Separius:

class Classifier(nn.Module):
def init(self):
super(Classifier,self).init()
self.net = nn.Sequential(nn.Linear(features.shape[1],50), nn.ReLU(), nn.Linear(50, 25), nn.ReLU(), nn.Linear(25, 2))
def forward(self, x)
return self.net(x)


class Classifier(nn.Module):
    def __init__(self):
        
        super(Classifier,self).__init__()
        self.net = nn.Sequential(nn.Linear(features.shape[1],50), nn.ReLU(), nn.Linear(50, 25), nn.ReLU(), nn.Linear(25, 2))
    def forward(self, x):
        return self.net(x)
data = classifier(features)
# data
tensor([[  9.5584,  44.1885],
        [ 14.0994,  43.1741],
        [ 12.7882,  38.1094],
        ...,
        [  9.4893,  24.7330],
        [ 13.7303,  40.0211],
        [  2.3035,   5.1796]])


I do not understand, the whole point of softmax is make the output as a probability but in the above case the values are exceeding 1?
Calculating loss with crossentropy gives a value of 7.8?