Create setting for multi-resolution CovNN model (stucture based on https://arxiv.org/pdf/1608.00647.pdf CNN1)

setCovNN(batchSize = 1000, outcomeWeight = 1, lr = 1e-05, decay = 1e-06,
dropout = 0, epochs = 10, filters = 3, kernelSize = 10,
loss = "binary_crossentropy", seed = NULL)

## Arguments

batchSize The number of samples to used in each batch during model training The weight assined to the outcome (make greater than 1 to reduce unballanced label issue) The learning rate The decay of the learning rate [currently not used] the dropout rate for regularisation The number of times data is used to train the model (e.g., epoches=1 means data only used once to train) The number of columns output by each convolution The number of time dimensions used for each convolution The loss function implemented The random seed

## Examples

# NOT RUN {
model.CovNN <- setCovNN()
# }