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<a href="https://github.com/williamFalcon/pytorch-lightning/edit/master/docs/LightningModule/properties.md" title="Edit this page" class="md-icon md-content__icon">&#xE3C9;</a>
<h1>Properties</h1>
<p>A LightningModule has the following properties which you can access at any time</p>
<hr />
<h4 id="current_epoch">current_epoch</h4>
<p>The current epoch </p>
<hr />
<h4 id="dtype">dtype</h4>
<p>Current dtype </p>
<hr />
<h4 id="logger">logger</h4>
<p>A reference to the logger you passed into trainer.
Passing a logger is optional. If you don't pass one in, Lightning will create one for you automatically.
This logger saves logs to '''/os.getcwd()/lightning_logs'''</p>
<table class="codehilitetable"><tr><td class="linenos"><div class="linenodiv"><pre>1</pre></div></td><td class="code"><div class="codehilite"><pre><span></span><span class="n">Trainer</span><span class="p">(</span><span class="n">logger</span><span class="o">=</span><span class="n">your_logger</span><span class="p">)</span>
</pre></div>
</td></tr></table>
<p>Call it from anywhere in your LightningModule to add metrics, images, etc... whatever your logger supports. </p>
<p>Here is an example using the TestTubeLogger (which is a wrapper on <a href="https://pytorch.org/docs/stable/tensorboard.html">PyTorch SummaryWriter</a> with versioned folder structure). </p>
<table class="codehilitetable"><tr><td class="linenos"><div class="linenodiv"><pre>1
2
3
4</pre></div></td><td class="code"><div class="codehilite"><pre><span></span><span class="c1"># if logger is a tensorboard logger or TestTubeLogger</span>
<span class="bp">self</span><span class="o">.</span><span class="n">logger</span><span class="o">.</span><span class="n">experiment</span><span class="o">.</span><span class="n">add_embedding</span><span class="p">(</span><span class="o">...</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">logger</span><span class="o">.</span><span class="n">experiment</span><span class="o">.</span><span class="n">log</span><span class="p">({</span><span class="s1">&#39;val_loss&#39;</span><span class="p">:</span> <span class="mf">0.9</span><span class="p">})</span>
<span class="bp">self</span><span class="o">.</span><span class="n">logger</span><span class="o">.</span><span class="n">experiment</span><span class="o">.</span><span class="n">add_scalars</span><span class="p">(</span><span class="o">...</span><span class="p">)</span>
</pre></div>
</td></tr></table>
<hr />
<h4 id="global_step">global_step</h4>
<p>Total training batches seen across all epochs </p>
<hr />
<h4 id="gradient_clip_val">gradient_clip_val</h4>
<p>The current gradient clip value </p>
<hr />
<h4 id="on_gpu">on_gpu</h4>
<p>True if your model is currently running on GPUs. Useful to set flags around the LightningModule for different CPU vs GPU behavior. </p>
<hr />
<h4 id="trainer">trainer</h4>
<p>Last resort access to any state the trainer has. Changing certain properties here could affect your training run.</p>
<table class="codehilitetable"><tr><td class="linenos"><div class="linenodiv"><pre>1
2
3</pre></div></td><td class="code"><div class="codehilite"><pre><span></span><span class="bp">self</span><span class="o">.</span><span class="n">trainer</span><span class="o">.</span><span class="n">optimizers</span>
<span class="bp">self</span><span class="o">.</span><span class="n">trainer</span><span class="o">.</span><span class="n">current_epoch</span>
<span class="o">...</span>
</pre></div>
</td></tr></table>
<h2 id="debugging">Debugging</h2>
<p>The LightningModule also offers these tricks to help debug. </p>
<hr />
<h4 id="example_input_array">example_input_array</h4>
<p>In the LightningModule init, you can set a dummy tensor for this property
to get a print out of sizes coming into and out of every layer. </p>
<table class="codehilitetable"><tr><td class="linenos"><div class="linenodiv"><pre>1
2
3</pre></div></td><td class="code"><div class="codehilite"><pre><span></span><span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="c1"># put the dimensions of the first input to your system</span>
<span class="bp">self</span><span class="o">.</span><span class="n">example_input_array</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">rand</span><span class="p">(</span><span class="mi">5</span><span class="p">,</span> <span class="mi">28</span> <span class="o">*</span> <span class="mi">28</span><span class="p">)</span>
</pre></div>
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