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Improvment to 97% accuracy #10
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@Ananas120 Hi, are you using python 2.7 or 3.*? |
I re-implemented your model architecture in python 3 in my personal project |
Yes it is normal ! I found that siamese networks need some epoch (sometimes more than 5 or 10 !) to increase their accuracy and this can be explained as follow : they try to separate « same » and « not same » from a scalar value (distance) so they have to find the right threshold before having a better score (before that they always predict either 0 or 1) |
Hello, i don’t know if this repo is active but if i can help, i used this repo for my project and i find a method to improve the loss / accuracy just by L2-normalizing the output of the encoder
My score with it is actually 0.97% accuracy and 0.02 val-BCEloss training for 25 epochs on a mixt of LibriSpeech and CommonVoice (fr) datasets (360 speakers in train set and 150 in validation set with 200 pairs for each speaker (100 same and 100 not same) (batch_size of size 32 (16 same and 16 not) with embedding dim 64)
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