fix mx.nd.load

R-package/R/ndarray.R

@@ -12,12 +12,7 @@
 #' @export
 mx.nd.load <- function(filename) {
   filename <- path.expand(filename)
-  ndarray <- mx.nd.internal.load(filename)
-  if (length(ndarray) == 1) {
-    return(ndarray[[1]])
-  } else {
-    return(ndarray)
-  }
+  return(mx.nd.internal.load(filename))
 }
 
 #' Save an mx.nd.array object
@@ -30,7 +25,7 @@ mx.nd.load <- function(filename) {
 #' mx.nd.save(mat, 'temp.mat')
 #' mat2 = mx.nd.load('temp.mat')
 #' as.array(mat)
-#' as.array(mat2)
+#' as.array(mat2[[1]])
 #'
 #' @export
 mx.nd.save <- function(ndarray, filename) {

R-package/tests/testthat/test_ndarray.R

@@ -44,6 +44,6 @@ test_that("ndarray ones, zeros, save and load", {
   mat = mx.nd.array(1:20)
   mx.nd.save(mat, 'temp.mat')
   mat2 = mx.nd.load('temp.mat')
-  expect_true(is.mx.ndarray(mat2))
-  expect_equal(as.array(mat), as.array(mat2))
+  expect_true(is.mx.ndarray(mat2[[1]]))
+  expect_equal(as.array(mat), as.array(mat2[[1]]))
 })

R-package/vignettes/classifyRealImageWithPretrainedModel.Rmd

@@ -41,7 +41,7 @@ model = mx.model.load("Inception/Inception_BN", iteration=39)
 We also need to load in the mean image, which is used for preprocessing using ```mx.nd.load```.
 
 ```{r}
-mean.img = as.array(mx.nd.load("Inception/mean_224.nd"))
+mean.img = as.array(mx.nd.load("Inception/mean_224.nd")[["mean_img"]])
 ```
 
 Load and Preprocess the Image

R-package/vignettes/ndarrayAndSymbolTutorial.Rmd

@@ -90,7 +90,7 @@ You can also load it back easily:
 
 ```{r}
 a <- mx.nd.load("temp.ndarray")
-as.array(a)
+as.array(a[[1]])
 ```
 
 In case you want to save data to the distributed file system such as S3 and HDFS,