RNN is not training (PyTorch)
I can't get what I am doing wrong when training RNN. I am trying to train RNN for AND operation on sequences (to learn how it works on simple task).
But my network is not learning, loss stays the same and it can't event overfit the model.
Can you please help me to find the problem? 

Data I am using: 

data = [
    [1, 1, 1, 1, 0, 0, 1, 1, 1],
    [1, 1, 1, 1],
    [0, 0, 1, 1],
    [0, 0, 0, 0, 0, 0, 0],
    [1, 1, 1, 1, 1, 1, 1],
    [1, 1],
    [0],
    [1],
    [1, 0]]
labels = [
    0,
    1, 
    0, 
    0,
    1,
    1,
    0,
    1,
    0
]


Code for NN:

class AndRNN(nn.Module):
def __init__(self):
    super(AndRNN, self).__init__()
    self.rnn = nn.RNN(1, 10, 5)
    self.fc = nn.Sequential(
        nn.Linear(10, 30),
        nn.Linear(30, 2)
    )

def forward(self, input, hidden):
    x, hidden = self.rnn(input, hidden)
    x = self.fc(x[-1])        
    return x, hidden

def initHidden(self):
    return Variable(torch.zeros((5, 1, 10)))


Training loop: 

criterion = torch.nn.CrossEntropyLoss()
optimizer = torch.optim.SGD(net.parameters(), lr=0.001, momentum=0.9)

correct = 0
for e in range(20):
for i in range(len(data)):
    tensor = torch.FloatTensor(data[i]).view(-1, 1, 1)
    label = torch.LongTensor([labels[i]])
    hidden = net.initHidden()
    optimizer.zero_grad()

    out, hidden = net(Variable(tensor), Variable(hidden.data))

    _, l = torch.topk(out, 1)
    if label[0] == l[0].data[0]:
        correct += 1

    loss = criterion(out, Variable(label))
    loss.backward()
    optimizer.step()

    print("Loss ", loss.data[0], "Accuracy ", (correct / (i + 1)))


Shape for tensor will be (sequence_len, 1 (which is batch size), 1), that is correct according to the PyTorch docs for RNN

The problem is this line:

out, hidden = net(Variable(tensor), Variable(hidden.data))


It should be simply

out, hidden = net(Variable(tensor), hidden)


By having Variable(hidden.data) here you are creating a new hidden_state variable (with all zeros) at very step instead of passing the hidden state from the previous state. 

I tried your example, and changed the optimizer to Adam. There are the complete code. 

class AndRNN(nn.Module):
    def __init__(self):
        super(AndRNN, self).__init__()
        self.rnn = nn.RNN(1, 10, 5)
        self.fc = nn.Sequential(
            nn.Linear(10, 30),
            nn.Linear(30, 2)
        )

    def forward(self, input, hidden):
        x, hidden = self.rnn(input, hidden)
        x = self.fc(x[-1])        
        return x, hidden

    def initHidden(self):
        return Variable(torch.zeros((5, 1, 10)))

net = AndRNN()    
criterion = torch.nn.CrossEntropyLoss()
optimizer = torch.optim.Adam(net.parameters())

correct = 0
for e in range(100):
    for i in range(len(data)):
        tensor = torch.FloatTensor(data[i]).view(-1, 1, 1)
        label = torch.LongTensor([labels[i]])
        hidden = net.initHidden()
        optimizer.zero_grad()

        out, hidden = net(Variable(tensor), hidden)

        loss = criterion(out, Variable(label))
        loss.backward()
        optimizer.step()
    if e % 25 == 0:
        print("Loss ", loss.data[0])


Result 

Loss  0.6370733976364136
Loss  0.25336754322052
Loss  0.006924811284989119
Loss  0.002351854695007205

