Hi,
I am a newbie to PyTorch. I was trying out the following network architecture to train a multi-class classifier. I used Softmax at the output layer and cross entropy as the loss function. However, the output doesn’t look like probabilities. For example, one of the outputs looks like this [2.0032e-10, 1.798e-8, …1.0000e+0,…2.112e-4]. My question is, how can one of them be 1 when their sum has to be equal to 1.
class Net(nn.Module):

    def __init__(self):
        super(Net, self).__init__()
        # 13 features in input layer
        self.fc1 = nn.Linear(13, 512)
        self.fc2 = nn.Linear(512, 512)
        self.fc3 = nn.Linear(512, 512)
        self.fc4 = nn.Linear(512, 512)
        self.fc5 = nn.Linear(512, 40)
        self.bn1 = nn.BatchNorm1d(512)
        self.bn2 = nn.BatchNorm1d(512)
        self.bn3 = nn.BatchNorm1d(512)
        self.bn4 = nn.BatchNorm1d(512)
        

    def forward(self, x):
        x = self.bn1(F.relu(self.fc1(x)))
        x = self.bn2(F.relu(self.fc2(x)))
        x = self.bn3(F.relu(self.fc3(x)))
        x = self.bn4(F.relu(self.fc4(x)))
        x = nn.Softmax(dim=1)(self.fc5(x))
        return x
    

Please correct me if I am wrong and help me out.