Fix training resuming docs (#1265)

docs/source/weights_loading.rst

@@ -84,9 +84,7 @@ To save your own checkpoint call:
 Checkpoint Loading
 ------------------
 
-You might want to not only load a model but also continue training it. Use this method to
-restore the trainer state as well. This will continue from the epoch and global step you last left off.
-However, the dataloaders will start from the first batch again (if you shuffled it shouldn't matter).
+To load a model along with its weights, biases and hyperparameters use following method:
 
 .. code-block:: python
 
@@ -95,4 +93,8 @@ However, the dataloaders will start from the first batch again (if you shuffled
     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.
+predictions as you would a nn.Module.
+
+
+.. note:: To restore the trainer state as well use
+    :meth:`pytorch_lightning.trainer.trainer.Trainer.resume_from_checkpoint`.

pytorch_lightning/trainer/training_io.py

@@ -39,15 +39,9 @@
 .. code-block:: python
 
     from pytorch_lightning import Trainer
-    from pytorch_lightning.loggers import TestTubeLogger
 
-    logger = TestTubeLogger(
-        save_dir='./savepath',
-        version=1  # An existing version with a saved checkpoint
-    )
     trainer = Trainer(
-        logger=logger,
-        default_save_path='./savepath'
+        resume_from_checkpoint=PATH
     )
 
     # this fit call loads model weights and trainer state