title: MxNet: label_shapes don't match names specified by label_names
body: <p>I wrote a script to do the classification of a single input image using a model I trained with MxNet. To classify the incoming image I feedforward them in through network.</p>
  
  <p>In short here is what I am doing:</p>
  
  <pre><code>symbol, arg_params, aux_params = mx.model.load_checkpoint('model-prefix', 42)
  model = mx.mod.Module(symbol=symbol, context=mx.cpu())
  model.bind(data_shapes=[('data', (1, 3, 224, 244))], for_training=False)
  model.set_params(arg_params, aux_params)
  
  # ... loading the image &amp; resizing ...
  # img is the image to classify as numpy array of shape (3, 244, 244)
  
  Batch = namedtuple('Batch', ['data'])
  self._model.forward(Batch(data=[mx.nd.array(img)]))
  probabilities = self._model.get_outputs()[0].asnumpy()
  
  print(str(probabilities))
  </code></pre>
  
  <p>This works fine, except that I am getting the following warning</p>
  
  <pre><code>UserWarning: Data provided by label_shapes don't match names specified by label_names ([] vs. ['softmax_label'])
  </code></pre>
  
  <p><strong>What should I change to avoid getting this warning?</strong> It is not clear to me what the <em>label_shapes</em> and <em>label_names</em> parameters are meant for, and what I am expect to fill them with. </p>
  
  <p><em>Note: I found some thread about them, but none enabled me to solve the problem. Similarly the MxNet documentation doesn't provide much details on what those parameters are and on how they are supposed to be filled.</em> </p>