Here I have three questions.
By chance I saw the code here 5.
What puzzles me is the class Net(nn.Module) and the loss function:
    def __init__(self):
        super(Net, self).__init__()
        self.fc1 = nn.Linear(13, 128)
        self.fc2 = nn.Linear(128, 128)
        self.fc3 = nn.Linear(128, 128)
        self.fc4 = nn.Linear(128, 128)
        self.fc5 = nn.Linear(128, 128)
        self.fc6 = nn.Linear(128, 2)

    def forward(self, x):
        x = F.relu(self.fc1(x)) # ReLU: max(x, 0)
        x = F.relu(self.fc2(x))
        x = F.relu(self.fc3(x))
        x = F.relu(self.fc4(x))
        x = F.relu(self.fc5(x))
        x = self.fc6(x)
        return F.log_softmax(x, dim=0)

criterion = nn.CrossEntropyLoss()
output = model(train_x)
loss = criterion(output, train_y)
loss.backward()
optimizer.step()

My first question is, F.log_softmax (x, dim = 0) shouldn’t be used here, is my understanding right?
As discussed in Using nn.CrossEntropyLoss(), how can I get softmax output? 3,

nn.CrossEntropyLoss() automatically apply logSoftmax using FC layer output.

The model class should be:
    def __init__(self):
        super(Net, self).__init__()
        self.fc1 = nn.Linear(13, 128)
        self.fc2 = nn.Linear(128, 128)
        self.fc3 = nn.Linear(128, 128)
        self.fc4 = nn.Linear(128, 128)
        self.fc5 = nn.Linear(128, 128)
        self.fc6 = nn.Linear(128, 2)

    def forward(self, x):
        x = F.relu(self.fc1(x)) # ReLU: max(x, 0)
        x = F.relu(self.fc2(x))
        x = F.relu(self.fc3(x))
        x = F.relu(self.fc4(x))
        x = F.relu(self.fc5(x))
        x = self.fc6(x)
        return x

My second question is, F.log_softmax() is used for what?
My third question is, whether F.log_softmax() is used or not, the model performance is about 90%. Why is this happening?
Finally, I should use F.log_softmax() or I should NOT use F.log_softmax()?