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LightningModule/RequiredTrainerInterface/index.html

@@ -914,6 +914,52 @@
 <li><a href="./#update_tng_log_metrics">update_tng_log_metrics</a></li>
 <li><a href="./#add_model_specific_args">add_model_specific_args</a></li>
 </ul>
+<hr />
+<p><strong>Minimal example</strong></p>
+<pre><code class="python">import pytorch_lightning as ptl
+import torch
+from torch.nn import functional as F
+from torch.utils.data import DataLoader
+from torchvision.datasets import MNIST
+
+class CoolModel(ptl.LightningModule):
+
+    def __init(self):
+        # not the best model...
+        self.l1 = torch.nn.Linear(28*28, 10)
+
+    def forward(self, x):
+        return torch.relu(self.l1(x))
+
+    def my_loss(self, y_hat, y):
+        return F.cross_entropy(y_hat, y)
+
+    def training_step(self, batch, batch_nb):
+        x, y = batch
+        y_hat = self.forward(x)
+        return {'tng_loss': self.my_loss(y_hat, y)}
+
+    def validation_step(self, batch, batch_nb):
+        x, y = batch
+        y_hat = self.forward(x)
+        return {'val_loss': self.my_loss(y_hat, y)}
+
+    def configure_optimizers(self):
+        return [torch.optim.Adam(self.parameters(), lr=0.02)]
+
+    @ptl.data_loader
+    def tng_dataloader(self):
+        return DataLoader(MNIST('path/to/save', train=True), batch_size=32)
+
+    @ptl.data_loader
+    def val_dataloader(self):
+        return DataLoader(MNIST('path/to/save', train=False), batch_size=32)
+
+    @ptl.data_loader
+    def test_dataloader(self):
+        return DataLoader(MNIST('path/to/save', train=False), batch_size=32)   
+</code></pre>
+
 <hr />
 <h3 id="training_step">training_step</h3>
 <pre><code class="python">def training_step(self, data_batch, batch_nb)