build_feature_NN: Build and compile a single Neural Network

In neuralGAM: Interpretable Neural Network Based on Generalized Additive Models

Build and compile a single Neural Network

Description

Builds and compiles a neural network using the keras library. The architecture of the neural network is configurable using the

Usage

build_feature_NN(
  num_units,
  learning_rate = 0.001,
  activation = "relu",
  kernel_initializer = "glorot_normal",
  kernel_regularizer = NULL,
  bias_regularizer = NULL,
  bias_initializer = "zeros",
  activity_regularizer = NULL,
  loss = "mean_squared_error",
  name = NULL,
  ...
)

Arguments

num_units	Defines the architecture of each neural network. If a scalar value is provided, a single hidden layer neural network with that number of units is used. If a vector of values is provided, a multi-layer neural network with each element of the vector defining the number of hidden units on each hidden layer is used.
learning_rate	Learning rate for the neural network optimizer.
activation	Activation function of the neural network. Defaults to relu
kernel_initializer	Kernel initializer for the Dense layers. Defaults to Xavier Initializer (glorot_normal).
kernel_regularizer	Optional regularizer function applied to the kernel weights matrix.
bias_regularizer	Optional regularizer function applied to the bias vector.
bias_initializer	Optional initializer for the bias vector.
activity_regularizer	Optional regularizer function applied to the output of the layer
loss	Loss function to use during neural network training. Defaults to the mean squared error.
name	Neural Network name.
...	Arguments passed on to neuralGAM formula An object of class "formula": a description of the model to be fitted. You can add smooth terms using s(). data A data frame containing the model response variable and covariates required by the formula. Additional terms not present in the formula will be ignored. family This is a family object specifying the distribution and link to use for fitting. By default, it is "gaussian" but also works to "binomial" for logistic regression. bf_threshold Convergence criterion of the backfitting algorithm. Defaults to 0.001 ls_threshold Convergence criterion of the local scoring algorithm. Defaults to 0.1 max_iter_backfitting An integer with the maximum number of iterations of the backfitting algorithm. Defaults to 10. max_iter_ls An integer with the maximum number of iterations of the local scoring Algorithm. Defaults to 10. w_train Optional sample weights seed A positive integer which specifies the random number generator seed for algorithms dependent on randomization. verbose Verbosity mode (0 = silent, 1 = print messages). Defaults to 1.

Value

compiled Neural Network

Author(s)

Ines Ortega-Fernandez, Marta Sestelo.

References

Kingma, D. P., & Ba, J. (2014). Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980.

neuralGAM documentation built on June 22, 2024, 6:55 p.m.