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Hooks
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Hooks
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<a href="https://github.com/williamFalcon/pytorch-lightning/edit/master/docs/Trainer/hooks.md" title="Edit this page" class="md-icon md-content__icon">&#xE3C9;</a>
<h1>Hooks</h1>
<h1 id="hooks">Hooks</h1>
<p>[<a href="https://github.com/williamFalcon/pytorch-lightning/blob/master/pytorch_lightning/root_module/hooks.py">Github Code</a>] </p>
<p>There are cases when you might want to do something different at different parts of the training/validation loop.
To enable a hook, simply override the method in your LightningModule and the trainer will call it at the correct time.</p>
<p><strong>Contributing</strong> If there's a hook you'd like to add, simply:
1. Fork PytorchLightning.
2. Add the hook <a href="https://github.com/williamFalcon/pytorch-lightning/blob/master/pytorch_lightning/root_module/hooks.py">here</a>. <br />
3. Add the correct place in the <a href="https://github.com/williamFalcon/pytorch-lightning/blob/master/pytorch_lightning/models/trainer.py">Trainer</a>
where it should be called.</p>
<hr />
<h4 id="on_epoch_start">on_epoch_start</h4>
<p>Called in the training loop at the very beginning of the epoch. </p>
<pre><code class="python">def on_epoch_start(self):
# do something when the epoch starts
</code></pre>
<hr />
<h4 id="on_batch_end">on_batch_end</h4>
<p>Called in the training loop at the very end of the epoch. </p>
<pre><code class="python">def on_epoch_end(self):
# do something when the epoch ends
</code></pre>
<hr />
<h4 id="on_batch_start">on_batch_start</h4>
<p>Called in the training loop before anything happens for that batch. </p>
<pre><code class="python">def on_batch_start(self):
# do something when the batch starts
</code></pre>
<hr />
<h4 id="on_pre_performance_check">on_pre_performance_check</h4>
<p>Called at the very beginning of the validation loop. </p>
<pre><code class="python">def on_pre_performance_check(self):
# do something before validation starts
</code></pre>
<hr />
<h4 id="on_post_performance_check">on_post_performance_check</h4>
<p>Called at the very end of the validation loop. </p>
<pre><code class="python">def on_post_performance_check(self):
# do something before validation end
</code></pre>
<hr />
<h4 id="on_tng_metrics">on_tng_metrics</h4>
<p>Called in the training loop, right before metrics are logged.
Although you can log at any time by using self.experiment, you can use
this callback to modify what will be logged.</p>
<pre><code class="python">def on_tng_metrics(self, metrics):
# do something before validation end
</code></pre>
<hr />
<h4 id="on_before_zero_grad">on_before_zero_grad</h4>
<p>Called in the training loop after taking an optimizer step and before zeroing grads.
Good place to inspect weight information with weights updated.</p>
<p>Called once per optimizer</p>
<pre><code class="python">def on_before_zero_grad(self, optimizer):
# do something with the optimizer or inspect it.
</code></pre>
<hr />
<h4 id="on_after_backward">on_after_backward</h4>
<p>Called in the training loop after model.backward()
This is the ideal place to inspect or log gradient information </p>
<pre><code class="python">def on_after_backward(self):
# example to inspect gradient information in tensorboard
if self.trainer.global_step % 25 == 0: # don't make the tf file huge
params = self.state_dict()
for k, v in params.items():
grads = v
name = k
self.experiment.add_histogram(tag=name, values=grads, global_step=self.trainer.global_step)
</code></pre>
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