Surprisingly I have not found an answer to this question after looking around the internet. I am specifically interested in a 3d tensor. From doing my own experiments, I have found that when I create a tensor:
h=torch.randn(5,12,5)
And then put a convolutional layer on it defined as follows:
conv=torch.nn.Conv1d(12,48,3,padding=1)
The output is a (5,48,5) tensor. So, am I correct in assuming that for a 3d tensor in pytorch the middle number represents the number of channels?
Edit: It seems that when running a conv2d, the input dimension is the first entry in the tensor, and I need to make it a 4d tensor (1,48,5,5) for example. Now I am very confused…
Any help is much appreciated! (I also posteed this question on SO, but no one has answered and it’s sort of urgent