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William Falcon
2019-10-05 16:10:29 -05:00
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Trainer/Training Loop/index.html
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<main class="md-main">
<main class="md-main" role="main">
<div class="md-main__inner md-grid" data-md-component="container">
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<ul class="md-nav__list" data-md-scrollfix>
<li class="md-nav__item">
<a href="#accumulated-gradients" title="Accumulated gradients" class="md-nav__link">
<a href="#accumulated-gradients" class="md-nav__link">
Accumulated gradients
</a>
</li>
<li class="md-nav__item">
<a href="#force-training-for-min-or-max-epochs" title="Force training for min or max epochs" class="md-nav__link">
<a href="#force-training-for-min-or-max-epochs" class="md-nav__link">
Force training for min or max epochs
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<li class="md-nav__item">
<a href="#early-stopping" title="Early stopping" class="md-nav__link">
<a href="#early-stopping" class="md-nav__link">
Early stopping
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<a href="#force-disable-early-stop" title="Force disable early stop" class="md-nav__link">
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Force disable early stop
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<li class="md-nav__item">
<a href="#gradient-clipping" title="Gradient Clipping" class="md-nav__link">
<a href="#gradient-clipping" class="md-nav__link">
Gradient Clipping
</a>
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<li class="md-nav__item">
<a href="#inspect-gradient-norms" title="Inspect gradient norms" class="md-nav__link">
<a href="#inspect-gradient-norms" class="md-nav__link">
Inspect gradient norms
</a>
</li>
<li class="md-nav__item">
<a href="#set-how-much-of-the-training-set-to-check" title="Set how much of the training set to check" class="md-nav__link">
<a href="#set-how-much-of-the-training-set-to-check" class="md-nav__link">
Set how much of the training set to check
</a>
@@ -559,49 +559,49 @@
<ul class="md-nav__list" data-md-scrollfix>
<li class="md-nav__item">
<a href="#accumulated-gradients" title="Accumulated gradients" class="md-nav__link">
<a href="#accumulated-gradients" class="md-nav__link">
Accumulated gradients
</a>
</li>
<li class="md-nav__item">
<a href="#force-training-for-min-or-max-epochs" title="Force training for min or max epochs" class="md-nav__link">
<a href="#force-training-for-min-or-max-epochs" class="md-nav__link">
Force training for min or max epochs
</a>
</li>
<li class="md-nav__item">
<a href="#early-stopping" title="Early stopping" class="md-nav__link">
<a href="#early-stopping" class="md-nav__link">
Early stopping
</a>
</li>
<li class="md-nav__item">
<a href="#force-disable-early-stop" title="Force disable early stop" class="md-nav__link">
<a href="#force-disable-early-stop" class="md-nav__link">
Force disable early stop
</a>
</li>
<li class="md-nav__item">
<a href="#gradient-clipping" title="Gradient Clipping" class="md-nav__link">
<a href="#gradient-clipping" class="md-nav__link">
Gradient Clipping
</a>
</li>
<li class="md-nav__item">
<a href="#inspect-gradient-norms" title="Inspect gradient norms" class="md-nav__link">
<a href="#inspect-gradient-norms" class="md-nav__link">
Inspect gradient norms
</a>
</li>
<li class="md-nav__item">
<a href="#set-how-much-of-the-training-set-to-check" title="Set how much of the training set to check" class="md-nav__link">
<a href="#set-how-much-of-the-training-set-to-check" class="md-nav__link">
Set how much of the training set to check
</a>
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<hr />
<h4 id="early-stopping">Early stopping</h4>
<p>To enable early-stopping, define the callback and give it to the trainer.</p>
<p>The trainer already sets up default early stopping for you.
To modify this behavior, pass in your own EarlyStopping callback.</p>
<table class="codehilitetable"><tr><td class="linenos"><div class="linenodiv"><pre> 1
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@@ -664,13 +665,13 @@
11
12</pre></div></td><td class="code"><div class="codehilite"><pre><span></span><span class="kn">from</span> <span class="nn">pytorch_lightning.callbacks</span> <span class="kn">import</span> <span class="n">EarlyStopping</span>
<span class="c1"># DEFAULTS</span>
<span class="c1"># DEFAULTS used by Trainer</span>
<span class="n">early_stop_callback</span> <span class="o">=</span> <span class="n">EarlyStopping</span><span class="p">(</span>
<span class="n">monitor</span><span class="o">=</span><span class="s1">&#39;val_loss&#39;</span><span class="p">,</span>
<span class="n">min_delta</span><span class="o">=</span><span class="mf">0.00</span><span class="p">,</span>
<span class="n">patience</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span>
<span class="n">patience</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span>
<span class="n">verbose</span><span class="o">=</span><span class="bp">False</span><span class="p">,</span>
<span class="n">mode</span><span class="o">=</span><span class="s1">&#39;auto&#39;</span>
<span class="n">mode</span><span class="o">=</span><span class="s1">&#39;min&#39;</span>
<span class="p">)</span>
<span class="n">trainer</span> <span class="o">=</span> <span class="n">Trainer</span><span class="p">(</span><span class="n">early_stop_callback</span><span class="o">=</span><span class="n">early_stop_callback</span><span class="p">)</span>
@@ -801,7 +802,7 @@ Specifically, this will <a href="https://pytorch.org/docs/stable/nn.html#torch.n
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