keras_mlp: Simple interface to MLP models via keras

In parsnip: A Common API to Modeling and Analysis Functions

Simple interface to MLP models via keras

Description

Instead of building a keras model sequentially, keras_mlp can be used to create a feedforward network with a single hidden layer. Regularization is via either weight decay or dropout.

Usage

keras_mlp(
  x,
  y,
  hidden_units = 5,
  penalty = 0,
  dropout = 0,
  epochs = 20,
  activation = "softmax",
  seeds = sample.int(10^5, size = 3),
  ...
)

Arguments

x	A data frame or matrix of predictors
y	A vector (factor or numeric) or matrix (numeric) of outcome data.
hidden_units	An integer for the number of hidden units.
penalty	A non-negative real number for the amount of weight decay. Either this parameter or dropout can specified.
dropout	The proportion of parameters to set to zero. Either this parameter or penalty can specified.
epochs	An integer for the number of passes through the data.
activation	A character string for the type of activation function between layers.
seeds	A vector of three positive integers to control randomness of the calculations.
...	Additional named arguments to pass to keras::compile() or keras::fit(). Arguments will be sorted and passed to either function internally.

Value

A keras model object.

parsnip documentation built on June 24, 2024, 5:14 p.m.