clean up docs around loggers (#304)

docs/LightningModule/RequiredTrainerInterface.md

@@ -281,7 +281,7 @@ def validation_step(self, batch, batch_nb):
     # or generated text... or whatever
     sample_imgs = x[:6]
     grid = torchvision.utils.make_grid(sample_imgs)
-    self.experiment.add_image('example_images', grid, 0) 
+    self.logger.experiment.add_image('example_images', grid, 0) 
     
     # calculate acc
     labels_hat = torch.argmax(out, dim=1)

docs/LightningModule/properties.md

@@ -11,18 +11,20 @@ Current dtype
 ---
 #### logger    
 A reference to the logger you passed into trainer. 
+Passing a logger is optional. If you don't pass one in, Lightning will create one for you automatically.
+This logger saves logs to '''/os.getcwd()/lightning_logs'''
 ```python
 Trainer(logger=your_logger)
 ```    
 
 Call it from anywhere in your LightningModule to add metrics, images, etc... whatever your logger supports.  
 
-Here is an example using the Test-tube logger (which is a wrapper on [PyTorch SummaryWriter](https://pytorch.org/docs/stable/tensorboard.html) with versioned folder structure).     
+Here is an example using the TestTubeLogger (which is a wrapper on [PyTorch SummaryWriter](https://pytorch.org/docs/stable/tensorboard.html) with versioned folder structure).     
 ```{.python}
-# if logger is a tensorboard logger or test-tube experiment
-self.logger.add_embedding(...)   
-self.logger.log({'val_loss': 0.9})   
-self.logger.add_scalars(...)   
+# if logger is a tensorboard logger or TestTubeLogger
+self.logger.experiment.add_embedding(...)   
+self.logger.experiment.log({'val_loss': 0.9})   
+self.logger.experiment.add_scalars(...)   
 ```
 
 ---