Merge branch 'master' of github.com:jmhb0/o2vae

README.md

@@ -77,6 +77,8 @@ To train an o2-vae model, edit `./run.bash` to point to the right config file, a
 ```
 bash run.bash
 ```
+The example commands in that script are for the demo dataset and configs (mext section). Training these demos on GPUs (nvidia-rtx) with the default configs in `run.bash` takes <1min per training epoch for both demo datasets. Training converges in about 50 epochs.
+
 **Important** check the terminal for the location of the saved models. Something like:
 > Logging directory is `wandb/<log_dir>`
 
@@ -86,10 +88,10 @@ We provide two demo datasets, [o2-mnist](./data/o2_mnist/README.md) and [MEFS](.
 python data/generate_o2mnist.py
 bash data/mefs/unzip_mefs.bash
 ```
-They each have a config file `configs/config_o2mnst.py` and `configs/config_mefs.py`. A model can be trained using the script above, OR they can be run in notebooks `notebooks/`
+They each have a config file `configs/config_o2mnst.py` and `configs/config_mefs.py`. A model can be trained using the script above, OR they can be run in notebooks `examples/`
 
 ### Running in a notebook
-Examples notebooks for training models are in `notebooks/`. This is mostly the same code as `run.py` but without any logging. 
+Examples notebooks for training models are in `examples/`. This is mostly the same code as `run.py` but without any logging. 
 
 ## <a name="usage2"/> Usage - using representation for analysis  
 ### Recovering trained models 
@@ -102,7 +104,7 @@ import torch
 from configs.<my_config> import config
 model=run.get_datasets_from_config(config)
 
-fname_model=wandb/<run_name>/files_model.pt`
+fname_model=wandb/<run_name>/files_model.pt
 saved_model=torch.load(fname_model)
 model.load_state_dict(saved_model['state_dict'])
 ```