# Why loss function always return zero after first epoch?

Why the loss function is always printing zero after the first epoch?

I suspect it’s because of `loss = loss_fn(outputs, torch.max(labels, 1)[1])`.

And if if I use `loss = loss_fn(outputs, torch.max(labels, 1)[0])`, I will get some values that are too high and I’m not sure if they make sense, like: 1200,800,600,500(one value for each epoc)

``````nepochs = 5

losses = np.zeros(nepochs)

loss_fn = nn.CrossEntropyLoss()

optimizer = optim.Adam(modell.parameters(), lr = 0.001)

for epoch in range(nepochs):

running_loss = 0.0
n = 0

#single batch
if(n == 1):
break;

inputs, labels = data

outputs = modell(inputs)

#loss = loss_fn(outputs, labels)
loss = loss_fn(outputs, torch.max(labels, 1)[1])
loss.backward()
optimizer.step()

running_loss += loss.item()
n += 1

losses[epoch] = running_loss / n
print(f"epoch: {epoch+1} loss: {losses[epoch] : .3f}")
``````

The model is:

``````def __init__(self, labels=10):
super(Classifier, self).__init__()
self.fc = nn.Linear(3 * 64 * 64, labels)

def forward(self, x):
out = x.reshape(x.size(0), -1)
out = self.fc (out)
return out
``````

the `labels` variable is a 64 tensor size like this
`tensor([[7],[1],[ 2],[3],[ 2],[9],[9],[8],[9],[8],[ 1],[7],[9],[2],[ 5],[1],[3],[3],[8],[3],[7],[1],[7],[9],[8],[ 8],[3],[7],[ 5],[ 1],[7],[3],[2],[1],[ 3],[3],[2],[0],[3],[4],[0],[7],[1],[ 8],[4],[1],[ 5],[ 3],[4],[3],[ 4],[8],[4],[1],[ 9],[7],[3],[ 2],[ 6],[4],[ 8],[3],[ 7],[3]])`

Your `labels` tensor seems to already contain class indices but has an additional unnecessary dimension.
The right approach would be to use `labels = labels.squeeze(1)` and pass it to the criterion.
Using `torch.max(labels, dim=1)[0]` would yield the same output.
However, `torch.max(labels, dim=1)[1]` would return the indices in `dim1` containing the max value, which would be a tensor full of zeros which is wrong and would thus explain the zero loss as your model would only learn to predict class0.