spark.mlp {SparkR} | R Documentation |
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
spark.mlp(data, formula, ...) ## S4 method for signature 'SparkDataFrame,formula' spark.mlp(data, formula, layers, blockSize = 128, solver = "l-bfgs", maxIter = 100, tol = 1e-06, stepSize = 0.03, seed = NULL, initialWeights = NULL) ## S4 method for signature 'MultilayerPerceptronClassificationModel' predict(object, newData) ## S4 method for signature 'MultilayerPerceptronClassificationModel' summary(object) ## S4 method for signature 'MultilayerPerceptronClassificationModel,character' write.ml(object, path, overwrite = FALSE)
data |
a |
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. |
object |
a Multilayer Perceptron Classification Model fitted by |
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. |
spark.mlp
returns a fitted Multilayer Perceptron Classification Model.
predict
returns a SparkDataFrame containing predicted labeled in a column named
"prediction".
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).
spark.mlp since 2.1.0
predict(MultilayerPerceptronClassificationModel) since 2.1.0
summary(MultilayerPerceptronClassificationModel) since 2.1.0
write.ml(MultilayerPerceptronClassificationModel, character) since 2.1.0
## Not run:
##D df <- read.df("data/mllib/sample_multiclass_classification_data.txt", source = "libsvm")
##D
##D # fit a Multilayer Perceptron Classification Model
##D model <- spark.mlp(df, label ~ features, blockSize = 128, layers = c(4, 3), solver = "l-bfgs",
##D maxIter = 100, tol = 0.5, stepSize = 1, seed = 1,
##D initialWeights = c(0, 0, 0, 0, 0, 5, 5, 5, 5, 5, 9, 9, 9, 9, 9))
##D
##D # get the summary of the model
##D summary(model)
##D
##D # make predictions
##D predictions <- predict(model, df)
##D
##D # save and load the model
##D path <- "path/to/model"
##D write.ml(model, path)
##D savedModel <- read.ml(path)
##D summary(savedModel)
## End(Not run)