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.
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()
orkeras::fit()
. Arguments will be sorted and passed to either function internally.