MXNet 时间序列示例 - 在本地运行时出现 Dropout 错误

Question

I am looking into using MXNet LSTM modelling for time-series analysis for a problem i am currently working on.

As a way of understanding how to implement this, I am following the example code given by xnNet from the link: https://mxnet.incubator.apache.org/tutorials/r/MultidimLstm.html

When running this script after downloading the necessary data to my local source, i am able to execute the code fine until i get to the following section to train the model:

## train the network
system.time(model <- mx.model.buckets(symbol = symbol, 
                                  train.data = train.data, 
                                  eval.data = eval.data,
                                  num.round = 100, 
                                  ctx = ctx, 
                                  verbose = TRUE, 
                                  metric = mx.metric.mse.seq, 
                                  initializer = initializer,
                                  optimizer = optimizer, 
                                  batch.end.callback = NULL, 
                                  epoch.end.callback = epoch.end.callback))

When running this section, the following error occurs once gaining connection to the API.

 Error in mx.nd.internal.as.array(nd) : 
 [14:22:53] c:\jenkins\workspace\mxnet\mxnet\src\operator\./rnn-inl.h:359: 
 Check failed: param_.p == 0 (0.2 vs. 0) Dropout is not supported at the moment. 

Is there currently a problem internally within the XNNet R package which is unable to run this code? I can't imagine they would provide a tutorial example for the package that is not executable.

My other thought is that it is something to do with my local device execution and connection to the API. I haven't been able to find any information about this being a problem for other users though.

Any inputs or suggestions would be greatly appreciated thanks.

Answer 1

Looks like you're running an old version of R package. I think following instructions on this page to build a recent R-package should resolve this issue.