I am building a network which includes a bidirectional GRU layer. After getting all my dimensions to fit, I am now trying to use simple inputs to check that the network is actually doing as intended.  However, I can not seem to get the expected output from my GRU layer?
My code looks like this:


class Net(nn.Module):
    def __init__(self):
        super(Net, self).__init__()
        
        self.nHidden=30
        self.nFilter=20
        self.conv1 = nn.Conv2d(1,self.nFilter,(5, 5), stride=(1, 1), padding=(2, 2))
        self.birnn = nn.GRU(self.nFilter*129,self.nHidden,1,bidirectional=True,batch_first=True)

        
        #debugging:
        self.conv1.bias[:]=0
        self.birnn.bias_ih_l=0
        self.birnn.bias_hh_l=0
        # self.birnn.weight_ih_l=0
        # self.birnn.weight_hh_l=0
        
    def forward(self, x):
        
        #filtering:
        x = F.relu(self.conv1(x))

        #biGRU:
        x=x.permute((0,2,1,3))

        x=x.reshape((-1,29,20*129))
        print(x.size())
        print(torch.sum(x,(1,2)))
        
        x,hn = self.birnn(x)
        print(torch.sum(x,(1,2)))

        return x
    
net=Net()
x=torch.zeros(3,1,29,129)
x[1,:,2,:]=1
output=net(x)

and running it gives the following:
torch.Size([3, 29, 2580])
tensor([   0.0000, 1551.1537,    0.0000], grad_fn=<SumBackward1>)
tensor([-6.9812,  5.4175, -6.9812], grad_fn=<SumBackward1>)

my intention is that one data point is all 1’s, and the rest are zeros. As I understand nn.GRU, if inputs are 0 and bias’s are zero, shouldn’t outputs be zero too? So, my expectation was to have an output that was [0,K,0] (like the input was). Why is that not happening?
Thank you for reading