A LightningModule has the following properties which you can access at any time

--- 
#### current_epoch
The current epoch   

---
#### dtype    
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 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 TestTubeLogger
self.logger.experiment.add_embedding(...)   
self.logger.experiment.log({'val_loss': 0.9})   
self.logger.experiment.add_scalars(...)   
```

--- 
#### global_step   
Total training batches seen across all epochs   

--- 
#### gradient_clip_val   
The current gradient clip value    

---
#### on_gpu    
True if your model is currently running on GPUs. Useful to set flags around the LightningModule for different CPU vs GPU behavior.    

---
#### trainer    
Last resort access to any state the trainer has. Changing certain properties here could affect your training run.
```{.python}   
self.trainer.optimizers
self.trainer.current_epoch
...   
```   

## Debugging   
The LightningModule also offers these tricks to help debug.   

---   
#### example_input_array    
In the LightningModule init, you can set a dummy tensor for this property
to get a print out of sizes coming into and out of every layer.   
```python
def __init__(self):
    # put the dimensions of the first input to your system
    self.example_input_array = torch.rand(5, 28 * 28)
```