`R/CovNN.R`

`setCovNN.Rd`

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)

batchSize | The number of samples to used in each batch during model training |
---|---|

outcomeWeight | The weight assined to the outcome (make greater than 1 to reduce unballanced label issue) |

lr | The learning rate |

decay | The decay of the learning rate |

dropout | [currently not used] the dropout rate for regularisation |

epochs | The number of times data is used to train the model (e.g., epoches=1 means data only used once to train) |

filters | The number of columns output by each convolution |

kernelSize | The number of time dimensions used for each convolution |

loss | The loss function implemented |

seed | The random seed |

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