Comments (4)
Hi, I am not sure the setting on your site, maybe it is related to issue 5, could be due to the insufficient training.
from dd-net.
Hello @fandulu
Thanks for your fast reply.
I thought it was something wrong with the settings since I just tried the provided code with the default values. And even changing the values I didn't get the desired confusion matrix.
Strangely I get similar model accuracy like in issue 5 , however the confusion matrix looks like this
having the following values:
[[54 1 0 3 0 0 0 0 0 0 0 0 0 0]
[ 4 47 0 2 1 1 0 0 0 5 0 0 0 1]
[ 0 2 49 0 0 0 0 0 4 0 0 0 0 0]
[ 6 0 0 43 0 2 0 0 0 0 0 0 0 0]
[ 0 0 0 0 54 0 1 0 0 0 0 0 0 0]
[ 0 0 0 0 0 57 0 1 0 0 0 0 0 0]
[ 0 0 0 0 0 0 62 0 0 0 0 0 0 0]
[ 0 0 0 0 1 0 0 53 0 0 0 0 0 0]
[ 0 2 1 0 0 0 0 0 64 1 0 0 0 0]
[ 0 2 0 3 0 0 0 0 0 56 0 0 0 0]
[ 0 0 0 0 0 1 0 0 0 0 68 0 0 0]
[ 0 0 0 0 0 0 0 0 0 0 0 57 0 0]
[ 0 0 0 0 0 0 0 0 0 0 0 0 58 0]
[ 0 1 1 0 2 0 0 0 0 0 0 0 0 69]]
More training didn't change the values much. This was the best I could get.
What I am expecting is something similar to :
So my question was, how you get this confusion matrix, where you achieve values >90% ?
I am using the provided code, so I expected to have a similar confusion matrix values.
Thanks for
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I think you need to normalize the value to be a ratio of 0%-100% instead of the number of samples.
from dd-net.
okay
if i normalize them i reach the values from the paper
@fandulu thank you so much
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