Hi,
when we checked your code, FIERY uses zero vector as a noise in the evaluation. This way, the model uses only the mean vectors for generating samples. In order to generate samples, we changed the this line from:
sample = mu + sigma * noise to sample = mu + sigma * torch.randn_like(noise) to ensure the model uses a gaussian noise vector. However, the results were the same as the previous ones. We thought there might be a problem in the sigma part of the model and print the sigma values. When we examine the sigma values the values were very close to 1 (1 ± 0.05), which means no standard deviation for the distributions.
Is there any other way to generate samples? Can you clarify how you sampled different future predictions with your model?
Thanks in advance
Hi, when we checked your code, FIERY uses zero vector as a noise in the evaluation. This way, the model uses only the mean vectors for generating samples. In order to generate samples, we changed the this line from:
sample = mu + sigma * noisetosample = mu + sigma * torch.randn_like(noise)to ensure the model uses a gaussian noise vector. However, the results were the same as the previous ones. We thought there might be a problem in the sigma part of the model and print the sigma values. When we examine the sigma values the values were very close to 1 (1 ± 0.05), which means no standard deviation for the distributions.Is there any other way to generate samples? Can you clarify how you sampled different future predictions with your model? Thanks in advance