Row 6773

Row ID: 6773 | Dataset Entry | Axioma AXP Content Repository

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Hi guys

I'm current cloning this repo: [https://github.com/bhpfelix/Variational-Autoencoder-PyTorch/blob/master/src/vanila\_vae.py](https://github.com/bhpfelix/Variational-Autoencoder-PyTorch/blob/master/src/vanila_vae.py) to do VAE.

What I don't understand is that in the reparametrize step of muy and sigma, he used a normal distribution which generates random variables. During inference, I check it is really output different outcome. Should I put a seed to lock the values? or any other method, or maybe just leave it there because it is part of the code ?

I'm doing inference only, not training.

FieldValue
text Hi guys I'm current cloning this repo: [https://github.com/bhpfelix/Variational-Autoencoder-PyTorch/blob/master/src/vanila\_vae.py](https://github.com/bhpfelix/Variational-Autoencoder-PyTorch/blob/master/src/vanila_vae.py) to do VAE. What I don't understand is that in the reparametrize step of muy and sigma, he used a normal distribution which generates random variables. During inference, I check it is really output different outcome. Should I put a seed to lock the values? or any other method…
label r/deeplearning
dataType post
communityName r/deeplearning
datetime 2024-05-13
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{
  "text": "Hi guys\n\nI'm current cloning this repo: [https://github.com/bhpfelix/Variational-Autoencoder-PyTorch/blob/master/src/vanila\\_vae.py](https://github.com/bhpfelix/Variational-Autoencoder-PyTorch/blob/master/src/vanila_vae.py) to do VAE.\n\nWhat I don't understand is that in the reparametrize step of muy and sigma, he used a normal distribution which generates random variables. During inference, I check it is really output different outcome. Should I put a seed to lock the values? or any other method, or maybe just leave it there because it is part of the code ? \n\nI'm doing inference only, not training.\n\n",
  "label": "r/deeplearning",
  "dataType": "post",
  "communityName": "r/deeplearning",
  "datetime": "2024-05-13",
  "username_encoded": "Z0FBQUFBQm5LakwzUW5sSmZaSHhBZmxsTGJXSi02d0hXSWNrbzhmSWp4Y21oeG9EZTBQVDJhTERXazkwMTQwNWNrdnBUY2FMclBXN2gzckp0REY2WkQ2REM4b05nbzMtcXc9PQ==",
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}
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