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v0.5.1
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<li class="toctree-l1"><a class="reference internal" href="engine.html">ignite.engine</a></li>
<li class="toctree-l1 current"><a class="current reference internal" href="#">ignite.handlers</a></li>
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<li class="toctree-l1"><a class="reference internal" href="contrib/handlers.html">ignite.contrib.handlers</a></li>
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<span class="fa fa-book"> Other Versions</span>
v: v0.5.1
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<dt>Tags</dt>
<dd><a href="v0.3.0/handlers.html">v0.3.0</a></dd>
<dd><a href="v0.4.0.post1/handlers.html">v0.4.0.post1</a></dd>
<dd><a href="v0.4.1/handlers.html">v0.4.1</a></dd>
<dd><a href="v0.4.10/handlers.html">v0.4.10</a></dd>
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<dd><a href="v0.4.7/handlers.html">v0.4.7</a></dd>
<dd><a href="v0.4.8/handlers.html">v0.4.8</a></dd>
<dd><a href="v0.4.9/handlers.html">v0.4.9</a></dd>
<dd><a href="v0.4rc.0.post1/handlers.html">v0.4rc.0.post1</a></dd>
<dd><a href="v0.5.0.post2/handlers.html">v0.5.0.post2</a></dd>
<dd><a href="v0.5.1/handlers.html">v0.5.1</a></dd>
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<section id="ignite-handlers">
<h1>ignite.handlers<a class="headerlink" href="#ignite-handlers" title="Permalink to this heading">#</a></h1>
<section id="complete-list-of-generic-handlers">
<h2>Complete list of generic handlers<a class="headerlink" href="#complete-list-of-generic-handlers" title="Permalink to this heading">#</a></h2>
<table class="autosummary longtable docutils align-default">
<colgroup>
<col style="width: 10%" />
<col style="width: 90%" />
</colgroup>
<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/ignite.handlers.checkpoint.Checkpoint.html#ignite.handlers.checkpoint.Checkpoint" title="ignite.handlers.checkpoint.Checkpoint"><code class="xref py py-obj docutils literal notranslate"><span class="pre">checkpoint.Checkpoint</span></code></a></p></td>
<td><p>Checkpoint handler can be used to periodically save and load objects which have attribute <code class="docutils literal notranslate"><span class="pre">state_dict/load_state_dict</span></code>.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/ignite.handlers.DiskSaver.html#ignite.handlers.DiskSaver" title="ignite.handlers.DiskSaver"><code class="xref py py-obj docutils literal notranslate"><span class="pre">DiskSaver</span></code></a></p></td>
<td><p>Handler that saves input checkpoint on a disk.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/ignite.handlers.checkpoint.ModelCheckpoint.html#ignite.handlers.checkpoint.ModelCheckpoint" title="ignite.handlers.checkpoint.ModelCheckpoint"><code class="xref py py-obj docutils literal notranslate"><span class="pre">checkpoint.ModelCheckpoint</span></code></a></p></td>
<td><p>ModelCheckpoint handler, inherits from <a class="reference internal" href="generated/ignite.handlers.checkpoint.Checkpoint.html#ignite.handlers.checkpoint.Checkpoint" title="ignite.handlers.checkpoint.Checkpoint"><code class="xref py py-class docutils literal notranslate"><span class="pre">Checkpoint</span></code></a>, can be used to periodically save objects to disk only.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/ignite.handlers.ema_handler.EMAHandler.html#ignite.handlers.ema_handler.EMAHandler" title="ignite.handlers.ema_handler.EMAHandler"><code class="xref py py-obj docutils literal notranslate"><span class="pre">ema_handler.EMAHandler</span></code></a></p></td>
<td><p>Exponential moving average (EMA) handler can be used to compute a smoothed version of model.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/ignite.handlers.early_stopping.EarlyStopping.html#ignite.handlers.early_stopping.EarlyStopping" title="ignite.handlers.early_stopping.EarlyStopping"><code class="xref py py-obj docutils literal notranslate"><span class="pre">early_stopping.EarlyStopping</span></code></a></p></td>
<td><p>EarlyStopping handler can be used to stop the training if no improvement after a given number of events.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/ignite.handlers.lr_finder.FastaiLRFinder.html#ignite.handlers.lr_finder.FastaiLRFinder" title="ignite.handlers.lr_finder.FastaiLRFinder"><code class="xref py py-obj docutils literal notranslate"><span class="pre">lr_finder.FastaiLRFinder</span></code></a></p></td>
<td><p>Learning rate finder handler for supervised trainers.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/ignite.handlers.terminate_on_nan.TerminateOnNan.html#ignite.handlers.terminate_on_nan.TerminateOnNan" title="ignite.handlers.terminate_on_nan.TerminateOnNan"><code class="xref py py-obj docutils literal notranslate"><span class="pre">terminate_on_nan.TerminateOnNan</span></code></a></p></td>
<td><p>TerminateOnNan handler can be used to stop the training if the <cite>process_function</cite>'s output contains a NaN or infinite number or <cite>torch.tensor</cite>.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/ignite.handlers.TimeLimit.html#ignite.handlers.TimeLimit" title="ignite.handlers.TimeLimit"><code class="xref py py-obj docutils literal notranslate"><span class="pre">TimeLimit</span></code></a></p></td>
<td><p>TimeLimit handler can be used to control training time for computing environments where session time is limited.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/ignite.handlers.time_profilers.BasicTimeProfiler.html#ignite.handlers.time_profilers.BasicTimeProfiler" title="ignite.handlers.time_profilers.BasicTimeProfiler"><code class="xref py py-obj docutils literal notranslate"><span class="pre">time_profilers.BasicTimeProfiler</span></code></a></p></td>
<td><p>BasicTimeProfiler can be used to profile the handlers, events, data loading and data processing times.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/ignite.handlers.time_profilers.HandlersTimeProfiler.html#ignite.handlers.time_profilers.HandlersTimeProfiler" title="ignite.handlers.time_profilers.HandlersTimeProfiler"><code class="xref py py-obj docutils literal notranslate"><span class="pre">time_profilers.HandlersTimeProfiler</span></code></a></p></td>
<td><p>HandlersTimeProfiler can be used to profile the handlers, data loading and data processing times.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/ignite.handlers.timing.Timer.html#ignite.handlers.timing.Timer" title="ignite.handlers.timing.Timer"><code class="xref py py-obj docutils literal notranslate"><span class="pre">timing.Timer</span></code></a></p></td>
<td><p>Timer object can be used to measure (average) time between events.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/ignite.handlers.global_step_from_engine.html#ignite.handlers.global_step_from_engine" title="ignite.handlers.global_step_from_engine"><code class="xref py py-obj docutils literal notranslate"><span class="pre">global_step_from_engine</span></code></a></p></td>
<td><p>Helper method to setup <cite>global_step_transform</cite> function using another engine.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/ignite.handlers.stores.EpochOutputStore.html#ignite.handlers.stores.EpochOutputStore" title="ignite.handlers.stores.EpochOutputStore"><code class="xref py py-obj docutils literal notranslate"><span class="pre">stores.EpochOutputStore</span></code></a></p></td>
<td><p>EpochOutputStore handler to save output prediction and target history after every epoch, could be useful for e.g., visualization purposes.</p></td>
</tr>
</tbody>
</table>
<table class="autosummary longtable docutils align-default">
<colgroup>
<col style="width: 10%" />
<col style="width: 90%" />
</colgroup>
<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/ignite.handlers.checkpoint.BaseSaveHandler.html#ignite.handlers.checkpoint.BaseSaveHandler" title="ignite.handlers.checkpoint.BaseSaveHandler"><code class="xref py py-obj docutils literal notranslate"><span class="pre">checkpoint.BaseSaveHandler</span></code></a></p></td>
<td><p>Base class for save handlers</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/ignite.handlers.param_scheduler.ParamScheduler.html#ignite.handlers.param_scheduler.ParamScheduler" title="ignite.handlers.param_scheduler.ParamScheduler"><code class="xref py py-obj docutils literal notranslate"><span class="pre">param_scheduler.ParamScheduler</span></code></a></p></td>
<td><p>An abstract class for updating an optimizer's parameter value during training.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/ignite.handlers.state_param_scheduler.StateParamScheduler.html#ignite.handlers.state_param_scheduler.StateParamScheduler" title="ignite.handlers.state_param_scheduler.StateParamScheduler"><code class="xref py py-obj docutils literal notranslate"><span class="pre">state_param_scheduler.StateParamScheduler</span></code></a></p></td>
<td><p>An abstract class for updating an engine state parameter values during training.</p></td>
</tr>
</tbody>
</table>
</section>
<section id="loggers">
<h2>Loggers<a class="headerlink" href="#loggers" title="Permalink to this heading">#</a></h2>
<table class="autosummary longtable docutils align-default">
<colgroup>
<col style="width: 10%" />
<col style="width: 90%" />
</colgroup>
<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/ignite.handlers.base_logger.html#module-ignite.handlers.base_logger" title="ignite.handlers.base_logger"><code class="xref py py-obj docutils literal notranslate"><span class="pre">base_logger</span></code></a></p></td>
<td><p>Base logger and its helper handlers.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/ignite.handlers.clearml_logger.html#module-ignite.handlers.clearml_logger" title="ignite.handlers.clearml_logger"><code class="xref py py-obj docutils literal notranslate"><span class="pre">clearml_logger</span></code></a></p></td>
<td><p>ClearML logger and its helper handlers.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/ignite.handlers.mlflow_logger.html#module-ignite.handlers.mlflow_logger" title="ignite.handlers.mlflow_logger"><code class="xref py py-obj docutils literal notranslate"><span class="pre">mlflow_logger</span></code></a></p></td>
<td><p>MLflow logger and its helper handlers.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/ignite.handlers.neptune_logger.html#module-ignite.handlers.neptune_logger" title="ignite.handlers.neptune_logger"><code class="xref py py-obj docutils literal notranslate"><span class="pre">neptune_logger</span></code></a></p></td>
<td><p>Neptune logger and its helper handlers.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/ignite.handlers.polyaxon_logger.html#module-ignite.handlers.polyaxon_logger" title="ignite.handlers.polyaxon_logger"><code class="xref py py-obj docutils literal notranslate"><span class="pre">polyaxon_logger</span></code></a></p></td>
<td><p>Polyaxon logger and its helper handlers.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/ignite.handlers.tensorboard_logger.html#module-ignite.handlers.tensorboard_logger" title="ignite.handlers.tensorboard_logger"><code class="xref py py-obj docutils literal notranslate"><span class="pre">tensorboard_logger</span></code></a></p></td>
<td><p>TensorBoard logger and its helper handlers.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/ignite.handlers.tqdm_logger.html#module-ignite.handlers.tqdm_logger" title="ignite.handlers.tqdm_logger"><code class="xref py py-obj docutils literal notranslate"><span class="pre">tqdm_logger</span></code></a></p></td>
<td><p>TQDM logger.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/ignite.handlers.visdom_logger.html#module-ignite.handlers.visdom_logger" title="ignite.handlers.visdom_logger"><code class="xref py py-obj docutils literal notranslate"><span class="pre">visdom_logger</span></code></a></p></td>
<td><p>Visdom logger and its helper handlers.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/ignite.handlers.wandb_logger.html#module-ignite.handlers.wandb_logger" title="ignite.handlers.wandb_logger"><code class="xref py py-obj docutils literal notranslate"><span class="pre">wandb_logger</span></code></a></p></td>
<td><p>WandB logger and its helper handlers.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/ignite.handlers.fbresearch_logger.html#module-ignite.handlers.fbresearch_logger" title="ignite.handlers.fbresearch_logger"><code class="xref py py-obj docutils literal notranslate"><span class="pre">fbresearch_logger</span></code></a></p></td>
<td><p>FBResearch logger and its helper handlers.</p></td>
</tr>
</tbody>
</table>
<div class="admonition seealso">
<p class="admonition-title">See also</p>
<p>Below are a comprehensive list of examples of various loggers.</p>
<ul class="simple">
<li><p>See <a class="reference external" href="https://github.com/pytorch/ignite/blob/master/examples/mnist/mnist_with_tensorboard_logger.py">tensorboardX mnist example</a>
and <a class="reference external" href="https://github.com/pytorch/ignite/tree/master/examples/notebooks">CycleGAN and EfficientNet notebooks</a> for detailed usage.</p></li>
<li><p>See <a class="reference external" href="https://github.com/pytorch/ignite/blob/master/examples/mnist/mnist_with_visdom_logger.py">visdom mnist example</a> for detailed usage.</p></li>
<li><p>See <a class="reference external" href="https://github.com/pytorch/ignite/blob/master/examples/mnist/mnist_with_neptune_logger.py">neptune mnist example</a> for detailed usage.</p></li>
<li><p>See <a class="reference external" href="https://github.com/pytorch/ignite/blob/master/examples/mnist/mnist_with_tqdm_logger.py">tqdm mnist example</a> for detailed usage.</p></li>
<li><p>See <a class="reference external" href="https://github.com/pytorch/ignite/blob/master/examples/mnist/mnist_with_wandb_logger.py">wandb mnist example</a> for detailed usage.</p></li>
<li><p>See <a class="reference external" href="https://github.com/pytorch/ignite/blob/master/examples/mnist/mnist_with_clearml_logger.py">clearml mnist example</a> for detailed usage.</p></li>
</ul>
</div>
</section>
<section id="parameter-scheduler">
<span id="param-scheduler-label"></span><h2>Parameter scheduler<a class="headerlink" href="#parameter-scheduler" title="Permalink to this heading">#</a></h2>
<table class="autosummary longtable docutils align-default">
<colgroup>
<col style="width: 10%" />
<col style="width: 90%" />
</colgroup>
<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/ignite.handlers.param_scheduler.BaseParamScheduler.html#ignite.handlers.param_scheduler.BaseParamScheduler" title="ignite.handlers.param_scheduler.BaseParamScheduler"><code class="xref py py-obj docutils literal notranslate"><span class="pre">BaseParamScheduler</span></code></a></p></td>
<td><p>An abstract class for updating an engine state or optimizer's parameter value during training.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/ignite.handlers.param_scheduler.ConcatScheduler.html#ignite.handlers.param_scheduler.ConcatScheduler" title="ignite.handlers.param_scheduler.ConcatScheduler"><code class="xref py py-obj docutils literal notranslate"><span class="pre">ConcatScheduler</span></code></a></p></td>
<td><p>Concat a list of parameter schedulers.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/ignite.handlers.param_scheduler.CosineAnnealingScheduler.html#ignite.handlers.param_scheduler.CosineAnnealingScheduler" title="ignite.handlers.param_scheduler.CosineAnnealingScheduler"><code class="xref py py-obj docutils literal notranslate"><span class="pre">CosineAnnealingScheduler</span></code></a></p></td>
<td><p>Anneals 'start_value' to 'end_value' over each cycle.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/ignite.handlers.param_scheduler.CyclicalScheduler.html#ignite.handlers.param_scheduler.CyclicalScheduler" title="ignite.handlers.param_scheduler.CyclicalScheduler"><code class="xref py py-obj docutils literal notranslate"><span class="pre">CyclicalScheduler</span></code></a></p></td>
<td><p>An abstract class for updating an optimizer's parameter value over a cycle of some size.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/ignite.handlers.param_scheduler.LRScheduler.html#ignite.handlers.param_scheduler.LRScheduler" title="ignite.handlers.param_scheduler.LRScheduler"><code class="xref py py-obj docutils literal notranslate"><span class="pre">LRScheduler</span></code></a></p></td>
<td><p>A wrapper class to call <cite>torch.optim.lr_scheduler</cite> objects as <cite>ignite</cite> handlers.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/ignite.handlers.param_scheduler.LinearCyclicalScheduler.html#ignite.handlers.param_scheduler.LinearCyclicalScheduler" title="ignite.handlers.param_scheduler.LinearCyclicalScheduler"><code class="xref py py-obj docutils literal notranslate"><span class="pre">LinearCyclicalScheduler</span></code></a></p></td>
<td><p>Linearly adjusts param value to 'end_value' for a half-cycle, then linearly adjusts it back to 'start_value' for a half-cycle.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/ignite.handlers.param_scheduler.ParamGroupScheduler.html#ignite.handlers.param_scheduler.ParamGroupScheduler" title="ignite.handlers.param_scheduler.ParamGroupScheduler"><code class="xref py py-obj docutils literal notranslate"><span class="pre">ParamGroupScheduler</span></code></a></p></td>
<td><p>Scheduler helper to group multiple schedulers into one.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/ignite.handlers.param_scheduler.ParamScheduler.html#ignite.handlers.param_scheduler.ParamScheduler" title="ignite.handlers.param_scheduler.ParamScheduler"><code class="xref py py-obj docutils literal notranslate"><span class="pre">ParamScheduler</span></code></a></p></td>
<td><p>An abstract class for updating an optimizer's parameter value during training.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/ignite.handlers.param_scheduler.PiecewiseLinear.html#ignite.handlers.param_scheduler.PiecewiseLinear" title="ignite.handlers.param_scheduler.PiecewiseLinear"><code class="xref py py-obj docutils literal notranslate"><span class="pre">PiecewiseLinear</span></code></a></p></td>
<td><p>Piecewise linear parameter scheduler</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/ignite.handlers.param_scheduler.ReduceLROnPlateauScheduler.html#ignite.handlers.param_scheduler.ReduceLROnPlateauScheduler" title="ignite.handlers.param_scheduler.ReduceLROnPlateauScheduler"><code class="xref py py-obj docutils literal notranslate"><span class="pre">ReduceLROnPlateauScheduler</span></code></a></p></td>
<td><p>Reduce LR when a metric stops improving.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/ignite.handlers.param_scheduler.create_lr_scheduler_with_warmup.html#ignite.handlers.param_scheduler.create_lr_scheduler_with_warmup" title="ignite.handlers.param_scheduler.create_lr_scheduler_with_warmup"><code class="xref py py-obj docutils literal notranslate"><span class="pre">create_lr_scheduler_with_warmup</span></code></a></p></td>
<td><p>Helper method to create a learning rate scheduler with a linear warm-up.</p></td>
</tr>
</tbody>
</table>
</section>
<section id="state-parameter-scheduler">
<h2>State Parameter scheduler<a class="headerlink" href="#state-parameter-scheduler" title="Permalink to this heading">#</a></h2>
<table class="autosummary longtable docutils align-default">
<colgroup>
<col style="width: 10%" />
<col style="width: 90%" />
</colgroup>
<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/ignite.handlers.state_param_scheduler.StateParamScheduler.html#ignite.handlers.state_param_scheduler.StateParamScheduler" title="ignite.handlers.state_param_scheduler.StateParamScheduler"><code class="xref py py-obj docutils literal notranslate"><span class="pre">StateParamScheduler</span></code></a></p></td>
<td><p>An abstract class for updating an engine state parameter values during training.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/ignite.handlers.state_param_scheduler.LambdaStateScheduler.html#ignite.handlers.state_param_scheduler.LambdaStateScheduler" title="ignite.handlers.state_param_scheduler.LambdaStateScheduler"><code class="xref py py-obj docutils literal notranslate"><span class="pre">LambdaStateScheduler</span></code></a></p></td>
<td><p>Update a parameter during training by using a user defined callable object.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/ignite.handlers.state_param_scheduler.PiecewiseLinearStateScheduler.html#ignite.handlers.state_param_scheduler.PiecewiseLinearStateScheduler" title="ignite.handlers.state_param_scheduler.PiecewiseLinearStateScheduler"><code class="xref py py-obj docutils literal notranslate"><span class="pre">PiecewiseLinearStateScheduler</span></code></a></p></td>
<td><p>Piecewise linear state parameter scheduler.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/ignite.handlers.state_param_scheduler.ExpStateScheduler.html#ignite.handlers.state_param_scheduler.ExpStateScheduler" title="ignite.handlers.state_param_scheduler.ExpStateScheduler"><code class="xref py py-obj docutils literal notranslate"><span class="pre">ExpStateScheduler</span></code></a></p></td>
<td><p>Update a parameter during training by using exponential function.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/ignite.handlers.state_param_scheduler.StepStateScheduler.html#ignite.handlers.state_param_scheduler.StepStateScheduler" title="ignite.handlers.state_param_scheduler.StepStateScheduler"><code class="xref py py-obj docutils literal notranslate"><span class="pre">StepStateScheduler</span></code></a></p></td>
<td><p>Update a parameter during training by using a step function.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/ignite.handlers.state_param_scheduler.MultiStepStateScheduler.html#ignite.handlers.state_param_scheduler.MultiStepStateScheduler" title="ignite.handlers.state_param_scheduler.MultiStepStateScheduler"><code class="xref py py-obj docutils literal notranslate"><span class="pre">MultiStepStateScheduler</span></code></a></p></td>
<td><p>Update a parameter during training by using a multi step function.</p></td>
</tr>
</tbody>
</table>
</section>
<section id="more-on-parameter-scheduling">
<h2>More on parameter scheduling<a class="headerlink" href="#more-on-parameter-scheduling" title="Permalink to this heading">#</a></h2>
<p>In this section there are visual examples of various parameter schedulings that can be achieved.</p>
<section id="example-with-cosineannealingscheduler">
<h3>Example with <a class="reference internal" href="generated/ignite.handlers.param_scheduler.CosineAnnealingScheduler.html#ignite.handlers.param_scheduler.CosineAnnealingScheduler" title="ignite.handlers.param_scheduler.CosineAnnealingScheduler"><code class="xref py py-class docutils literal notranslate"><span class="pre">CosineAnnealingScheduler</span></code></a><a class="headerlink" href="#example-with-cosineannealingscheduler" title="Permalink to this heading">#</a></h3>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span><span class="w"> </span><span class="nn">numpy</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">np</span>
<span class="kn">import</span><span class="w"> </span><span class="nn">matplotlib.pylab</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">plt</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">ignite.handlers</span><span class="w"> </span><span class="kn">import</span> <span class="n">CosineAnnealingScheduler</span>
<span class="n">lr_values_1</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">CosineAnnealingScheduler</span><span class="o">.</span><span class="n">simulate_values</span><span class="p">(</span><span class="n">num_events</span><span class="o">=</span><span class="mi">75</span><span class="p">,</span> <span class="n">param_name</span><span class="o">=</span><span class="s1">'lr'</span><span class="p">,</span>
<span class="n">start_value</span><span class="o">=</span><span class="mf">1e-1</span><span class="p">,</span> <span class="n">end_value</span><span class="o">=</span><span class="mf">2e-2</span><span class="p">,</span> <span class="n">cycle_size</span><span class="o">=</span><span class="mi">20</span><span class="p">))</span>
<span class="n">lr_values_2</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">CosineAnnealingScheduler</span><span class="o">.</span><span class="n">simulate_values</span><span class="p">(</span><span class="n">num_events</span><span class="o">=</span><span class="mi">75</span><span class="p">,</span> <span class="n">param_name</span><span class="o">=</span><span class="s1">'lr'</span><span class="p">,</span>
<span class="n">start_value</span><span class="o">=</span><span class="mf">1e-1</span><span class="p">,</span> <span class="n">end_value</span><span class="o">=</span><span class="mf">2e-2</span><span class="p">,</span> <span class="n">cycle_size</span><span class="o">=</span><span class="mi">20</span><span class="p">,</span> <span class="n">cycle_mult</span><span class="o">=</span><span class="mf">1.3</span><span class="p">))</span>
<span class="n">lr_values_3</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">CosineAnnealingScheduler</span><span class="o">.</span><span class="n">simulate_values</span><span class="p">(</span><span class="n">num_events</span><span class="o">=</span><span class="mi">75</span><span class="p">,</span> <span class="n">param_name</span><span class="o">=</span><span class="s1">'lr'</span><span class="p">,</span>
<span class="n">start_value</span><span class="o">=</span><span class="mf">1e-1</span><span class="p">,</span> <span class="n">end_value</span><span class="o">=</span><span class="mf">2e-2</span><span class="p">,</span>
<span class="n">cycle_size</span><span class="o">=</span><span class="mi">20</span><span class="p">,</span> <span class="n">start_value_mult</span><span class="o">=</span><span class="mf">0.7</span><span class="p">))</span>
<span class="n">lr_values_4</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">CosineAnnealingScheduler</span><span class="o">.</span><span class="n">simulate_values</span><span class="p">(</span><span class="n">num_events</span><span class="o">=</span><span class="mi">75</span><span class="p">,</span> <span class="n">param_name</span><span class="o">=</span><span class="s1">'lr'</span><span class="p">,</span>
<span class="n">start_value</span><span class="o">=</span><span class="mf">1e-1</span><span class="p">,</span> <span class="n">end_value</span><span class="o">=</span><span class="mf">2e-2</span><span class="p">,</span>
<span class="n">cycle_size</span><span class="o">=</span><span class="mi">20</span><span class="p">,</span> <span class="n">end_value_mult</span><span class="o">=</span><span class="mf">0.1</span><span class="p">))</span>
<span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">25</span><span class="p">,</span> <span class="mi">5</span><span class="p">))</span>
<span class="n">plt</span><span class="o">.</span><span class="n">subplot</span><span class="p">(</span><span class="mi">141</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s2">"Cosine annealing"</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">lr_values_1</span><span class="p">[:,</span> <span class="mi">0</span><span class="p">],</span> <span class="n">lr_values_1</span><span class="p">[:,</span> <span class="mi">1</span><span class="p">],</span> <span class="n">label</span><span class="o">=</span><span class="s2">"learning rate"</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">xlabel</span><span class="p">(</span><span class="s2">"events"</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">ylabel</span><span class="p">(</span><span class="s2">"values"</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">legend</span><span class="p">()</span>
<span class="n">plt</span><span class="o">.</span><span class="n">ylim</span><span class="p">([</span><span class="mf">0.0</span><span class="p">,</span> <span class="mf">0.12</span><span class="p">])</span>
<span class="n">plt</span><span class="o">.</span><span class="n">subplot</span><span class="p">(</span><span class="mi">142</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s2">"Cosine annealing with cycle_mult=1.3"</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">lr_values_2</span><span class="p">[:,</span> <span class="mi">0</span><span class="p">],</span> <span class="n">lr_values_2</span><span class="p">[:,</span> <span class="mi">1</span><span class="p">],</span> <span class="n">label</span><span class="o">=</span><span class="s2">"learning rate"</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">xlabel</span><span class="p">(</span><span class="s2">"events"</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">ylabel</span><span class="p">(</span><span class="s2">"values"</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">legend</span><span class="p">()</span>
<span class="n">plt</span><span class="o">.</span><span class="n">ylim</span><span class="p">([</span><span class="mf">0.0</span><span class="p">,</span> <span class="mf">0.12</span><span class="p">])</span>
<span class="n">plt</span><span class="o">.</span><span class="n">subplot</span><span class="p">(</span><span class="mi">143</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s2">"Cosine annealing with start_value_mult=0.7"</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">lr_values_3</span><span class="p">[:,</span> <span class="mi">0</span><span class="p">],</span> <span class="n">lr_values_3</span><span class="p">[:,</span> <span class="mi">1</span><span class="p">],</span> <span class="n">label</span><span class="o">=</span><span class="s2">"learning rate"</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">xlabel</span><span class="p">(</span><span class="s2">"events"</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">ylabel</span><span class="p">(</span><span class="s2">"values"</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">legend</span><span class="p">()</span>
<span class="n">plt</span><span class="o">.</span><span class="n">ylim</span><span class="p">([</span><span class="mf">0.0</span><span class="p">,</span> <span class="mf">0.12</span><span class="p">])</span>
<span class="n">plt</span><span class="o">.</span><span class="n">subplot</span><span class="p">(</span><span class="mi">144</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s2">"Cosine annealing with end_value_mult=0.1"</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">lr_values_4</span><span class="p">[:,</span> <span class="mi">0</span><span class="p">],</span> <span class="n">lr_values_4</span><span class="p">[:,</span> <span class="mi">1</span><span class="p">],</span> <span class="n">label</span><span class="o">=</span><span class="s2">"learning rate"</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">xlabel</span><span class="p">(</span><span class="s2">"events"</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">ylabel</span><span class="p">(</span><span class="s2">"values"</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">legend</span><span class="p">()</span>
<span class="n">plt</span><span class="o">.</span><span class="n">ylim</span><span class="p">([</span><span class="mf">0.0</span><span class="p">,</span> <span class="mf">0.12</span><span class="p">])</span>
</pre></div>
</div>
<img alt="_images/cosine_annealing_example.png" src="_images/cosine_annealing_example.png" />
</section>
<section id="example-with-ignite-handlers-param-scheduler-linearcyclicalscheduler">
<h3>Example with <a class="reference internal" href="generated/ignite.handlers.param_scheduler.LinearCyclicalScheduler.html#ignite.handlers.param_scheduler.LinearCyclicalScheduler" title="ignite.handlers.param_scheduler.LinearCyclicalScheduler"><code class="xref py py-class docutils literal notranslate"><span class="pre">ignite.handlers.param_scheduler.LinearCyclicalScheduler</span></code></a><a class="headerlink" href="#example-with-ignite-handlers-param-scheduler-linearcyclicalscheduler" title="Permalink to this heading">#</a></h3>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span><span class="w"> </span><span class="nn">numpy</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">np</span>
<span class="kn">import</span><span class="w"> </span><span class="nn">matplotlib.pylab</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">plt</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">ignite.handlers</span><span class="w"> </span><span class="kn">import</span> <span class="n">LinearCyclicalScheduler</span>
<span class="n">lr_values_1</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">LinearCyclicalScheduler</span><span class="o">.</span><span class="n">simulate_values</span><span class="p">(</span><span class="n">num_events</span><span class="o">=</span><span class="mi">75</span><span class="p">,</span> <span class="n">param_name</span><span class="o">=</span><span class="s1">'lr'</span><span class="p">,</span>
<span class="n">start_value</span><span class="o">=</span><span class="mf">1e-1</span><span class="p">,</span> <span class="n">end_value</span><span class="o">=</span><span class="mf">2e-2</span><span class="p">,</span> <span class="n">cycle_size</span><span class="o">=</span><span class="mi">20</span><span class="p">))</span>
<span class="n">lr_values_2</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">LinearCyclicalScheduler</span><span class="o">.</span><span class="n">simulate_values</span><span class="p">(</span><span class="n">num_events</span><span class="o">=</span><span class="mi">75</span><span class="p">,</span> <span class="n">param_name</span><span class="o">=</span><span class="s1">'lr'</span><span class="p">,</span>
<span class="n">start_value</span><span class="o">=</span><span class="mf">1e-1</span><span class="p">,</span> <span class="n">end_value</span><span class="o">=</span><span class="mf">2e-2</span><span class="p">,</span> <span class="n">cycle_size</span><span class="o">=</span><span class="mi">20</span><span class="p">,</span> <span class="n">cycle_mult</span><span class="o">=</span><span class="mf">1.3</span><span class="p">))</span>
<span class="n">lr_values_3</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">LinearCyclicalScheduler</span><span class="o">.</span><span class="n">simulate_values</span><span class="p">(</span><span class="n">num_events</span><span class="o">=</span><span class="mi">75</span><span class="p">,</span> <span class="n">param_name</span><span class="o">=</span><span class="s1">'lr'</span><span class="p">,</span>
<span class="n">start_value</span><span class="o">=</span><span class="mf">1e-1</span><span class="p">,</span> <span class="n">end_value</span><span class="o">=</span><span class="mf">2e-2</span><span class="p">,</span>
<span class="n">cycle_size</span><span class="o">=</span><span class="mi">20</span><span class="p">,</span> <span class="n">start_value_mult</span><span class="o">=</span><span class="mf">0.7</span><span class="p">))</span>
<span class="n">lr_values_4</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">LinearCyclicalScheduler</span><span class="o">.</span><span class="n">simulate_values</span><span class="p">(</span><span class="n">num_events</span><span class="o">=</span><span class="mi">75</span><span class="p">,</span> <span class="n">param_name</span><span class="o">=</span><span class="s1">'lr'</span><span class="p">,</span>
<span class="n">start_value</span><span class="o">=</span><span class="mf">1e-1</span><span class="p">,</span> <span class="n">end_value</span><span class="o">=</span><span class="mf">2e-2</span><span class="p">,</span>
<span class="n">cycle_size</span><span class="o">=</span><span class="mi">20</span><span class="p">,</span> <span class="n">end_value_mult</span><span class="o">=</span><span class="mf">0.1</span><span class="p">))</span>
<span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">25</span><span class="p">,</span> <span class="mi">5</span><span class="p">))</span>
<span class="n">plt</span><span class="o">.</span><span class="n">subplot</span><span class="p">(</span><span class="mi">141</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s2">"Linear cyclical scheduler"</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">lr_values_1</span><span class="p">[:,</span> <span class="mi">0</span><span class="p">],</span> <span class="n">lr_values_1</span><span class="p">[:,</span> <span class="mi">1</span><span class="p">],</span> <span class="n">label</span><span class="o">=</span><span class="s2">"learning rate"</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">xlabel</span><span class="p">(</span><span class="s2">"events"</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">ylabel</span><span class="p">(</span><span class="s2">"values"</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">legend</span><span class="p">()</span>
<span class="n">plt</span><span class="o">.</span><span class="n">ylim</span><span class="p">([</span><span class="mf">0.0</span><span class="p">,</span> <span class="mf">0.12</span><span class="p">])</span>
<span class="n">plt</span><span class="o">.</span><span class="n">subplot</span><span class="p">(</span><span class="mi">142</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s2">"Linear cyclical scheduler with cycle_mult=1.3"</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">lr_values_2</span><span class="p">[:,</span> <span class="mi">0</span><span class="p">],</span> <span class="n">lr_values_2</span><span class="p">[:,</span> <span class="mi">1</span><span class="p">],</span> <span class="n">label</span><span class="o">=</span><span class="s2">"learning rate"</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">xlabel</span><span class="p">(</span><span class="s2">"events"</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">ylabel</span><span class="p">(</span><span class="s2">"values"</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">legend</span><span class="p">()</span>
<span class="n">plt</span><span class="o">.</span><span class="n">ylim</span><span class="p">([</span><span class="mf">0.0</span><span class="p">,</span> <span class="mf">0.12</span><span class="p">])</span>
<span class="n">plt</span><span class="o">.</span><span class="n">subplot</span><span class="p">(</span><span class="mi">143</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s2">"Linear cyclical scheduler with start_value_mult=0.7"</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">lr_values_3</span><span class="p">[:,</span> <span class="mi">0</span><span class="p">],</span> <span class="n">lr_values_3</span><span class="p">[:,</span> <span class="mi">1</span><span class="p">],</span> <span class="n">label</span><span class="o">=</span><span class="s2">"learning rate"</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">xlabel</span><span class="p">(</span><span class="s2">"events"</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">ylabel</span><span class="p">(</span><span class="s2">"values"</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">legend</span><span class="p">()</span>
<span class="n">plt</span><span class="o">.</span><span class="n">ylim</span><span class="p">([</span><span class="mf">0.0</span><span class="p">,</span> <span class="mf">0.12</span><span class="p">])</span>
<span class="n">plt</span><span class="o">.</span><span class="n">subplot</span><span class="p">(</span><span class="mi">144</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s2">"Linear cyclical scheduler with end_value_mult=0.1"</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">lr_values_4</span><span class="p">[:,</span> <span class="mi">0</span><span class="p">],</span> <span class="n">lr_values_4</span><span class="p">[:,</span> <span class="mi">1</span><span class="p">],</span> <span class="n">label</span><span class="o">=</span><span class="s2">"learning rate"</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">xlabel</span><span class="p">(</span><span class="s2">"events"</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">ylabel</span><span class="p">(</span><span class="s2">"values"</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">legend</span><span class="p">()</span>
<span class="n">plt</span><span class="o">.</span><span class="n">ylim</span><span class="p">([</span><span class="mf">0.0</span><span class="p">,</span> <span class="mf">0.12</span><span class="p">])</span>
</pre></div>
</div>
<img alt="_images/linear_cyclical_example.png" src="_images/linear_cyclical_example.png" />
</section>
<section id="example-with-ignite-handlers-param-scheduler-concatscheduler">
<h3>Example with <a class="reference internal" href="generated/ignite.handlers.param_scheduler.ConcatScheduler.html#ignite.handlers.param_scheduler.ConcatScheduler" title="ignite.handlers.param_scheduler.ConcatScheduler"><code class="xref py py-class docutils literal notranslate"><span class="pre">ignite.handlers.param_scheduler.ConcatScheduler</span></code></a><a class="headerlink" href="#example-with-ignite-handlers-param-scheduler-concatscheduler" title="Permalink to this heading">#</a></h3>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span><span class="w"> </span><span class="nn">numpy</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">np</span>
<span class="kn">import</span><span class="w"> </span><span class="nn">matplotlib.pylab</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">plt</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">ignite.handlers</span><span class="w"> </span><span class="kn">import</span> <span class="n">LinearCyclicalScheduler</span><span class="p">,</span> <span class="n">CosineAnnealingScheduler</span><span class="p">,</span> <span class="n">ConcatScheduler</span>
<span class="kn">import</span><span class="w"> </span><span class="nn">torch</span>
<span class="n">t1</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">zeros</span><span class="p">([</span><span class="mi">1</span><span class="p">],</span> <span class="n">requires_grad</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
<span class="n">optimizer</span> <span class="o">=</span> <span class="n">torch</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">t1</span><span class="p">],</span> <span class="n">lr</span><span class="o">=</span><span class="mf">0.1</span><span class="p">)</span>
<span class="n">scheduler_1</span> <span class="o">=</span> <span class="n">LinearCyclicalScheduler</span><span class="p">(</span><span class="n">optimizer</span><span class="p">,</span> <span class="s2">"lr"</span><span class="p">,</span> <span class="n">start_value</span><span class="o">=</span><span class="mf">0.1</span><span class="p">,</span> <span class="n">end_value</span><span class="o">=</span><span class="mf">0.5</span><span class="p">,</span> <span class="n">cycle_size</span><span class="o">=</span><span class="mi">30</span><span class="p">)</span>
<span class="n">scheduler_2</span> <span class="o">=</span> <span class="n">CosineAnnealingScheduler</span><span class="p">(</span><span class="n">optimizer</span><span class="p">,</span> <span class="s2">"lr"</span><span class="p">,</span> <span class="n">start_value</span><span class="o">=</span><span class="mf">0.5</span><span class="p">,</span> <span class="n">end_value</span><span class="o">=</span><span class="mf">0.01</span><span class="p">,</span> <span class="n">cycle_size</span><span class="o">=</span><span class="mi">50</span><span class="p">)</span>
<span class="n">durations</span> <span class="o">=</span> <span class="p">[</span><span class="mi">15</span><span class="p">,</span> <span class="p">]</span>
<span class="n">lr_values_1</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">ConcatScheduler</span><span class="o">.</span><span class="n">simulate_values</span><span class="p">(</span><span class="n">num_events</span><span class="o">=</span><span class="mi">100</span><span class="p">,</span> <span class="n">schedulers</span><span class="o">=</span><span class="p">[</span><span class="n">scheduler_1</span><span class="p">,</span> <span class="n">scheduler_2</span><span class="p">],</span> <span class="n">durations</span><span class="o">=</span><span class="n">durations</span><span class="p">))</span>
<span class="n">t1</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">zeros</span><span class="p">([</span><span class="mi">1</span><span class="p">],</span> <span class="n">requires_grad</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
<span class="n">optimizer</span> <span class="o">=</span> <span class="n">torch</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">t1</span><span class="p">],</span> <span class="n">lr</span><span class="o">=</span><span class="mf">0.1</span><span class="p">)</span>
<span class="n">scheduler_1</span> <span class="o">=</span> <span class="n">LinearCyclicalScheduler</span><span class="p">(</span><span class="n">optimizer</span><span class="p">,</span> <span class="s2">"lr"</span><span class="p">,</span> <span class="n">start_value</span><span class="o">=</span><span class="mf">0.1</span><span class="p">,</span> <span class="n">end_value</span><span class="o">=</span><span class="mf">0.5</span><span class="p">,</span> <span class="n">cycle_size</span><span class="o">=</span><span class="mi">30</span><span class="p">)</span>
<span class="n">scheduler_2</span> <span class="o">=</span> <span class="n">CosineAnnealingScheduler</span><span class="p">(</span><span class="n">optimizer</span><span class="p">,</span> <span class="s2">"momentum"</span><span class="p">,</span> <span class="n">start_value</span><span class="o">=</span><span class="mf">0.5</span><span class="p">,</span> <span class="n">end_value</span><span class="o">=</span><span class="mf">0.01</span><span class="p">,</span> <span class="n">cycle_size</span><span class="o">=</span><span class="mi">50</span><span class="p">)</span>
<span class="n">durations</span> <span class="o">=</span> <span class="p">[</span><span class="mi">15</span><span class="p">,</span> <span class="p">]</span>
<span class="n">lr_values_2</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">ConcatScheduler</span><span class="o">.</span><span class="n">simulate_values</span><span class="p">(</span><span class="n">num_events</span><span class="o">=</span><span class="mi">100</span><span class="p">,</span> <span class="n">schedulers</span><span class="o">=</span><span class="p">[</span><span class="n">scheduler_1</span><span class="p">,</span> <span class="n">scheduler_2</span><span class="p">],</span> <span class="n">durations</span><span class="o">=</span><span class="n">durations</span><span class="p">,</span>
<span class="n">param_names</span><span class="o">=</span><span class="p">[</span><span class="s2">"lr"</span><span class="p">,</span> <span class="s2">"momentum"</span><span class="p">]))</span>
<span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">25</span><span class="p">,</span> <span class="mi">5</span><span class="p">))</span>
<span class="n">plt</span><span class="o">.</span><span class="n">subplot</span><span class="p">(</span><span class="mi">131</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s2">"Concat scheduler of linear + cosine annealing"</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">lr_values_1</span><span class="p">[:,</span> <span class="mi">0</span><span class="p">],</span> <span class="n">lr_values_1</span><span class="p">[:,</span> <span class="mi">1</span><span class="p">],</span> <span class="n">label</span><span class="o">=</span><span class="s2">"learning rate"</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">xlabel</span><span class="p">(</span><span class="s2">"events"</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">ylabel</span><span class="p">(</span><span class="s2">"values"</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">legend</span><span class="p">()</span>
<span class="n">plt</span><span class="o">.</span><span class="n">subplot</span><span class="p">(</span><span class="mi">132</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s2">"Concat scheduler of linear LR scheduler</span><span class="se">\n</span><span class="s2"> and cosine annealing on momentum"</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">lr_values_2</span><span class="p">[:,</span> <span class="mi">0</span><span class="p">],</span> <span class="n">lr_values_2</span><span class="p">[:,</span> <span class="mi">1</span><span class="p">],</span> <span class="n">label</span><span class="o">=</span><span class="s2">"learning rate"</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">xlabel</span><span class="p">(</span><span class="s2">"events"</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">ylabel</span><span class="p">(</span><span class="s2">"values"</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">legend</span><span class="p">()</span>
<span class="n">plt</span><span class="o">.</span><span class="n">subplot</span><span class="p">(</span><span class="mi">133</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s2">"Concat scheduler of linear LR scheduler</span><span class="se">\n</span><span class="s2"> and cosine annealing on momentum"</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">lr_values_2</span><span class="p">[:,</span> <span class="mi">0</span><span class="p">],</span> <span class="n">lr_values_2</span><span class="p">[:,</span> <span class="mi">2</span><span class="p">],</span> <span class="n">label</span><span class="o">=</span><span class="s2">"momentum"</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">xlabel</span><span class="p">(</span><span class="s2">"events"</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">ylabel</span><span class="p">(</span><span class="s2">"values"</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">legend</span><span class="p">()</span>
</pre></div>
</div>
<img alt="_images/concat_example.png" src="_images/concat_example.png" />
<section id="piecewise-linear-scheduler">
<h4>Piecewise linear scheduler<a class="headerlink" href="#piecewise-linear-scheduler" title="Permalink to this heading">#</a></h4>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span><span class="w"> </span><span class="nn">numpy</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">np</span>
<span class="kn">import</span><span class="w"> </span><span class="nn">matplotlib.pylab</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">plt</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">ignite.handlers</span><span class="w"> </span><span class="kn">import</span> <span class="n">LinearCyclicalScheduler</span><span class="p">,</span> <span class="n">ConcatScheduler</span>
<span class="n">scheduler_1</span> <span class="o">=</span> <span class="n">LinearCyclicalScheduler</span><span class="p">(</span><span class="n">optimizer</span><span class="p">,</span> <span class="s2">"lr"</span><span class="p">,</span> <span class="n">start_value</span><span class="o">=</span><span class="mf">0.0</span><span class="p">,</span> <span class="n">end_value</span><span class="o">=</span><span class="mf">0.6</span><span class="p">,</span> <span class="n">cycle_size</span><span class="o">=</span><span class="mi">50</span><span class="p">)</span>
<span class="n">scheduler_2</span> <span class="o">=</span> <span class="n">LinearCyclicalScheduler</span><span class="p">(</span><span class="n">optimizer</span><span class="p">,</span> <span class="s2">"lr"</span><span class="p">,</span> <span class="n">start_value</span><span class="o">=</span><span class="mf">0.6</span><span class="p">,</span> <span class="n">end_value</span><span class="o">=</span><span class="mf">0.0</span><span class="p">,</span> <span class="n">cycle_size</span><span class="o">=</span><span class="mi">150</span><span class="p">)</span>
<span class="n">durations</span> <span class="o">=</span> <span class="p">[</span><span class="mi">25</span><span class="p">,</span> <span class="p">]</span>
<span class="n">lr_values</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">ConcatScheduler</span><span class="o">.</span><span class="n">simulate_values</span><span class="p">(</span><span class="n">num_events</span><span class="o">=</span><span class="mi">100</span><span class="p">,</span> <span class="n">schedulers</span><span class="o">=</span><span class="p">[</span><span class="n">scheduler_1</span><span class="p">,</span> <span class="n">scheduler_2</span><span class="p">],</span> <span class="n">durations</span><span class="o">=</span><span class="n">durations</span><span class="p">))</span>
<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s2">"Piecewise linear scheduler"</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">lr_values</span><span class="p">[:,</span> <span class="mi">0</span><span class="p">],</span> <span class="n">lr_values</span><span class="p">[:,</span> <span class="mi">1</span><span class="p">],</span> <span class="n">label</span><span class="o">=</span><span class="s2">"learning rate"</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">xlabel</span><span class="p">(</span><span class="s2">"events"</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">ylabel</span><span class="p">(</span><span class="s2">"values"</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">legend</span><span class="p">()</span>
</pre></div>
</div>
<img alt="_images/piecewise_linear.png" src="_images/piecewise_linear.png" />
</section>
</section>
<section id="example-with-ignite-handlers-param-scheduler-lrscheduler">
<h3>Example with <a class="reference internal" href="generated/ignite.handlers.param_scheduler.LRScheduler.html#ignite.handlers.param_scheduler.LRScheduler" title="ignite.handlers.param_scheduler.LRScheduler"><code class="xref py py-class docutils literal notranslate"><span class="pre">ignite.handlers.param_scheduler.LRScheduler</span></code></a><a class="headerlink" href="#example-with-ignite-handlers-param-scheduler-lrscheduler" title="Permalink to this heading">#</a></h3>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span><span class="w"> </span><span class="nn">numpy</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">np</span>
<span class="kn">import</span><span class="w"> </span><span class="nn">matplotlib.pylab</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">plt</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">ignite.handlers</span><span class="w"> </span><span class="kn">import</span> <span class="n">LRScheduler</span>
<span class="kn">import</span><span class="w"> </span><span class="nn">torch</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">torch.optim.lr_scheduler</span><span class="w"> </span><span class="kn">import</span> <span class="n">ExponentialLR</span><span class="p">,</span> <span class="n">StepLR</span><span class="p">,</span> <span class="n">CosineAnnealingLR</span>
<span class="n">tensor</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">zeros</span><span class="p">([</span><span class="mi">1</span><span class="p">],</span> <span class="n">requires_grad</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
<span class="n">optimizer</span> <span class="o">=</span> <span class="n">torch</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">tensor</span><span class="p">],</span> <span class="n">lr</span><span class="o">=</span><span class="mf">0.1</span><span class="p">)</span>
<span class="n">lr_scheduler_1</span> <span class="o">=</span> <span class="n">StepLR</span><span class="p">(</span><span class="n">optimizer</span><span class="o">=</span><span class="n">optimizer</span><span class="p">,</span> <span class="n">step_size</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span> <span class="n">gamma</span><span class="o">=</span><span class="mf">0.77</span><span class="p">)</span>
<span class="n">lr_scheduler_2</span> <span class="o">=</span> <span class="n">ExponentialLR</span><span class="p">(</span><span class="n">optimizer</span><span class="o">=</span><span class="n">optimizer</span><span class="p">,</span> <span class="n">gamma</span><span class="o">=</span><span class="mf">0.98</span><span class="p">)</span>
<span class="n">lr_scheduler_3</span> <span class="o">=</span> <span class="n">CosineAnnealingLR</span><span class="p">(</span><span class="n">optimizer</span><span class="o">=</span><span class="n">optimizer</span><span class="p">,</span> <span class="n">T_max</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span> <span class="n">eta_min</span><span class="o">=</span><span class="mf">0.01</span><span class="p">)</span>
<span class="n">lr_values_1</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">LRScheduler</span><span class="o">.</span><span class="n">simulate_values</span><span class="p">(</span><span class="n">num_events</span><span class="o">=</span><span class="mi">100</span><span class="p">,</span> <span class="n">lr_scheduler</span><span class="o">=</span><span class="n">lr_scheduler_1</span><span class="p">))</span>
<span class="n">lr_values_2</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">LRScheduler</span><span class="o">.</span><span class="n">simulate_values</span><span class="p">(</span><span class="n">num_events</span><span class="o">=</span><span class="mi">100</span><span class="p">,</span> <span class="n">lr_scheduler</span><span class="o">=</span><span class="n">lr_scheduler_2</span><span class="p">))</span>
<span class="n">lr_values_3</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">LRScheduler</span><span class="o">.</span><span class="n">simulate_values</span><span class="p">(</span><span class="n">num_events</span><span class="o">=</span><span class="mi">100</span><span class="p">,</span> <span class="n">lr_scheduler</span><span class="o">=</span><span class="n">lr_scheduler_3</span><span class="p">))</span>
<span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">25</span><span class="p">,</span> <span class="mi">5</span><span class="p">))</span>
<span class="n">plt</span><span class="o">.</span><span class="n">subplot</span><span class="p">(</span><span class="mi">131</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s2">"Torch LR scheduler wrapping StepLR"</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">lr_values_1</span><span class="p">[:,</span> <span class="mi">0</span><span class="p">],</span> <span class="n">lr_values_1</span><span class="p">[:,</span> <span class="mi">1</span><span class="p">],</span> <span class="n">label</span><span class="o">=</span><span class="s2">"learning rate"</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">xlabel</span><span class="p">(</span><span class="s2">"events"</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">ylabel</span><span class="p">(</span><span class="s2">"values"</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">legend</span><span class="p">()</span>
<span class="n">plt</span><span class="o">.</span><span class="n">subplot</span><span class="p">(</span><span class="mi">132</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s2">"Torch LR scheduler wrapping ExponentialLR"</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">lr_values_2</span><span class="p">[:,</span> <span class="mi">0</span><span class="p">],</span> <span class="n">lr_values_2</span><span class="p">[:,</span> <span class="mi">1</span><span class="p">],</span> <span class="n">label</span><span class="o">=</span><span class="s2">"learning rate"</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">xlabel</span><span class="p">(</span><span class="s2">"events"</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">ylabel</span><span class="p">(</span><span class="s2">"values"</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">legend</span><span class="p">()</span>
<span class="n">plt</span><span class="o">.</span><span class="n">subplot</span><span class="p">(</span><span class="mi">133</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s2">"Torch LR scheduler wrapping CosineAnnealingLR"</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">lr_values_3</span><span class="p">[:,</span> <span class="mi">0</span><span class="p">],</span> <span class="n">lr_values_3</span><span class="p">[:,</span> <span class="mi">1</span><span class="p">],</span> <span class="n">label</span><span class="o">=</span><span class="s2">"learning rate"</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">xlabel</span><span class="p">(</span><span class="s2">"events"</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">ylabel</span><span class="p">(</span><span class="s2">"values"</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">legend</span><span class="p">()</span>
</pre></div>
</div>
<img alt="_images/lr_scheduler.png" src="_images/lr_scheduler.png" />
<section id="concatenate-with-torch-schedulers">
<h4>Concatenate with torch schedulers<a class="headerlink" href="#concatenate-with-torch-schedulers" title="Permalink to this heading">#</a></h4>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span><span class="w"> </span><span class="nn">numpy</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">np</span>
<span class="kn">import</span><span class="w"> </span><span class="nn">matplotlib.pylab</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">plt</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">ignite.handlers</span><span class="w"> </span><span class="kn">import</span> <span class="n">LRScheduler</span><span class="p">,</span> <span class="n">ConcatScheduler</span>
<span class="kn">import</span><span class="w"> </span><span class="nn">torch</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">torch.optim.lr_scheduler</span><span class="w"> </span><span class="kn">import</span> <span class="n">ExponentialLR</span><span class="p">,</span> <span class="n">StepLR</span>
<span class="n">t1</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">zeros</span><span class="p">([</span><span class="mi">1</span><span class="p">],</span> <span class="n">requires_grad</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
<span class="n">optimizer</span> <span class="o">=</span> <span class="n">torch</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">t1</span><span class="p">],</span> <span class="n">lr</span><span class="o">=</span><span class="mf">0.1</span><span class="p">)</span>
<span class="n">scheduler_1</span> <span class="o">=</span> <span class="n">LinearCyclicalScheduler</span><span class="p">(</span><span class="n">optimizer</span><span class="p">,</span> <span class="s2">"lr"</span><span class="p">,</span> <span class="n">start_value</span><span class="o">=</span><span class="mf">0.001</span><span class="p">,</span> <span class="n">end_value</span><span class="o">=</span><span class="mf">0.1</span><span class="p">,</span> <span class="n">cycle_size</span><span class="o">=</span><span class="mi">30</span><span class="p">)</span>
<span class="n">lr_scheduler</span> <span class="o">=</span> <span class="n">ExponentialLR</span><span class="p">(</span><span class="n">optimizer</span><span class="o">=</span><span class="n">optimizer</span><span class="p">,</span> <span class="n">gamma</span><span class="o">=</span><span class="mf">0.7</span><span class="p">)</span>
<span class="n">scheduler_2</span> <span class="o">=</span> <span class="n">LRScheduler</span><span class="p">(</span><span class="n">lr_scheduler</span><span class="o">=</span><span class="n">lr_scheduler</span><span class="p">)</span>
<span class="n">durations</span> <span class="o">=</span> <span class="p">[</span><span class="mi">15</span><span class="p">,</span> <span class="p">]</span>
<span class="n">lr_values_1</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">ConcatScheduler</span><span class="o">.</span><span class="n">simulate_values</span><span class="p">(</span><span class="n">num_events</span><span class="o">=</span><span class="mi">30</span><span class="p">,</span> <span class="n">schedulers</span><span class="o">=</span><span class="p">[</span><span class="n">scheduler_1</span><span class="p">,</span> <span class="n">scheduler_2</span><span class="p">],</span> <span class="n">durations</span><span class="o">=</span><span class="n">durations</span><span class="p">))</span>
<span class="n">scheduler_1</span> <span class="o">=</span> <span class="n">LinearCyclicalScheduler</span><span class="p">(</span><span class="n">optimizer</span><span class="p">,</span> <span class="s2">"lr"</span><span class="p">,</span> <span class="n">start_value</span><span class="o">=</span><span class="mf">0.001</span><span class="p">,</span> <span class="n">end_value</span><span class="o">=</span><span class="mf">0.1</span><span class="p">,</span> <span class="n">cycle_size</span><span class="o">=</span><span class="mi">30</span><span class="p">)</span>
<span class="n">lr_scheduler</span> <span class="o">=</span> <span class="n">StepLR</span><span class="p">(</span><span class="n">optimizer</span><span class="o">=</span><span class="n">optimizer</span><span class="p">,</span> <span class="n">step_size</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span> <span class="n">gamma</span><span class="o">=</span><span class="mf">0.7</span><span class="p">)</span>
<span class="n">scheduler_2</span> <span class="o">=</span> <span class="n">LRScheduler</span><span class="p">(</span><span class="n">lr_scheduler</span><span class="o">=</span><span class="n">lr_scheduler</span><span class="p">)</span>
<span class="n">durations</span> <span class="o">=</span> <span class="p">[</span><span class="mi">15</span><span class="p">,</span> <span class="p">]</span>
<span class="n">lr_values_2</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">ConcatScheduler</span><span class="o">.</span><span class="n">simulate_values</span><span class="p">(</span><span class="n">num_events</span><span class="o">=</span><span class="mi">75</span><span class="p">,</span> <span class="n">schedulers</span><span class="o">=</span><span class="p">[</span><span class="n">scheduler_1</span><span class="p">,</span> <span class="n">scheduler_2</span><span class="p">],</span> <span class="n">durations</span><span class="o">=</span><span class="n">durations</span><span class="p">))</span>
<span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">15</span><span class="p">,</span> <span class="mi">5</span><span class="p">))</span>
<span class="n">plt</span><span class="o">.</span><span class="n">subplot</span><span class="p">(</span><span class="mi">121</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s2">"Concat scheduler of linear + ExponentialLR"</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">lr_values_1</span><span class="p">[:,</span> <span class="mi">0</span><span class="p">],</span> <span class="n">lr_values_1</span><span class="p">[:,</span> <span class="mi">1</span><span class="p">],</span> <span class="n">label</span><span class="o">=</span><span class="s2">"learning rate"</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">xlabel</span><span class="p">(</span><span class="s2">"events"</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">ylabel</span><span class="p">(</span><span class="s2">"values"</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">legend</span><span class="p">()</span>
<span class="n">plt</span><span class="o">.</span><span class="n">subplot</span><span class="p">(</span><span class="mi">122</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s2">"Concat scheduler of linear + StepLR"</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">lr_values_2</span><span class="p">[:,</span> <span class="mi">0</span><span class="p">],</span> <span class="n">lr_values_2</span><span class="p">[:,</span> <span class="mi">1</span><span class="p">],</span> <span class="n">label</span><span class="o">=</span><span class="s2">"learning rate"</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">xlabel</span><span class="p">(</span><span class="s2">"events"</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">ylabel</span><span class="p">(</span><span class="s2">"values"</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">legend</span><span class="p">()</span>
</pre></div>
</div>
<img alt="_images/concat_linear_exp_step_lr.png" src="_images/concat_linear_exp_step_lr.png" />
</section>
</section>
<section id="example-with-ignite-handlers-param-scheduler-reducelronplateauscheduler">
<h3>Example with <a class="reference internal" href="generated/ignite.handlers.param_scheduler.ReduceLROnPlateauScheduler.html#ignite.handlers.param_scheduler.ReduceLROnPlateauScheduler" title="ignite.handlers.param_scheduler.ReduceLROnPlateauScheduler"><code class="xref py py-class docutils literal notranslate"><span class="pre">ignite.handlers.param_scheduler.ReduceLROnPlateauScheduler</span></code></a><a class="headerlink" href="#example-with-ignite-handlers-param-scheduler-reducelronplateauscheduler" title="Permalink to this heading">#</a></h3>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span><span class="w"> </span><span class="nn">matplotlib.pyplot</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">plt</span>
<span class="kn">import</span><span class="w"> </span><span class="nn">numpy</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">np</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">ignite.handlers</span><span class="w"> </span><span class="kn">import</span> <span class="n">ReduceLROnPlateauScheduler</span>
<span class="n">metric_values</span> <span class="o">=</span> <span class="p">[</span><span class="mf">0.7</span><span class="p">,</span> <span class="mf">0.78</span><span class="p">,</span> <span class="mf">0.81</span><span class="p">,</span> <span class="mf">0.82</span><span class="p">,</span> <span class="mf">0.82</span><span class="p">,</span> <span class="mf">0.83</span><span class="p">,</span> <span class="mf">0.80</span><span class="p">,</span> <span class="mf">0.81</span><span class="p">,</span> <span class="mf">0.84</span><span class="p">,</span> <span class="mf">0.78</span><span class="p">]</span>
<span class="n">num_events</span> <span class="o">=</span> <span class="mi">10</span>
<span class="n">init_lr</span> <span class="o">=</span> <span class="mf">0.1</span>
<span class="n">lr_values</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">ReduceLROnPlateauScheduler</span><span class="o">.</span><span class="n">simulate_values</span><span class="p">(</span>
<span class="n">num_events</span><span class="p">,</span> <span class="n">metric_values</span><span class="p">,</span> <span class="n">init_lr</span><span class="p">,</span>
<span class="n">factor</span><span class="o">=</span><span class="mf">0.5</span><span class="p">,</span> <span class="n">patience</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">mode</span><span class="o">=</span><span class="s1">'max'</span><span class="p">,</span> <span class="n">threshold</span><span class="o">=</span><span class="mf">0.01</span><span class="p">,</span> <span class="n">threshold_mode</span><span class="o">=</span><span class="s1">'abs'</span>
<span class="p">)</span>
<span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">15</span><span class="p">,</span> <span class="mi">5</span><span class="p">))</span>
<span class="n">plt</span><span class="o">.</span><span class="n">suptitle</span><span class="p">(</span><span class="s2">"ReduceLROnPlateauScheduler"</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">subplot</span><span class="p">(</span><span class="mi">121</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">lr_values</span><span class="p">[:,</span> <span class="mi">1</span><span class="p">],</span> <span class="n">label</span><span class="o">=</span><span class="s2">"learning rate"</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">xticks</span><span class="p">(</span><span class="n">lr_values</span><span class="p">[:,</span> <span class="mi">0</span><span class="p">])</span>
<span class="n">plt</span><span class="o">.</span><span class="n">xlabel</span><span class="p">(</span><span class="s2">"events"</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">ylabel</span><span class="p">(</span><span class="s2">"values"</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">legend</span><span class="p">()</span>
<span class="n">plt</span><span class="o">.</span><span class="n">subplot</span><span class="p">(</span><span class="mi">122</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">metric_values</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s2">"metric"</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">xticks</span><span class="p">(</span><span class="n">lr_values</span><span class="p">[:,</span> <span class="mi">0</span><span class="p">])</span>
<span class="n">plt</span><span class="o">.</span><span class="n">xlabel</span><span class="p">(</span><span class="s2">"events"</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">ylabel</span><span class="p">(</span><span class="s2">"values"</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">legend</span><span class="p">()</span>
</pre></div>
</div>
<img alt="_images/reduce_lr_on_plateau_example.png" src="_images/reduce_lr_on_plateau_example.png" />
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