made load from checkpoint flexible (#1307)

* made load from checkpoint flexible

* made load from checkpoint flexible

* made load from checkpoint flexible

docs/source/weights_loading.rst

@@ -84,7 +84,7 @@ To save your own checkpoint call:
 Checkpoint Loading
 ------------------
 
-To load a model along with its weights, biases and hyperparameters use following method:
+To load a model along with its weights, biases and hyperparameters use following method.
 
 .. code-block:: python
 
@@ -92,9 +92,42 @@ To load a model along with its weights, biases and hyperparameters use following
     model.eval()
     y_hat = model(x)
 
-A LightningModule is no different than a nn.Module. This means you can load it and use it for
-predictions as you would a nn.Module.
+The above only works if you used `hparams` in your model definition
+
+.. code-block:: python
+
+    class MyModel(pl.LightningModule):
+
+        def __init__(self, hparams):
+            self.hparams = hparams
+            self.l1 = nn.Linear(hparams.in_dim, hparams.out_dim)
+
+But if you don't and instead pass individual parameters
+
+.. code-block:: python
+
+    class MyModel(pl.LightningModule):
+
+        def __init__(self, in_dim, out_dim):
+            self.l1 = nn.Linear(in_dim, out_dim)
+
+you can restore the model like this
+
+.. code-block:: python
+
+    model = MyModel.load_from_checkpoint(PATH, in_dim=128, out_dim=10)
 
 
-.. note:: To restore the trainer state as well use
-    :meth:`pytorch_lightning.trainer.trainer.Trainer.resume_from_checkpoint`.
+Restoring Training State
+------------------------
+
+If you don't just want to load weights, but instead restore the full training,
+do the following:
+
+.. code-block:: python
+
+   model = LitModel()
+   trainer = Trainer(resume_from_checkpoint='some/path/to/my_checkpoint.ckpt')
+
+   # automatically restores model, epoch, step, LR schedulers, apex, etc...
+   trainer.fit(model)