title: Error with LSTM RNN in mxnet (R environment)
body: <p>I'm trying to setup a LSTM RNN by using mxnet in R, however, while trying to train my network I get this error and R is showing me a fatal error all the time: 
  <em>"[00:36:08] d:\program files (x86)\jenkins\workspace\mxnet\mxnet\src\operator\tensor./matrix_op-inl.h:155: Using target_shape will be deprecated.
  [00:36:08] d:\program files (x86)\jenkins\workspace\mxnet\mxnet\src\operator\tensor./matrix_op-inl.h:155: Using target_shape will be deprecated.
  [00:36:08] d:\program files (x86)\jenkins\workspace\mxnet\mxnet\src\operator\tensor./matrix_op-inl.h:155: Using target_shape will be deprecated."</em></p>
  
  <p>here is my code:</p>
  
  <pre><code># install.packages("drat", repos="https://cran.rstudio.com")
  # drat:::addRepo("dmlc")
  # install.packages("mxnet")
  
  rm(list = ls())
  require(mxnet)
  require(mlbench)
  
  inputData &lt;- read.table(file.path(getwd(), "Data", "input.csv"),
                       header = TRUE, sep = ",")
  inputData$X &lt;- as.Date(inputData$X)
  inputData &lt;- na.omit(inputData)
  
  index &lt;- 1:nrow(inputData)*0.8
  
  train.dates &lt;- inputData[index,1]
  test.dates &lt;- inputData[-index,1]
  inputData[,1] &lt;- NULL
  
  train &lt;- inputData[index,]
  test &lt;- inputData[-index,]
  
  train.x &lt;- data.matrix(train[,-ncol(train)])
  test.x &lt;- data.matrix(test[,-ncol(test)])
  
  train.y &lt;- train[,ncol(train)]
  test.y &lt;- test[,ncol(test)]
  
  get.label &lt;- function(X) {
    label &lt;- array(0, dim=dim(X))
    d &lt;- dim(X)[1]
    w &lt;- dim(X)[2]
    for (i in 0:(w-1)) {
      for (j in 1:d) {
        label[i*d+j] &lt;- X[(i*d+j)%%(w*d)+1]
      }
    }
    return (label)
  }
  
  X.train.label &lt;- get.label(t(train.x))
  X.val.label &lt;- get.label(t(test.x))
  
  X.train &lt;- list(data=t(train.x), label=X.train.label)
  X.val &lt;- list(data=t(test.x), label=X.val.label)
  
  batch.size = 1
  seq.len = 32
  num.hidden = 16
  num.embed = 16
  num.lstm.layer = 1
  num.round = 1
  learning.rate= 0.1
  wd=0.00001
  clip_gradient=1
  update.period = 1
  
  model &lt;- mx.lstm(X.train, X.val,
                   ctx=mx.cpu(),
                   num.round=num.round,
                   update.period=update.period,
                   num.lstm.layer=num.lstm.layer,
                   seq.len=seq.len,
                   num.hidden=num.hidden,
                   num.embed=num.embed,
                   num.label=15,
                   batch.size=batch.size,
                   input.size=15,
                   initializer=mx.init.uniform(0.1),
                   learning.rate=learning.rate,
                   wd=wd,
                   clip_gradient=clip_gradient)
  </code></pre>
  
  <p>Input dataset consists of Date column, 15 features, and the target value.
  Please hep me. Thanks in advance!</p>