mmn: Train and evaluate a Multi-Modal Neural Network (MMN) model

In citoverse/cito: Building and Training Neural Networks

Train and evaluate a Multi-Modal Neural Network (MMN) model

Description

This function trains a Multi-Modal Neural Network (MMN) model on the provided data.

Usage

mmn(
  formula,
  dataList = NULL,
  fusion_hidden = c(50L, 50L),
  fusion_activation = c("relu", "leaky_relu", "tanh", "elu", "rrelu", "prelu",
    "softplus", "celu", "selu", "gelu", "relu6", "sigmoid", "softsign", "hardtanh",
    "tanhshrink", "softshrink", "hardshrink", "log_sigmoid"),
  fusion_bias = TRUE,
  fusion_dropout = 0,
  loss = c("mse", "mae", "softmax", "cross-entropy", "gaussian", "binomial", "poisson"),
  optimizer = c("sgd", "adam", "adadelta", "adagrad", "rmsprop", "rprop"),
  lr = 0.01,
  alpha = 0.5,
  lambda = 0,
  validation = 0,
  batchsize = 32L,
  burnin = 10,
  shuffle = TRUE,
  epochs = 100,
  early_stopping = NULL,
  lr_scheduler = NULL,
  custom_parameters = NULL,
  device = c("cpu", "cuda", "mps"),
  plot = TRUE,
  verbose = TRUE
)

Arguments

formula	A formula object specifying the model structure. See examples for more information
dataList	A list containing the data for training the model. The list should contain all variables used in the formula.
fusion_hidden	A numeric vector specifying the number of units in each hidden layer of the fusion network.
fusion_activation	A character vector specifying the activation function for each hidden layer of the fusion network. Available options are: "relu", "leaky_relu", "tanh", "elu", "rrelu", "prelu", "softplus", "celu", "selu", "gelu", "relu6", "sigmoid", "softsign", "hardtanh", "tanhshrink", "softshrink", "hardshrink", "log_sigmoid".
fusion_bias	A logical value or vector (length(fusion_hidden) + 1) indicating whether to include bias terms in the layers of the fusion network.
fusion_dropout	The dropout rate for the fusion network, a numeric value or vector (length(fusion_hidden)) between 0 and 1.
loss	The loss function to be optimized during training. Available options are: "mse", "mae", "softmax", "cross-entropy", "gaussian", "binomial", "poisson".
optimizer	The optimization algorithm to be used during training. Available options are: "sgd", "adam", "adadelta", "adagrad", "rmsprop", "rprop".
lr	The learning rate for the optimizer.
alpha	The alpha parameter for elastic net regularization. Should be a value between 0 and 1.
lambda	The lambda parameter for elastic net regularization. Should be a positive value.
validation	The proportion of the training data to use for validation. Should be a value between 0 and 1.
batchsize	The batch size used during training.
burnin	training is aborted if the trainings loss is not below the baseline loss after burnin epochs
shuffle	A logical indicating whether to shuffle the training data in each epoch.
epochs	The number of epochs to train the model.
early_stopping	If provided, the training will stop if the validation loss does not improve for the specified number of epochs. If set to NULL, early stopping is disabled.
lr_scheduler	Learning rate scheduler created with config_lr_scheduler
custom_parameters	A list of parameters used by custom loss functions. See vignette for examples.
device	The device on which to perform computations. Available options are: "cpu", "cuda", "mps".
plot	A logical indicating whether to plot training and validation loss curves.
verbose	A logical indicating whether to display verbose output during training.

Value

An object of class "citommn" containing the trained MMN model and other information.

See Also

predict.citommn, print.citommn, summary.citommn, continue_training, analyze_training

citoverse/cito documentation built on Jan. 16, 2025, 11:49 p.m.