I have 14 classification blocks each one is: 1024 → 1.
I wish to concatenate the 14 results to form a 1-D output as if I had 1024 → 14 classes.
prior_layers =  for l in LABELS: prior_layers.append(LinearAttentionPriorBlock(l, normalize_attention=normalize_attention))
then in the forward pass I would have something like:
res =  for l in LABELS: res.append(LinearAttentionPriorBlock(x))
and each entry int
res will be batchX1 is it possible to get the desired results as if it was standard classification or the only way is to output 14 values from the forward pass.