x = torch.randn(1, 100)
criterion = nn.NLLLoss()
# binary label
y = np.array([1])
# if u have K classes the target label will be made out of interegers between 0 and K-1
# in your case since you have 2 classes lables will be 0 and 1
y = torch.from_numpy(y)
y=y.long()
f1=nn.Sequential(nn.Linear(100,10),nn.ReLU(),nn.Linear(10,2),nn.LogSoftmax(dim=1))
predict=f1(x)
loss = criterion(predict,y)
print(loss)