I’m trying to build a neural network that can generate label score for classification and use the NLLLoss2d function.

I have 1000 samples, each sample is a vector of 180 entries. Thus the input is a 1000x100 matrix. For each sample in the input, I am trying to generate two 3x5 matrices, containing scores for the two labels. Thus the output should be a 1000x2x3x5 tensor as described in the doc of NLLLoss2d function.

In the network, I used an array of nn.Linear() functions:

class Net(torch.nn.Module):
def __init__(self):
super(Net, self).__init__()
self.linear = [[torch.nn.Linear(100, 2) for i in range(5)] for j in range(3)]
def forward(self, x):
y = [[[0,0] for i in range(5)] for j in range(3)]
for i in range(3):
for j in range(5):
y[i][j]=torch.nn.functional.log_softmax(nnFunc.relu(self.linear[i][j](x)))
return y

However, the output of the network is a list of dimension 3x5x1000x2. The first two dimensions are of list type, and the last two dimensions are of Variable type.

I am trying to permutate the tensor I got from the network, but I’m not sure it could be used later by the backward-propagation function.

Thanks for your reply. I tried using permute in the model, but it showed the error

there are no graph nodes that require computing gradients.

The network I was using is

class Net(torch.nn.Module):
def __init__(self):
super(Net, self).__init__()
self.linear = [[torch.nn.Linear(100, 2) for i in range(5)] for j in range(3)]
def forward(self, x):
N = len(x)
y = torch.Tensor([[np.zeros((N,2)) for i in range(5)] for j in range(3)])
for i in range(3):
for j in range(5):
y[i][j]=torch.nn.functional.log_softmax(nnFunc.relu(self.linear[i][j](x))).data
return Variable(y.permute(2,3,0,1))

I have to put Variable() wrapper there, otherwise it would show the error

process. This means the gradient the code calculated is zero. Do you think this is to do with how I got the return value y which somehow makes the optimizer treat it as a constant?

Enlightened by this thread, I found the solution, which is to use the torch.stack() function. So to get the output y, I did y = torch.stack([torch.stack([nnFunc.log_softmax(nnFunc.relu(self.linear[i][j](x))) for j,m in enumerate(l)],0) for i,l in enumerate(self.linear)],1)