Spark ML -- Multilayer Perceptron
Classification model based on the Multilayer Perceptron. Each layer has sigmoid activation function, output layer has softmax.
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, ...)
ml_pipeline, or 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.
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.
The maximum number of iterations to use.
Step size to be used for each iteration of optimization (> 0).
Param for the convergence tolerance for iterative algorithms.
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
The solver algorithm for optimization. Supported options: "gd" (minibatch gradient descent) or "l-bfgs". Default: "l-bfgs"
A random seed. Set this value if you need your results to be reproducible across repeated calls.
The initial weights of the model.
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
Label column name. The column should be a numeric column. Usually this column is output by
Prediction column name.
A character string used to uniquely identify the ML estimator.
Optional arguments; see Details.
(Deprecated) The name of the response column (as a length-one character vector.)
(Deprecated) The name of features (terms) to use for the model fit.
x is a
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_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
type = "pipeline" to faciliate model refresh workflows.
ml_multilayer_perceptron() is an alias for
ml_multilayer_perceptron_classifier() for backwards compatibility.
The object returned depends on the class of
spark_connection, the function returns an instance of a
ml_predictorobject. The object contains a pointer to a Spark
Predictorobject and can be used to compose
ml_pipeline, the function returns a
ml_pipelinewith the predictor appended to the pipeline.
tbl_spark, a predictor is constructed then immediately fit with the input
tbl_spark, returning a prediction model.
formula: specified When
formulais specified, the input
tbl_sparkis first transformed using a
RFormulatransformer before being fit by the predictor. The object returned in this case is a
ml_modelwhich is a wrapper of a
See http://spark.apache.org/docs/latest/ml-classification-regression.html for more information on the set of supervised learning algorithms.
Other ml algorithms: