# Multilayer Perceptron Classification Model

`spark.mlp.Rd`

`spark.mlp`

fits a multi-layer perceptron neural network model against a SparkDataFrame.
Users can call `summary`

to print a summary of the fitted model, `predict`

to make
predictions on new data, and `write.ml`

/`read.ml`

to save/load fitted models.
Only categorical data is supported.
For more details, see
Multilayer Perceptron

## Usage

```
spark.mlp(data, formula, ...)
# S4 method for class 'SparkDataFrame,formula'
spark.mlp(
data,
formula,
layers,
blockSize = 128,
solver = "l-bfgs",
maxIter = 100,
tol = 1e-06,
stepSize = 0.03,
seed = NULL,
initialWeights = NULL,
handleInvalid = c("error", "keep", "skip")
)
# S4 method for class 'MultilayerPerceptronClassificationModel'
summary(object)
# S4 method for class 'MultilayerPerceptronClassificationModel'
predict(object, newData)
# S4 method for class 'MultilayerPerceptronClassificationModel,character'
write.ml(object, path, overwrite = FALSE)
```

## Arguments

- data
a

`SparkDataFrame`

of observations and labels for model fitting.- formula
a symbolic description of the model to be fitted. Currently only a few formula operators are supported, including '~', '.', ':', '+', and '-'.

- ...
additional arguments passed to the method.

- layers
integer vector containing the number of nodes for each layer.

- blockSize
blockSize parameter.

- solver
solver parameter, supported options: "gd" (minibatch gradient descent) or "l-bfgs".

- maxIter
maximum iteration number.

- tol
convergence tolerance of iterations.

- stepSize
stepSize parameter.

- seed
seed parameter for weights initialization.

- initialWeights
initialWeights parameter for weights initialization, it should be a numeric vector.

- handleInvalid
How to handle invalid data (unseen labels or NULL values) in features and label column of string type. Supported options: "skip" (filter out rows with invalid data), "error" (throw an error), "keep" (put invalid data in a special additional bucket, at index numLabels). Default is "error".

- object
a Multilayer Perceptron Classification Model fitted by

`spark.mlp`

- newData
a SparkDataFrame for testing.

- path
the directory where the model is saved.

- overwrite
overwrites or not if the output path already exists. Default is FALSE which means throw exception if the output path exists.

## Value

`spark.mlp`

returns a fitted Multilayer Perceptron Classification Model.

`summary`

returns summary information of the fitted model, which is a list.
The list includes `numOfInputs`

(number of inputs), `numOfOutputs`

(number of outputs), `layers`

(array of layer sizes including input
and output layers), and `weights`

(the weights of layers).
For `weights`

, it is a numeric vector with length equal to the expected
given the architecture (i.e., for 8-10-2 network, 112 connection weights).

`predict`

returns a SparkDataFrame containing predicted labeled in a column named
"prediction".

## Note

spark.mlp since 2.1.0

summary(MultilayerPerceptronClassificationModel) since 2.1.0

predict(MultilayerPerceptronClassificationModel) since 2.1.0

write.ml(MultilayerPerceptronClassificationModel, character) since 2.1.0

## Examples

```
if (FALSE) { # \dontrun{
df <- read.df("data/mllib/sample_multiclass_classification_data.txt", source = "libsvm")
# fit a Multilayer Perceptron Classification Model
model <- spark.mlp(df, label ~ features, blockSize = 128, layers = c(4, 3), solver = "l-bfgs",
maxIter = 100, tol = 0.5, stepSize = 1, seed = 1,
initialWeights = c(0, 0, 0, 0, 0, 5, 5, 5, 5, 5, 9, 9, 9, 9, 9))
# get the summary of the model
summary(model)
# make predictions
predictions <- predict(model, df)
# save and load the model
path <- "path/to/model"
write.ml(model, path)
savedModel <- read.ml(path)
summary(savedModel)
} # }
```