From 52bcfefe8c92e817ecde429ddf9006be68cd7afb Mon Sep 17 00:00:00 2001 From: Hans Date: Sun, 15 Nov 2020 14:48:42 +0000 Subject: [PATCH] better top-k comments --- train.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/train.py b/train.py index bdd907df..8e7f2535 100755 --- a/train.py +++ b/train.py @@ -218,10 +218,10 @@ def setup_training_options( args.G_args.fmap_base = 32 << 10 args.G_args.fmap_max = 1024 args.loss_args.G_top_k = True - # args.loss_args.G_top_k_gamma = 0.9862 # takes 12500 kimg to decay to 0.5 - args.loss_args.G_top_k_gamma = 0.9726 # takes 6250 kimg to decay to 0.5 + # args.loss_args.G_top_k_gamma = 0.9862 # takes 12500 kimg to decay to 0.5 (~1/2 of total_kimg when training from scratch) + args.loss_args.G_top_k_gamma = 0.9726 # takes 6250 kimg to decay to 0.5 (~1/4 of total_kimg when training from scratch) args.loss_args.G_top_k_frac = 0.5 - args.minibatch_gpu = 2 # probably will need to set this pretty low with such a large G + args.minibatch_gpu = 2 # probably will need to set this pretty low with such a large G, higher values work better for top-k training though # args.G_args.num_fp16_res = 6 # making more layers fp16 can help as well if cfg == 'cifar' or cfg.split('-')[-1] == 'complex':