Hello,
I’ve read nn.Module with multiple inputs 25, and this is more of a follow-up question here. I’m curious if there is a way to concatenate a tensor in the middle of sequential. Or in the middle of a model, for example:
def __init__(self, z_dim=10, nc=1,stim=np.random.randint(13,size=8)):
    super(BetaVAE_H_wStim, self).__init__()
    torch.backends.cudnn.benchmark = True
    self.z_dim = z_dim
    self.nc = nc
    self.stim = stim
    self.encoder = nn.Sequential(
        nn.Conv3d(nc, 32, 3, (1,2,2), 1),          # B, 32, 4, 48, 48
        nn.ReLU(True),
        nn.Conv3d(32, 32, 3, (1,2,2), 1),          # B, 32, 2, 24, 24
        nn.ReLU(True),
        nn.Conv3d(32, 64, 3, 2, 1),            # B, 64, 1, 12, 12
        nn.ReLU(True),
        nn.Conv3d(64, 64, 3, 2, 1),            # B,  64,  6,  6
        nn.ReLU(True),
        nn.Conv3d(64, 256, 3, 2, 1),          # B,  64,  6,  6
        nn.ReLU(True),
        View((-1,256)),                               # B, 256
        # i want to concat here
        nn.Linear(256,z_dim*2),                # B, z_dim*2
    )

Where my comment is is where I’m hoping to concat a numpy array of size 8 (1 axis) to my tensor in the model. It’s different from the problem posted above. I actually want those values to be fully connected to the output of this model.
Is there an easy way to do this?