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Dec 24, 2021 at 1:27 answer added Kevin timeline score: 1
Jul 30, 2020 at 16:13 comment added mrgloom There is a typo in calculations, but yes loss is asymmetric: >>> -0.2 * np.log(0.1) - (1.0-0.2) * np.log(1.0-0.1) 0.5448054311250702 >>> -0.2 * np.log(0.3) - (1.0-0.2) * np.log(1.0-0.3) 0.5261345160161732
Jun 10, 2020 at 14:52 history edited Flek CC BY-SA 4.0
Fixed a typo
S Mar 8, 2019 at 14:29 history bounty ended Flek
S Mar 8, 2019 at 14:29 history notice removed Flek
Mar 8, 2019 at 14:27 vote accept Flek
Mar 7, 2019 at 22:36 answer added hans timeline score: 8
Mar 7, 2019 at 22:02 comment added hans Sure, I was just joking :-) I understand your question. For binary data answer is obvious - gradient behaves as constant when prediction is very close to true value (for log), while for mse it is linear, i.e. it becomes small.
Mar 7, 2019 at 19:00 comment added Flek Indeed, I used log10, but the base doesn't matter. What matters is the asymmetry and what its benefits are. Maybe log loss and mse are both equally useful. I was just wondering as it appeared to me that ~50% use log loss for image autoencoders and the other ~50% use mse.
Mar 7, 2019 at 17:49 comment added hans Funny, I pasted your calculations into python and I got 0.639 and 0.526 respectively. Probably you used log based on 10 and I used natural logarithm.
Mar 6, 2019 at 12:00 history tweeted twitter.com/StackStats/status/1103264123419328512
Mar 2, 2019 at 5:34 answer added ReneBt timeline score: 1
Mar 1, 2019 at 22:48 answer added JaeHyeok Shin timeline score: 3
S Mar 1, 2019 at 15:34 history bounty started Flek
S Mar 1, 2019 at 15:34 history notice added Flek Draw attention
Feb 28, 2019 at 14:43 history edited Flek CC BY-SA 4.0
Fixed a small typo to clarify the sentence
Feb 26, 2019 at 23:55 review First posts
Feb 27, 2019 at 7:13
Feb 26, 2019 at 23:51 history asked Flek CC BY-SA 4.0