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View Code? Open in Web Editor NEWTensorflow implementation of variational auto-encoder for MNIST
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charlie13 zhuyin521 jaypy-h jaehyek oppa3109 jeilove leoleishi fsucilab chagge jdc08161063 sunjieee drzhanying spinachr minas1900 mna12478 tobby2002 ehfo0 ml-lab zhyhhust wangpeng138375 imalikshake yunfanz mohanarunachalam insikk zhouqingping canbuoy gchoi wryoung412 ycui11 geflaspohler zhixiangxu andreofner mu94w haoyuanz13 tracycuiq damcy ywwang2013 anmolduainter hccho2 daniel451 he-org mingukkang pasudyan tjusxh kentwait sola303 sskqgfnnh goldkim92 ykwon0407 sumihui isaacre joonyoungyi chengyiwen afcarl handsomeboy pplntech abdelpakey maplewzx hansroh jinyucai95 weig1210 limingzhan0904 leloykun jaykimbravekjh vivonasg jianyuheng neheller waynecai2 hihenry2002 hojinyang lyunix sukajung kaiqiao1992 thdgudcks jungkj94 lawyerphx1 deoko zqcchris jlazarow bellazbl shraddhamachchhar yedam-lee sunghyunwee jayju heechulbae theanhle elephann joechenrh zyzisyz hykim-hil jnhoward kingfou reniew sddai hgh2017 cxncu001 yonghoonkwon nagano28 luojiecn minsukchoitensorflow-mnist-vae's Issues
some question about change datasets
I want to use this code to run on USPS dataset，how can i change the dataset , i always meet a typeerror. wait for your answer and thank you!
Nan Loss
Sometimes when I try this code, I get a Nan loss. For example the loss for epoch "i" is 100 and the loss for epoch "i+1" is Nan...
Do you have any idea why this happens?
Have you implemented the reconstruction loss for the Gaussian decoder?
How to calculate the accuracy？
Training on all the digits at once
Hi, While training on multiple images of MNIST I was not getting any reasonable output upon generation of images, however when I trained on a single digit (say 3) I was able to generate reasonable looking digits (like 3).
I tried increasing the iteration for training with multiple digits in dataset together, but still I am not getting reasonable outputs like yours, any suggestions?
ADD_NOISE
Hi,
In run_main, in the section "if ADD_NOISE:", the code states:
if ADD_NOISE:
x_PMLR = x_PMLR * np.random.randint(2, size=x_PMLR.shape)
x_PMLR += np.random.randint(2, size=x_PMLR.shape)
Line 1 creates pepper noise (since you are multiplying by 1 or 0). The second line adds 1 or 0 to each pixel. What is the rationale? Why not simply add a Gaussian or uniform noise of small amplitude to each pixel? Thanks.
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