And what if I were to concatenate additional information of type continuous? How to proceed? Would the network be able to capture the additional information by concatenation? (For e.g 300 hidden neurons post convolution + 1 for the added info). I tried both this and expanding the 1D additional info, to let´s say, 10. I wasn´t very sucessfull in both. The VAE just ignores the added info.
I don’t understand. Should the one-hot encoded tensor be the output?
My question is still, how and where should these tensors be concatenated?
Since the number of elements is different, please refer to my previous post.
apologies,as i understand the needs of a conditional VAE, I need to concatate both the input in to the encoder [24,1,260,132] and the Z input into the decoder [24,100] with the one hot vector [24,6].
I can adjust the Z input by changing the latent dimension from 100 to 6 thats easy, however the input shape is fixed, this is the the problem… because otherwise the input becomes [10053, 6] (i realise its not an integer and this needs addressing if this is the only way forward…)
the input into the encoder is a 2d tensor which is fixed in size (260,132).
I don’t understand what you mean by increase the number of inputs. I have a batchsize of 24 so the input would be 24,1,260,132 and the one-hot vector is 24,6 (as there are 6 different types available)…
if i use:
inputs = torch.cat([data_in, labels], 1)
then data_in and labels have to be the same size, don’t they???
I am feeling really thick at the moment, i’m sure i’m missing a trick, I just cannot see it…