Running the code below causes the entire R environment to terminate. This code used to work on older versions of mxnet (older than 1.2.0). It seems to be related to mx.io.arrayiter

Environment info (Required)

R version 3.5.0 (2018-04-23)
Platform: x86_64-w64-mingw32/x64 (64-bit)
Running under: Windows >= 8 x64 (build 9200)

Matrix products: default

locale:
[1] LC_COLLATE=English_United States.1252 LC_CTYPE=English_United States.1252
[3] LC_MONETARY=English_United States.1252 LC_NUMERIC=C
[5] LC_TIME=English_United States.1252

attached base packages:
[1] stats graphics grDevices utils datasets methods base

other attached packages:
[1] mxnet_1.3.0

loaded via a namespace (and not attached):
[1] Rcpp_0.12.16 pillar_1.2.2 compiler_3.5.0 RColorBrewer_1.1-2 influenceR_0.1.0 plyr_1.8.4
[7] bindr_0.1.1 viridis_0.5.1 tools_3.5.0 digest_0.6.15 jsonlite_1.5 viridisLite_0.3.0
[13] tibble_1.4.2 gtable_0.2.0 rgexf_0.15.3 pkgconfig_2.0.1 rlang_0.2.0 igraph_1.2.1
[19] rstudioapi_0.7 yaml_2.1.19 bindrcpp_0.2.2 gridExtra_2.3 downloader_0.4 DiagrammeR_1.0.0
[25] dplyr_0.7.4 stringr_1.3.1 htmlwidgets_1.2 hms_0.4.2 grid_3.5.0 glue_1.2.0
[31] R6_2.2.2 Rook_1.1-1 XML_3.98-1.11 readr_1.1.1 purrr_0.2.4 tidyr_0.8.0
[37] ggplot2_2.2.1 magrittr_1.5 codetools_0.2-15 scales_0.5.0 htmltools_0.3.6 assertthat_0.2.0
[43] colorspace_1.3-2 brew_1.0-6 stringi_1.2.2 visNetwork_2.0.3 lazyeval_0.2.1 munsell_0.4.3

Build info (Required if built from source)

https://github.com/apache/incubator-mxnet/tree/master/R-package

cran <- getOption("repos")
cran["dmlc"] <- "https://apache-mxnet.s3-accelerate.dualstack.amazonaws.com/R/CRAN/"
options(repos = cran)
install.packages("mxnet")

Error Message:

R environment crashes.

Minimum reproducible example

data.A = read.csv("./matty_inv/A.csv", header = FALSE)
data.A.2 = read.csv("./matty_inv/A_2.csv", header = FALSE)

data.A <- as.matrix(data.A)
data.A.2 <- as.matrix(data.A.2)

dim(data.A) <- c(3,3,1,10)
dim(data.A.2) <- c(3,3,1,10)

train_iter = mx.io.arrayiter(data = data.A,
                             label = data.A.2,
                             batch.size = 1)

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")

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=n.rounds, array.batch.size=batch_size,
    learning.rate=8e-3, array.layout = "rowmajor",
    eval.metric = mx.metric.rmse, optimizer = "adam",
    verbose = TRUE)