From c710e3a3467f9382a680dd68d71f4f740586bfe1 Mon Sep 17 00:00:00 2001 From: Denis Yarats Date: Mon, 23 Sep 2019 15:20:14 -0400 Subject: [PATCH] Update README.md --- README.md | 13 ++++++------- 1 file changed, 6 insertions(+), 7 deletions(-) diff --git a/README.md b/README.md index 7795a0b..645843f 100644 --- a/README.md +++ b/README.md @@ -24,17 +24,17 @@ python train.py \ --work_dir ./log \ --seed 1 ``` -This will produce a folder (`./save`) by default, where all the output is going to be stored including train/eval logs, tensorboard blobs, evaluation videos, and model snapshots. It is possible to attach tensorboard to a particular run using the following command: +This will produce 'log' folder, where all the outputs are going to be stored including train/eval logs, tensorboard blobs, and evaluation episode videos. One can attacha tensorboard to monitor training by running: ``` -tensorboard --logdir save +tensorboard --logdir log ``` -Then open up tensorboad in your browser. +and opening up tensorboad in your browser. -You will also see some console output, something like this: +The console output is also available in a form: ``` | train | E: 1 | S: 1000 | D: 0.8 s | R: 0.0000 | BR: 0.0000 | ALOSS: 0.0000 | CLOSS: 0.0000 | RLOSS: 0.0000 ``` -This line means: +a training entry decodes as: ``` train - training episode E - total number of episodes @@ -46,8 +46,7 @@ ALOSS - average loss of actor CLOSS - average loss of critic RLOSS - average reconstruction loss (only if is trained from pixels and decoder) ``` -These are just the most important number, more of all other metrics can be found in tensorboard. -Also, besides training, once in a while there is evaluation output, like this: +and an evaluation entry : ``` | eval | S: 0 | ER: 21.1676 ```