I am using mxnet to train a VQA model, the input is `(6244,)`

vector and the output is a single label

During my epoch, the loss never change but the accuracy is oscillating in a small range, the first 5 epochs are

```
Epoch 1. Loss: 2.7262569132562255, Train_acc 0.06867348986554285
Epoch 2. Loss: 2.7262569132562255, Train_acc 0.06955649207304837
Epoch 3. Loss: 2.7262569132562255, Train_acc 0.06853301224162152
Epoch 4. Loss: 2.7262569132562255, Train_acc 0.06799116997792494
Epoch 5. Loss: 2.7262569132562255, Train_acc 0.06887417218543046
```

This is a multi-class classification problem, with each answer label stands for a class, so I use softmax as final layer and cross-entropy to evaluate the loss, the code of them are as follows

So why the loss never change?.. I just directly get if from `cross_entropy`

```
trainer = gluon.Trainer(net.collect_params(), 'sgd', {'learning_rate': 0.01})
loss = gluon.loss.SoftmaxCrossEntropyLoss()
epochs = 10
moving_loss = 0.
best_eva = 0
for e in range(epochs):
for i, batch in enumerate(data_train):
data1 = batch.data[0].as_in_context(ctx)
data2 = batch.data[1].as_in_context(ctx)
data = [data1, data2]
label = batch.label[0].as_in_context(ctx)
with autograd.record():
output = net(data)
cross_entropy = loss(output, label)
cross_entropy.backward()
trainer.step(data[0].shape[0])
moving_loss = np.mean(cross_entropy.asnumpy()[0])
train_accuracy = evaluate_accuracy(data_train, net)
print("Epoch %s. Loss: %s, Train_acc %s" % (e, moving_loss, train_accuracy))
```

The eval function is as follows

```
def evaluate_accuracy(data_iterator, net, ctx=mx.cpu()):
numerator = 0.
denominator = 0.
metric = mx.metric.Accuracy()
data_iterator.reset()
for i, batch in enumerate(data_iterator):
with autograd.record():
data1 = batch.data[0].as_in_context(ctx)
data2 = batch.data[1].as_in_context(ctx)
data = [data1, data2]
label = batch.label[0].as_in_context(ctx)
output = net(data)
metric.update([label], [output])
return metric.get()[1]
```