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View Code? Open in Web Editor NEWTensorflow implementation of variational auto-encoder for MNIST
Tensorflow implementation of variational auto-encoder for MNIST
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.
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?
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!
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?
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