title: can't predict in mxnet 0.94 for R
body: <p>I have been able to use nnet and neuralnet to predict values in a conventional backprop network, but have been strugling to do the same with MXNET and R for many reasons.</p>
  
  <p>This is the file (simple CSV with headers, columns have been normalized)
  <a href="https://files.fm/u/cfhf3zka" rel="nofollow noreferrer">https://files.fm/u/cfhf3zka</a></p>
  
  <p>And this is the code I use:</p>
  
  <pre><code>filedata &lt;- read.csv("example.csv")
  
  require(mxnet)
  
  datain &lt;- filedata[,1:3]
  dataout &lt;- filedata[,4]
  
  lcinm &lt;- data.matrix(datain, rownames.force = "NA")
  lcoutm &lt;- data.matrix(dataout, rownames.force = "NA")
  lcouta &lt;- as.numeric(lcoutm)
  
  data &lt;- mx.symbol.Variable("data")
  fc1 &lt;- mx.symbol.FullyConnected(data, name="fc1", num_hidden=3)
  act1 &lt;- mx.symbol.Activation(fc1, name="sigm1", act_type="sigmoid")
  fc2 &lt;- mx.symbol.FullyConnected(act1, name="fc2", num_hidden=3)
  act2 &lt;- mx.symbol.Activation(fc2, name="sigm2", act_type="sigmoid")
  fc3 &lt;- mx.symbol.FullyConnected(act2, name="fc3", num_hidden=3)
  act3 &lt;- mx.symbol.Activation(fc3, name="sigm3", act_type="sigmoid")
  fc4 &lt;- mx.symbol.FullyConnected(act3, name="fc4", num_hidden=1)
  softmax &lt;- mx.symbol.LogisticRegressionOutput(fc4, name="softmax")
  
  mx.set.seed(0)
  mxn &lt;- mx.model.FeedForward.create(array.layout = "rowmajor", softmax, X = lcinm, y = lcouta, learning.rate=0.01, eval.metric=mx.metric.rmse)
  
  preds &lt;- predict(mxn, lcinm)
  
  predsa &lt;-array(preds)
  
  predsa
  </code></pre>
  
  <p>The console output is: </p>
  
  <pre><code>Start training with 1 devices
  [1] Train-rmse=0.0852988247858687
  [2] Train-rmse=0.068769514264606
  [3] Train-rmse=0.0687647380075881
  [4] Train-rmse=0.0687647164103567
  [5] Train-rmse=0.0687647161066822
  [6] Train-rmse=0.0687647160828069
  [7] Train-rmse=0.0687647161241598
  [8] Train-rmse=0.0687647160882147
  [9] Train-rmse=0.0687647160594508
  [10] Train-rmse=0.068764716079949
  &gt; preds &lt;- predict(mxn, lcinm)
  Warning message:
  In mx.model.select.layout.predict(X, model) :
    Auto detect layout of input matrix, use rowmajor..
  
  &gt; predsa &lt;-array(preds)
  &gt; predsa
     [1] 0.6776764 0.6776764 0.6776764 0.6776764 0.6776764 0.6776764 0.6776764 0.6776764 0.6776764
    [10] 0.6776764 0.6776764 0.6776764 0.6776764 0.6776764 0.6776764 0.6776764 0.6776764 0.6776764
  </code></pre>
  
  <p>So it gets an "average" but is not able to predict values, have tried other ways and learningrates to avoid overprediction but have never reached an even variable output.</p>