# Struct fann::CascadeParams
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[src]

pub struct CascadeParams { pub output_change_fraction: c_float, pub output_stagnation_epochs: c_uint, pub candidate_change_fraction: c_float, pub candidate_stagnation_epochs: c_uint, pub candidate_limit: fann_type, pub weight_multiplier: fann_type, pub max_out_epochs: c_uint, pub max_cand_epochs: c_uint, pub activation_functions: Vec<ActivationFunc>, pub activation_steepnesses: Vec<fann_type>, pub num_candidate_groups: c_uint, }

Parameters for cascade training.

## Fields

`output_change_fraction` | A number between 0 and 1 determining how large a fraction the mean square error should
change within This means: If the MSE does not change by a fraction of |

`output_stagnation_epochs` | The number of epochs training is allowed to continue without changing the MSE by a fraction
of at least |

`candidate_change_fraction` | A number between 0 and 1 determining how large a fraction the mean square error should
change within This means: If the MSE does not change by a fraction of |

`candidate_stagnation_epochs` | The number of epochs training is allowed to continue without changing the MSE by a fraction
of |

`candidate_limit` | A limit for how much the candidate neuron may be trained. It limits the ratio between the MSE and the candidate score. |

`weight_multiplier` | Multiplier for the weight of the candidate neuron before adding it to the network. Usually between 0 and 1, to make training less aggressive. |

`max_out_epochs` | The maximum number of epochs the output connections may be trained after adding a new candidate neuron. |

`max_cand_epochs` | The maximum number of epochs the input connections to the candidates may be trained before adding a new candidate neuron. |

`activation_functions` | The activation functions for the candidate neurons. |

`activation_steepnesses` | The activation function steepness values for the candidate neurons. |

`num_candidate_groups` | The number of candidate neurons to be trained for each combination of activation function and steepness. |

## Methods

`impl CascadeParams`

`fn get_num_candidates(&self) -> c_uint`

The number of candidates used during training: the number of combinations of activation
functions and steepnesses, times `num_candidate_groups`

.

For every combination of activation function and steepness, `num_candidate_groups`

such
neurons, with different initial weights, are trained.