R: Spark ML - Multilayer Perceptron

ml_multilayer_perceptron_classifier {sparklyr}

R Documentation

Spark ML – Multilayer Perceptron

Description

Classification model based on the Multilayer Perceptron. Each layer has sigmoid activation function, output layer has softmax.

Usage

ml_multilayer_perceptron_classifier(x, formula = NULL, layers,
  max_iter = 100L, step_size = 0.03, tol = 1e-06, block_size = 128L,
  solver = "l-bfgs", seed = NULL, initial_weights = NULL,
  features_col = "features", label_col = "label",
  prediction_col = "prediction",
  uid = random_string("multilayer_perceptron_classifier_"), ...)

ml_multilayer_perceptron(x, formula = NULL, layers, max_iter = 100L,
  step_size = 0.03, tol = 1e-06, block_size = 128L, solver = "l-bfgs",
  seed = NULL, initial_weights = NULL, features_col = "features",
  label_col = "label", prediction_col = "prediction",
  uid = random_string("multilayer_perceptron_classifier_"), response = NULL,
  features = NULL, ...)

Arguments

`x`	A `spark_connection`, `ml_pipeline`, or a `tbl_spark`.
`formula`	Used when `x` is a `tbl_spark`. R formula as a character string or a formula. This is used to transform the input dataframe before fitting, see ft_r_formula for details.
`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.
`max_iter`	The maximum number of iterations to use.
`step_size`	Step size to be used for each iteration of optimization (> 0).
`tol`	Param for the convergence tolerance for iterative algorithms.
`block_size`	Block size for stacking input data in matrices to speed up the computation. Data is stacked within partitions. If block size is more than remaining data in a partition then it is adjusted to the size of this data. Recommended size is between 10 and 1000. Default: 128
`solver`	The solver algorithm for optimization. Supported options: "gd" (minibatch gradient descent) or "l-bfgs". Default: "l-bfgs"
`seed`	A random seed. Set this value if you need your results to be reproducible across repeated calls.
`initial_weights`	The initial weights of the model.
`features_col`	Features column name, as a length-one character vector. The column should be single vector column of numeric values. Usually this column is output by `ft_r_formula`.
`label_col`	Label column name. The column should be a numeric column. Usually this column is output by `ft_r_formula`.
`prediction_col`	Prediction column name.
`uid`	A character string used to uniquely identify the ML estimator.
`...`	Optional arguments; see Details.
`response`	(Deprecated) The name of the response column (as a length-one character vector.)
`features`	(Deprecated) The name of features (terms) to use for the model fit.

Details

When x is a tbl_spark and formula (alternatively, response and features) is specified, the function returns a ml_model object wrapping a ml_pipeline_model which contains data pre-processing transformers, the ML predictor, and, for classification models, a post-processing transformer that converts predictions into class labels. For classification, an optional argument predicted_label_col (defaults to "predicted_label") can be used to specify the name of the predicted label column. In addition to the fitted ml_pipeline_model, ml_model objects also contain a ml_pipeline object where the ML predictor stage is an estimator ready to be fit against data. This is utilized by ml_save with type = "pipeline" to faciliate model refresh workflows.

ml_multilayer_perceptron() is an alias for ml_multilayer_perceptron_classifier() for backwards compatibility.

Value

The object returned depends on the class of x.