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LightningModule/methods/index.html

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 <hr />
 <h3 id="load_from_metrics">load_from_metrics</h3>
-<p>This is the easiest/fastest way which uses the meta_tags.csv file from test-tube to rebuild the model.
-The meta_tags.csv file can be found in the test-tube experiment save_dir.       </p>
+<p>This is the easiest/fastest way which loads hyperparameters and weights from a checkpoint,
+such as the one saved by the <code>ModelCheckpoint</code> callback</p>
+<table class="codehilitetable"><tr><td class="linenos"><div class="linenodiv"><pre>1
+2
+3
+4
+5
+6
+7
+8</pre></div></td><td class="code"><div class="codehilite"><pre><span></span><span class="n">pretrained_model</span> <span class="o">=</span> <span class="n">MyLightningModule</span><span class="o">.</span><span class="n">load_from_checkpoint</span><span class="p">(</span>
+    <span class="n">checkpoint_path</span><span class="o">=</span><span class="s1">&#39;/path/to/pytorch_checkpoint.ckpt&#39;</span>
+<span class="p">)</span>
+
+<span class="c1"># predict</span>
+<span class="n">pretrained_model</span><span class="o">.</span><span class="n">eval</span><span class="p">()</span>
+<span class="n">pretrained_model</span><span class="o">.</span><span class="n">freeze</span><span class="p">()</span>
+<span class="n">y_hat</span> <span class="o">=</span> <span class="n">pretrained_model</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
+</pre></div>
+</td></tr></table>
+
+<hr />
+<h3 id="load_from_metrics_1">load_from_metrics</h3>
+<p>If you're using test tube, there is an alternate method which uses the meta_tags.csv
+file from test-tube to rebuild the model. The meta_tags.csv file can be found in the
+test-tube experiment save_dir.       </p>
 <table class="codehilitetable"><tr><td class="linenos"><div class="linenodiv"><pre> 1
  2
  3
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