PyTorch RNN gives accuracy 0.0

Question

I tried to run the following code but the output is [0.0, 0.0]. I suppose this shld not happen, but cannot seem to figure out what could be causing this issue. Any idea what could have gone wrong? Appreciate any help, thanks

def load_array(data_arrays, batch_size, is_train=True): 
    """Construct a PyTorch data iterator."""
    dataset = data.TensorDataset(*data_arrays)
    return data.DataLoader(dataset, batch_size, shuffle=is_train)

data_iter = load_array((train_x, train_y), 1)

class extractlastcell(nn.Module):
  def forward(self,x):
    out , _ = x
    return out[:, -1, :]

net= nn.Sequential(
    nn.Embedding(5000, 256), 
    nn.LSTM(256, 32), 
    extractlastcell(),
    nn.Linear(32, 16), nn.Softmax())

def init_weights(m): 
    if type(m) == nn.Linear:
        nn.init.normal_(m.weight, std=0.01)

net.apply(init_weights)

def train_model(train_dl, model, epoch):
    train_ls = []
    # define the optimization
    loss = nn.BCELoss(reduction='none')
    trainer = torch.optim.Adam(net.parameters(), lr=0.1)
    # enumerate epochs
    for epoch in range(epoch):
        # enumerate mini batches
        for i, (inputs, targets) in enumerate(train_dl):
            # clear the gradients
            trainer.zero_grad()
            # compute the model output
            yhat = model(inputs)
            # calculate loss
            l = loss(yhat, targets)
            # credit assignment
            l.sum().backward()
            # update model weights
            trainer.step()
        
        train_ls.append(d2l.evaluate_accuracy(net, train_dl))
    return train_ls

train_model(data_iter,net,2)