[GitHub] jeremiedb commented on issue #12431: [R] use of mx.io.arrayiter completely crashes R environment

[GitBox <gi...@apache.org>]

jeremiedb commented on issue #12431: [R] use of mx.io.arrayiter completely crashes R environment
URL: https://github.com/apache/incubator-mxnet/issues/12431#issuecomment-423410481
 
 
   I think the issue comes from the evaluation metric. The following code include a modified rmse metric that flatten the pred and label vector. I think it's bug that the eval metric fails when the predictions are not in a flat setting, I'll open a PR to get it fixed. 
   
   ```
   data.A <- mx.nd.random.normal(shape = c(3,3,1,10))
   data.A.2 <- mx.nd.random.normal(shape = c(3,3,1,10))
   
   batch_size <- 5
   
   train_iter = mx.io.arrayiter(data = as.array(data.A),
                                label = as.array(data.A.2),
                                batch.size = batch_size)
   
   data <- mx.symbol.Variable('data')
   label <- mx.symbol.Variable('label')
   
   conv_1 <- mx.symbol.Convolution(data= data, kernel = c(1,1), num_filter = 4, name="conv_1")
   conv_act_1 <- mx.symbol.Activation(data= conv_1, act_type = "relu", name="conv_act_1")
   flat <- mx.symbol.flatten(data = conv_act_1,  name="flatten")
   fcl_1 <- mx.symbol.FullyConnected(data = flat, num_hidden = 9, name="fc_1")
   fcl_2 <- mx.symbol.reshape(fcl_1, shape=c(3, 3, 1, batch_size))
   NN_Model <- mx.symbol.LinearRegressionOutput(data=fcl_2 , label=label, name="lro")
   
   fcl_2$infer.shape(list(data = c(3,3,1,batch_size)))
   NN_Model$infer.shape(list(data = c(3,3,1,batch_size)))
   
   mx.metric.rmse <- mx.metric.custom("rmse", function(label, pred) {
     pred <- mx.nd.reshape(pred, shape = -1)
     label <- mx.nd.reshape(label, shape = -1)
     res <- mx.nd.sqrt(mx.nd.mean(mx.nd.square(label-pred)))
     return(as.array(res))
   })
   
   mx.set.seed(99)
   autoencoder <- mx.model.FeedForward.create(
     NN_Model, 
     X = train_iter, 
     initializer = mx.init.uniform(0.01),
     ctx=mx.cpu(), 
     num.round=5,
     eval.metric = mx.metric.rmse, 
     optimizer = mx.opt.create("sgd"),
     verbose = TRUE)
   
   ```

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