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Conjugate gradient backpropagation with Powell-Beale restarts

`net.trainFcn = 'traincgb'[net,tr] = train(net,...)`

`traincgb` is a network training function that
updates weight and bias values according to the conjugate gradient
backpropagation with Powell-Beale restarts.

`net.trainFcn = 'traincgb'`

`[net,tr] = train(net,...)`

Training occurs according to `traincgb`'s
training parameters, shown here with their default values:

net.trainParam.epochs | 100 | Maximum number of epochs to train |

net.trainParam.show | 25 | Epochs between displays ( |

net.trainParam.showCommandLine | 0 | Generate command-line output |

net.trainParam.showWindow | 1 | Show training GUI |

net.trainParam.goal | 0 | Performance goal |

net.trainParam.time | inf | Maximum time to train in seconds |

net.trainParam.min_grad | 1e-6 | Minimum performance gradient |

net.trainParam.max_fail | 5 | Maximum validation failures |

net.trainParam.searchFcn | 'srchcha' | Name of line search routine to use |

Parameters related to line search methods (not all used for all methods):

net.trainParam.scal_tol | 20 | Divide into |

net.trainParam.alpha | 0.001 | Scale factor that determines sufficient reduction in |

net.trainParam.beta | 0.1 | Scale factor that determines sufficiently large step size |

net.trainParam.delta | 0.01 | Initial step size in interval location step |

net.trainParam.gama | 0.1 | Parameter to avoid small reductions in performance, usually
set to |

net.trainParam.low_lim | 0.1 | Lower limit on change in step size |

net.trainParam.up_lim | 0.5 | Upper limit on change in step size |

net.trainParam.maxstep | 100 | Maximum step length |

net.trainParam.minstep | 1.0e-6 | Minimum step length |

net.trainParam.bmax | 26 | Maximum step size |

You can create a standard network that uses `traincgb` with `feedforwardnet` or `cascadeforwardnet`.

To prepare a custom network to be trained with `traincgb`,

In either case, calling `train` with the resulting
network trains the network with `traincgb`.

Here a neural network is trained to predict median house prices.

[x,t] = house_dataset; net = feedforwardnet(10,'traincgb'); net = train(net,x,t); y = net(x)

For all conjugate gradient algorithms, the search direction is periodically reset to the negative of the gradient. The standard reset point occurs when the number of iterations is equal to the number of network parameters (weights and biases), but there are other reset methods that can improve the efficiency of training. One such reset method was proposed by Powell [Powe77], based on an earlier version proposed by Beale [Beal72]. This technique restarts if there is very little orthogonality left between the current gradient and the previous gradient. This is tested with the following inequality:

If this condition is satisfied, the search direction is reset to the negative of the gradient.

The `traincgb` routine has somewhat better
performance than `traincgp` for some problems, although
performance on any given problem is difficult to predict. The storage
requirements for the Powell-Beale algorithm (six vectors) are slightly
larger than for Polak-Ribiére (four vectors).

Powell, M.J.D., "Restart procedures for the conjugate
gradient method," *Mathematical Programming*,
Vol. 12, 1977, pp. 241–254

`trainbfg` | `traincgf` | `traincgp` | `traingda` | `traingdm` | `traingdx` | `trainlm` | `trainoss` | `trainscg`

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