Hi,
at the last 2 lines how does it work? as in why do we need to exp?
i thought we can just model(img)
and we will obtain the result?
because when we model(img).size() the output is torch.Size([1, 10]) which is the 10 categories and the highest is the predicted result?
Thanks

model = nn.Sequential(nn.Linear(784, 128),
nn.ReLU(),
nn.Linear(128, 64),
nn.ReLU(),
nn.Linear(64, 10),
nn.LogSoftmax(dim=1))
criterion = nn.NLLLoss()
optimizer = optim.SGD(model.parameters(), lr=0.003)


snip some codes -


images, labels = next(iter(trainloader))
img = images[0].view(1, 784)
Turn off gradients to speed up this part
with torch.no_grad():
logps = model(img) #input an image after re-shaped

Output of the network are log-probabilities, need to take exponential for probabilities
ps = torch.exp(logps) # <-- same effect
ps = F.softmax(logps,dim=0) <–same effect
view_classify(img.view(1, 28, 28), ps)