.LRScheduler"><code class="xref py py-obj docutils literal notranslate"><span class="pre">lr_scheduler.LRScheduler</span></code></a></p></td>
<td><p>Adjusts the learning rate during optimization.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/torch.optim.lr_scheduler.LambdaLR.html#torch.optim.lr_scheduler.LambdaLR" title="torch.optim.lr_scheduler.LambdaLR"><code class="xref py py-obj docutils literal notranslate"><span class="pre">lr_scheduler.LambdaLR</span></code></a></p></td>
<td><p>Sets the initial learning rate.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/torch.optim.lr_scheduler.MultiplicativeLR.html#torch.optim.lr_scheduler.MultiplicativeLR" title="torch.optim.lr_scheduler.MultiplicativeLR"><code class="xref py py-obj docutils literal notranslate"><span class="pre">lr_scheduler.MultiplicativeLR</span></code></a></p></td>
<td><p>Multiply the learning rate of each parameter group by the factor given in the specified function.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/torch.optim.lr_scheduler.StepLR.html#torch.optim.lr_scheduler.StepLR" title="torch.optim.lr_scheduler.StepLR"><code class="xref py py-obj docutils literal notranslate"><span class="pre">lr_scheduler.StepLR</span></code></a></p></td>
<td><p>Decays the learning rate of each parameter group by gamma every step_size epochs.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/torch.optim.lr_scheduler.MultiStepLR.html#torch.optim.lr_scheduler.MultiStepLR" title="torch.optim.lr_scheduler.MultiStepLR"><code class="xref py py-obj docutils literal notranslate"><span class="pre">lr_scheduler.MultiStepLR</span></code></a></p></td>
<td><p>Decays the learning rate of each parameter group by gamma once the number of epoch reaches one of the milestones.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/torch.optim.lr_scheduler.ConstantLR.html#torch.optim.lr_scheduler.ConstantLR" title="torch.optim.lr_scheduler.ConstantLR"><code class="xref py py-obj docutils literal notranslate"><span class="pre">lr_scheduler.ConstantLR</span></code></a></p></td>
<td><p>Multiply the learning rate of each parameter group by a small constant factor.</p></td>