Spark ML -- Multilayer Perceptron

Creates and trains multilayer perceptron on a Spark DataFrame.

ml_multilayer_perceptron(x, response, features, layers, iter.max = 100,
  seed = sample(.Machine$integer.max, 1), ml.options = ml_options(), ...)

Arguments

x	An object coercable to a Spark DataFrame (typically, a `tbl_spark`).
response	The name of the response vector (as a length-one character vector), or a formula, giving a symbolic description of the model to be fitted. When `response` is a formula, it is used in preference to other parameters to set the `response`, `features`, and `intercept` parameters (if available). Currently, only simple linear combinations of existing parameters is supposed; e.g. `response ~ feature1 + feature2 + ...`. The intercept term can be omitted by using `- 1` in the model fit.
features	The name of features (terms) to use for the model fit.
layers	A numeric vector describing the layers -- each element in the vector gives the size of a layer. For example, `c(4, 5, 2)` would imply three layers, with an input (feature) layer of size 4, an intermediate layer of size 5, and an output (class) layer of size 2.
iter.max	The maximum number of iterations to use.
seed	A random seed. Set this value if you need your results to be reproducible across repeated calls.
ml.options	Optional arguments, used to affect the model generated. See `ml_options` for more details.
...	Optional arguments. The `data` argument can be used to specify the data to be used when `x` is a formula; this allows calls of the form `ml_linear_regression(y ~ x, data = tbl)`, and is especially useful in conjunction with `do`.