Hi,
I am very new to Pytorch and this is my first post here, so I apologise if the question is very straightforward.
My problem is that I have defined class net1  and initialised it randomly with a manual seed.
random.seed(opt.manualSeed)
torch.manual_seed(opt.manualSeed)
if torch.cuda.is_available():
    torch.cuda.manual_seed_all(opt.manualSeed)    

class net1(nn.Module):
    def __init__(self):
        super(net1, self).__init__()
        self.main_body = nn.Sequential(
            nn.Conv2d(in_channels=3, out_channels=96, kernel_size=11, stride=4, padding=0),
            # rest of the network...#
        )
    def forward(self, x):
        output = self.main_body(x)
        return output

# custom weights initialization called on net1
def weights_init(m):
    classname = m.__class__.__name__
    if classname.find('Conv') != -1:
        m.weight.data.normal_(0.0, 0.02)
    elif classname.find('BatchNorm') != -1:
        m.weight.data.normal_(1.0, 0.02)
        m.bias.data.fill_(0)

net1_ = net1()
net1_.apply(weights_init)

However, when I add another class net2 to the code:
class net2(nn.Module):
    def __init__(self):
        super(net2, self).__init__()
        self.main_body = nn.Sequential(
            nn.Conv2d(4096, 1, 1)
            nn.Sigmoid()
        )
    def forward(self, x):
        output = self.main_body(x)
        return output.view(-1, 1).squeeze(1)

net2_ = net2()

and instantiate it, even though I do not use it anywhere else and it is not connected to my main graph (which is built on net1_), I get different outputs from my graph.
Is it a reasonable outcome?