Github user felixcheung commented on a diff in the pull request:

    https://github.com/apache/spark/pull/15051#discussion_r79303689
  
    --- Diff: R/pkg/R/mllib.R ---
    @@ -694,8 +694,14 @@ setMethod("predict", signature(object = "KMeansModel"),
     #' }
     #' @note spark.mlp since 2.1.0
     setMethod("spark.mlp", signature(data = "SparkDataFrame"),
    -          function(data, blockSize = 128, layers = c(3, 5, 2), solver = 
"l-bfgs", maxIter = 100,
    -                   tol = 0.5, stepSize = 1, seed = 1) {
    +          function(data, layers, blockSize = 128, solver = "l-bfgs", 
maxIter = 100,
    +                   tol = 1E-6, stepSize = 0.03, seed = 0x7FFFFFFF) {
    +            if (length(layers) <= 1) {
    +              stop("layers vector require length > 0.")
    +            }
    +            if (any(sapply(layers, function(e) as.integer(e) != e))) {
    --- End diff --
    
    This way `layers = c(1.0, 2.0)` would pass the `as.integer(e) != e` test.
    One possible issue is with how we are handling this on the Scala side. 
Since we are passing it as `as.array(layers)`, this could end up as double in 
JVM - would it handle that correctly?
    
    There are other ways to do this but generally coercing to integer is a 
reasonable approach.
    
    One alternative implementation is this:
    ```
    layers <- as.integer(na.omit(layers))
    if (length(layers) <= 1) {
        stop("layers vector must be integer values with length > 0.")
    }
    ```



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