Description Usage Arguments Author(s) Examples

Training single or mutiple hidden layers neural network by BP

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`x` |
matrix of x values for examples |

`y` |
vector or matrix of target values for examples |

`initW` |
initial weights. If missing chosen at random |

`initB` |
initial bias. If missing chosen at random |

`hidden` |
vector for number of units of hidden layers.Default is c(10). |

`activationfun` |
activation function of hidden unit.Can be "sigm","linear" or "tanh".Default is "sigm" for logistic function |

`learningrate` |
learning rate for gradient descent. Default is 0.8. |

`momentum` |
momentum for gradient descent. Default is 0.5 . |

`learningrate_scale` |
learning rate will be mutiplied by this scale after every iteration. Default is 1 . |

`output` |
function of output unit, can be "sigm","linear" or "softmax". Default is "sigm". |

`numepochs` |
number of iteration for samples Default is 3. |

`batchsize` |
size of mini-batch. Default is 100. |

`hidden_dropout` |
drop out fraction for hidden layer. Default is 0. |

`visible_dropout` |
drop out fraction for input layer Default is 0. |

Xiao Rong

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