hello all i am a beginner in deep learning and pytorch. I have made sequential model in pytorch like code below.
input shape : (1934,1024)
expected output shape : (1934,8)
batch size = 32
when i train my model and check the output the size turn out to be (14,8). How i can have my output size as my expected size
class MLP(nn.Module):
    def __init__(self):
        super(MLP, self).__init__()
        self.layers = nn.Sequential(
            nn.Linear(1024, 100),
            nn.Sigmoid(),
            nn.Linear(100, 8),
            #tempsigmoid()
            TempSigmoid()
            )
        
    def forward(self, x):
        x = self.layers(x)
        return x

this is how i train my model
# Model TRAINING
epochs = 5
train_losses=[]
elapsed=[]

for epochs in range(epochs):
    t0 = time.time()
    model.train()
    for batch_idx, (x,y) in enumerate(loader):
        x,y = x.to(device), y.to(device)
        optimizer.zero_grad()
        outputs = model(x)
        loss = loss_fn(outputs,y)
        loss.backward()
        optimizer.step()
        StopatLossValue()
        
        # program timing
        torch.cuda.synchronize() #the right way to measure time in pytorch
        t1 = time.time()
        eta = t1-t0
        
        if batch_idx % 100 == 0:
            print('Train Epoch: {} \t Loss: {:.6f} \t Elapsed time :{:.2f} ms'.format(epochs, loss.item(), (eta*1000)))
            elapsed.append(float(eta))
            np.savetxt("eta.csv", elapsed, delimiter=",")