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docs/source/transfer_learning.rst

@@ -7,6 +7,8 @@ Using Pretrained Models
 Sometimes we want to use a LightningModule as a pretrained model. This is fine because
 a LightningModule is just a `torch.nn.Module`!
 
+.. note:: Remember that a pl.LightningModule is EXACTLY a torch.nn.Module but with more capabilities.
+
 Let's use the `AutoEncoder` as a feature extractor in a separate model.
 
 
@@ -27,7 +29,7 @@ Let's use the `AutoEncoder` as a feature extractor in a separate model.
             self.feature_extractor.freeze()
 
             # the autoencoder outputs a 100-dim representation and CIFAR-10 has 10 classes
-            self.classifier = nn.Liner(100, 10)
+            self.classifier = nn.Linear(100, 10)
 
         def forward(self, x):
             representations = self.feature_extractor(x)
@@ -36,11 +38,58 @@ Let's use the `AutoEncoder` as a feature extractor in a separate model.
 
 We used our pretrained Autoencoder (a LightningModule) for transfer learning!
 
-Example: BERT (transformers)
-^^^^^^^^^^^^^^^^^^^^^^^^^^^^
-Lightning is completely agnostic to what's used for tranfer learning so long
+Example: Imagenet (computer Vision)
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+.. code-block:: python
+
+    import torchvision.models as models
+
+    class ImagenetTranferLearning(pl.LightingModule):
+        def __init__(self):
+            # init a pretrained resnet
+            num_target_classes = 10
+            self.feature_extractor = model.resnet50(
+                                        pretrained=True,
+                                        num_classes=num_target_classes)
+            self.feature_extractor.eval()
+
+            # use the pretrained model to classify cifar-10 (10 image classes)
+            self.classifier = nn.Linear(2048, num_target_classes)
+
+        def forward(self, x):
+            representations = self.feature_extractor(x)
+            x = self.classifier(representations)
+            ...
+
+Finetune
+
+.. code-block:: python
+
+    model = ImagenetTranferLearning()
+    trainer = Trainer()
+    trainer.fit(model)
+
+And use it to predict your data of interest
+
+.. code-block:: python
+
+    model = ImagenetTranferLearning.load_from_checkpoint(PATH)
+    model.freeze()
+
+    x = some_images_from_cifar10()
+    predictions = model(x)
+
+We used a pretrained model on imagenet, finetuned on CIFAR-10 to predict on CIFAR-10.
+In the non-academic world we would finetune on a tiny dataset you have and predict on your dataset.
+
+Example: BERT (NLP)
+^^^^^^^^^^^^^^^^^^^
+Lightning is completely agnostic to what's used for transfer learning so long
 as it is a `torch.nn.Module` subclass.
 
+Here's a model that uses `Huggingface transformers <https://github.com/huggingface/transformers>`_.
+
 .. code-block:: python
 
     from transformers import BertModel