parsnip: keras_mlp – R documentation

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keras_mlp

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.
`...`	Currently ignored.

Value

A keras model object.

parsnip

A Common API to Modeling and Analysis Functions

v0.1.5

GPL-2

Authors

Max Kuhn [aut, cre], Davis Vaughan [aut], RStudio [cph]

Initial release