Pytorch nn.Linear different output for same input
For learning purposes, I'm trying to build a simple perceptron with pytorch which should not be trained but just give the output for set weights. Here's the code:
import torch.nn
from torch import tensor

class Net(torch.nn.Module):
    def __init__(self):
        super(Net, self).__init__()
        self.fc1 = torch.nn.Linear(3,1)
        self.relu = torch.nn.ReLU()
        # force weights to equal one
        with torch.no_grad():
            self.fc1.weight = torch.nn.Parameter(torch.ones_like(self.fc1.weight))

    def forward(self, x):
        x = self.fc1(x)
        output = self.relu(x)
        return output

net = Net()
test_tensor = tensor([1, 1, 1])
print(net(test_tensor.float()).item())

I expect this single layer neural network to output 3. And that is roughly(!) the output for every execution, but it ranges from 2.5 to 3.5. Where does randomness enter the model?


Q: Where does randomness enter the model?

It comes from the bias init. As you can see here, the bias is not initialized to zero as you expected.
You can fix it this way:
import torch
from torch import nn

class Net(torch.nn.Module):
    def __init__(self):
        super(Net, self).__init__()
        self.fc1 = torch.nn.Linear(3,1)
        self.relu = torch.nn.ReLU()
        # force weights to equal one
        with torch.no_grad():
            torch.nn.init.ones_(self.fc1.weight)
            torch.nn.init.zeros_(self.fc1.bias)

    def forward(self, x):
        x = self.fc1(x)
        output = self.relu(x)
        return output

x = torch.tensor([1., 1., 1.])
Net()(x)
# &gt;&gt;&gt; tensor([3.], grad_fn=&lt;ReluBackward0&gt;)

