diff --git a/README.md b/README.md
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@@ -7,6 +7,8 @@ GitHub repo for my Tensorflow World hackathon submission
 
 To replicate my results, just clone this repo and run `scripts/WGAN-GP.py` after modifying `DATA_DIR`. I used [this](http://deepyeti.ucsd.edu/cdonahue/wavegan/data/mancini_piano.tar.gz) data after converting the files to wave. The violin data I used was just scraped from Youtube.
 
+Run `tensorboard --logdir logs/train` to view generator and critic loss with Tensorboard.
+
 ## Inspiration
 Since the inception of generative adversarial networks, I have been fascinated by their capacity to perform tasks of unprecedented complexity. They are a prime example of how machines can learn in a similar manner to humans - akin to reinforcement learning. I am also a huge fan of music and love to play the piano. So I thought, why not conflate my love for machine learning and my passion for music!?