Trying to improve readability

README.md

@@ -9,80 +9,80 @@ Documentation is available [here](http://pydlt.readthedocs.io/)
 
 ## Features
 
-- **Trainers** (currently Vanilla, VanillaGAN, WGAN-GP, BEGAN, FisherGAN)
+**Trainers** (currently Vanilla, VanillaGAN, WGAN-GP, BEGAN, FisherGAN)
 
 ```python
 trainer = dlt.train.VanillaGANTrainer(generator, discriminator, g_optim, d_optim)
 for batch, (prediction, losses) in trainer(data_loader):
     # Training happens in the iterator and relevant results are returned for each step
 ```
-- Built in configurable **parser** with arguments.
+Built in configurable **parser** with arguments.
 
 ```python
 opt = dlt.config.parse() # Has built in options (can add extra)
 print('Some Settings: ', opt.experiment_name, opt.batch_size, opt.lr)
 ```
 
-- **Configuration files** support and parser compatible functions.
+**Configuration files** support and parser compatible functions.
 
 ```bash
 $ python main.py @settings.cfg
 Some Settings:  config_test 32 0.0001
 ```
 
-- **HDR imaging** support (.hdr, .exr, and .pfm formats)
+**HDR imaging** support (.hdr, .exr, and .pfm formats)
 
 ```python
 img = dlt.hdr.imread('test.pfm')
 dlt.hdr.imwrite('test.exr', img)
 ```
 
-- **Checkpointing** of (torch serializable) objects; Network state dicts supported.
+**Checkpointing** of (torch serializable) objects; Network state dicts supported.
 
 ```python
 data_chkp = Checkpointer('data')
 data_chkp.save(np.array([1,2,3]))
 a = data_chkp.load()
 ```
 
-- **Image operations** and easy conversions between multiple library views (torch, cv, plt)
+**Image operations** and easy conversions between multiple library views (torch, cv, plt)
 
 ```python
 img = cv2.imread('image.jpg') # Height x Width x Channels - BGR
 dlt.viz.imshow(img, view='cv')  # Height x Width x Channels - RGB
 tensor_with_torch_view = cv2torch(img) # Channels x Height x Width - RGB
 ```
 
-- Easy **visualization** (and make_grid supporting Arrays, Tensors, Variables and lists)
+Easy **visualization** (and make_grid supporting Arrays, Tensors, Variables and lists)
 
 ```python
 for batch, (prediction, loss) in trainer(loader):
     grid = dlt.util.make_grid([ batch[0], batch[1], prediction], size(3, opt.batch_size))
     dlt.viz.imshow(grid, pause=0.01, title='Training Progress')
 ```
 
-- Model parameter and layer input/outputs/gradients visualization.
+Parameter and input/outputs/gradients **layer visualization**.
 
 ```python
 net = nn.Sequential(nn.Linear(10, 10))
 dlt.viz.modules.forward_hook(net, [nn.Linear], tag='layer_outputs', histogram=False)
 net(Variable(torch.Tensor(3,10)))
 ```
 
-- CSV **Logger**.
+CSV **Logger**.
 
 ```python
 log = dlt.util.Logger('losses', ['train_loss', 'val_loss'])
 log({'train_loss': 10, 'val_loss':20})
 ```
 
-- Command line tool for easy **plotting** of CSV files (with live updating).
+Command line tool for easy **plotting** of CSV files (with live updating).
 
 ```bash
 $ dlt-plot --file losses.csv train_loss val_loss --refresh 5 --loglog True --tail 100
 ```
 
-- A minimal **Progress bar** (with global on/off switch).
+A minimal **Progress bar** (with global on/off switch).
 
 ```python
 from dlt.util import barit
@@ -93,7 +93,7 @@ for batch in barit(loader, start='Loading'):
 
 ## Installation
 
-Make sure you have PyTorch_ installed. OpenCV is also required:
+Make sure you have [PyTorch](http://pytorch.org/) installed. OpenCV is also required:
 
 ```bash    
 conda install -c menpo opencv