Update READMEs

README.md

@@ -5,8 +5,8 @@ This repository provides the implementation of **Lifting Layers: Analysis and Ap
 Instructions
 -------------------
 
-1. Execute `git clone git@github.com:michimoeller/liftingLayers.git` to clone the repository
-2. Follow installation and usage instructions in the respective MATLAB and Python subdirectories
+1. Execute `git clone git@github.com:michimoeller/liftingLayers.git` to clone the repository.
+2. Follow installation and usage instructions in the respective MATLAB and Python subdirectories.
 
 Publication
 -------------------

python_experiments/README.md

@@ -10,15 +10,16 @@ Installation
 2. Load datasets from [here](https://drive.google.com/drive/folders/14OUgEqUcCkZC26Zu_FPXurlpIZZmOXHV?usp=sharing) and extract them to the `data` directory.
 3. (**Optional**, for storing experiments in a database) Install MongoDB and PyMongo and start a MongoDB daemon (`mongod`).
 
-Usage - Maxout (MNIST classfication) Experiments
+Usage - Maxout (MNIST classification) Experiments
 -------------------
 
-1. Run experiment for one of three activation functions (`RELU`, `LIFT`, `MAXOUT`) by executing for example `python src/train_mnist.py LIFT`.
+1. Start Tensorboard with `tensorboard --logdir=logs` (default: [localhost:6006](localhost:6006)).
+2. Run experiment for one of three activation functions (`RELU`, `LIFT`, `MAXOUT`) by executing for example `python src/train_mnist.py LIFT`.
 
 Usage - Denoising Experiments
 -------------------
 
-1. Start visdom server with `python -m visdom.server -env_path logs/visdom` at [localhost:8097](localhost:8097).
+1. Start visdom server with `python -m visdom.server -env_path logs/visdom` (default: [localhost:8097](localhost:8097)).
 2. Test an existing model with: `python src/train_denoising.py with seed=1 test_model_path=models/25_dncnn_s_17_best_val.model`
 3. Train a new model with: `CUDA_VISIBLE_DEVICES=0 python src/train_denoising.py with seed=1 model_name=LiftNet nn_train.data_cfg.noise.stddev=25.0`.
 4. See `config/train_denoising.yaml` or execute `python src/train_denoising.py print_config` for further configuration options.