I was playing with an autoencoder. The decoder part is defined like this:
    self.decoder = torch.nn.Sequential(
        torch.nn.Linear(10, 125)
        , torch.nn.ReLU()
        , torch.nn.Linear(125, 250)
        , torch.nn.ReLU()
        , torch.nn.Linear(250, 500)
        , torch.nn.ReLU()
        , torch.nn.Linear(500, 1000)
        , torch.nn.ReLU()
        , torch.nn.Linear(1000, 28 * 28)

After training the network without problems, if I do:
myInput1 = torch.autograd.Variable( torch.Tensor([1,0,0,0,0,0,0,0,0,0]) )
myOutput1 = autoencoder.decoder(myInput1)
print(myOutput1.data - myOutput1.data)

I, obviously get all ceros.
But if I predict the exact same input again in the network and substract the two outputs:
myOutput2 = autoencoder.decoder(myInput1)
print(myOutput1.data - myOutput2.data)

I get small numbers but not ceros.
My question is, why there is a different result?, why is it not deterministic if the network it’s not changing between executions?
This is all in the same machine without training the network again.
Obviously, I’m missing something here. Thanks in advance.