 # Regression with nn.Sequential network fail

Hi there I was trying to use nn.Sequential module in a simple regression problem, just a line. The thing is that I print MSELoss every iteration and it is trying to converge at 1, intead of the values I am wondering.

Here is the code:

``````NN = nn.Sequential(
nn.Linear(1,3),
nn.Sigmoid(),
nn.Linear(3,4),
nn.Sigmoid(),
nn.Linear(4,7),
nn.Sigmoid(),
nn.Linear(7,1),
nn.Sigmoid()
)

criterion = nn.MSELoss()

X = torch.rand(200,1)
y = 2*X + 3

print(X.shape,y.shape)
for i in range(1000):
NN.train()
out = NN(X)
#print(out,y)
loss = criterion(y,out)
loss.backward()
optimizer.step()

print ("#" + str(i) + " Loss: " + str(loss.item()))
``````

If I try this to check the results:

``````newX = []
newY = []
for i in range(5):
r = random.uniform(0, 1)
newX.append(r)
newY.append(NN(torch.tensor([r])).item())
``````

I get `[0.9996629953384399, 0.9996600151062012, 0.9996635913848877, 0.9996637105941772, 0.9996615648269653]`.

I have tryed lot of things changing every on the network but don’t know what is happening.

Hi,

There is an issue with your model architecture, your target values are all greater than one (because of 2x+3 operation) and last operation in your network is `nn.Sigmoid()` which will return values in the range 0 to 1 so your model would never be able to converge.

Oh,I feel so silly its true. And what should I do, change the structure of the network or normalize inputs and outputs values?

Just remove last `nn.Sigmoid()` operation from your model.

That works. Thank you really much!!