diff --git a/docs/LightningModule/RequiredTrainerInterface.md b/docs/LightningModule/RequiredTrainerInterface.md
index 9cecf8a0..8014b0b8 100644
--- a/docs/LightningModule/RequiredTrainerInterface.md
+++ b/docs/LightningModule/RequiredTrainerInterface.md
@@ -143,10 +143,15 @@ def training_step(self, batch, batch_nb):
     out = self.forward(x)
     loss = self.loss(out, x)
     
+    logger_logs = {'training_loss': loss} # optional (MUST ALL BE TENSORS)
+    
+    # if using TestTubeLogger or TensorboardLogger you can nest scalars
+    logger_logs = {'losses': logger_logs} # optional (MUST ALL BE TENSORS)
+    
     output = {
         'loss': loss, # required
         'progress_bar': {'training_loss': loss}, # optional (MUST ALL BE TENSORS)
-        'log': {'training_loss': loss} # optional (MUST ALL BE TENSORS)
+        'log': logger_logs
     }
     
     # return a dict
diff --git a/docs/Trainer/Logging.md b/docs/Trainer/Logging.md
index f3e93b14..aa59a089 100644
--- a/docs/Trainer/Logging.md
+++ b/docs/Trainer/Logging.md
@@ -33,7 +33,10 @@ Lightning supports several common experiment tracking frameworks out of the box
 ---
 #### Test tube
 
-Log using [test tube](https://williamfalcon.github.io/test-tube/).
+Log using [test tube](https://williamfalcon.github.io/test-tube/). Test tube logger is
+a strict subclass of [PyTorch SummaryWriter](https://pytorch.org/docs/stable/tensorboard.html), refer to their
+documentation for all supported operations. The TestTubeLogger adds a nicer folder structure
+to manage experiments and snapshots all hyperparameters you pass to a LightningModule.
 
 ```{.python}
 from pytorch_lightning.logging import TestTubeLogger
@@ -46,6 +49,16 @@ tt_logger = TestTubeLogger(
 trainer = Trainer(logger=tt_logger)
 ```
 
+Use the logger anywhere in you LightningModule as follows:
+```python
+def train_step(...):
+    # example
+    self.logger.experiment.whatever_method_summary_writer_supports(...)
+    
+def any_lightning_module_function_or_hook(...):
+    self.logger.experiment.add_histogram(...)
+```
+
 ---
 #### MLFlow
 
@@ -60,6 +73,16 @@ mlf_logger = MLFlowLogger(
 trainer = Trainer(logger=mlf_logger)
 ```
 
+Use the logger anywhere in you LightningModule as follows:
+```python
+def train_step(...):
+    # example
+    self.logger.experiment.whatever_ml_flow_supports(...)
+    
+def any_lightning_module_function_or_hook(...):
+    self.logger.experiment.whatever_ml_flow_supports(...)
+```
+
 ---
 #### Custom logger
 
@@ -101,13 +124,14 @@ a pull request to add it to Lighting!
 #### Using loggers
 You can call the logger anywhere from your LightningModule by doing:
 ```python
-self.logger
-
-# add an image if using TestTubeLogger
-self.logger.experiment.add_image(...)
+def train_step(...):
+    # example
+    self.logger.experiment.whatever_method_summary_writer_supports(...)
+    
+def any_lightning_module_function_or_hook(...):
+    self.logger.experiment.add_histogram(...)
 ```
 
-
 #### Display metrics in progress bar 
 ``` {.python}
 # DEFAULT