abp | Create a multi-layer perceptron network using a global adaptive training step length |
-di <i-data> | name of the input data frame for training |
-do <o-data> | name of the target data frame for training |
-net <nlayers> <nneu1> ... <nneuN> | network configuration, number of layers, number of neurons in each layer |
[-types <s | t | l> ... <s | t | l>] | use a sigmoid, tanh or linear network in each layer, default is sigmoid |
[-ti <ti-data>] | input data frame for test set |
[-to <to-data>] | target data frame for test set |
[-vi <vi-data>] | input data frame for a validation set |
[-vo <vo-data>] | target data frame for a validation set |
-nout <wdata> | data frame for saving the trained network weights |
[-ef <edata>] | output error to a frame |
[-bs <tstep>] | training step length (default is 0.01) |
[-em <epochs>] | maximum number of training epochs (default is 200) |
[-mdn <mdown>] | training step multiplier downwards (default is 0.8) |
[-mup <mup>] | training step multiplier upwards (default is 1.1) |
[-ac <adapt-cr>] | adaption criterion (default is 1.01) |
[-one] | forces one neuron / one input |
This command trains a backpropagation network using a Matlab style training algorithm. This method is based on a global adaptive learning rate parameter. By default one neuron for each input is used.
Example (ex5.8): Train a three-layer (input + hidden + output layer) MLP network with sine data using sigmoid activation functions in the neurons. After training, save the network output.
NDA> load sin.dat NDA> select sinx -f sin.x NDA> select siny -f sin.y NDA> abp -di sinx -do siny -net 3 1 10 1 -types s s s -em 350 -nout wei -ef virhe -bs 1.2 -ac 1.04 -mup 1.1 -mdn 0.8 NDA> fbp -d sinx -dout out -win wei NDA> select output -f sin.x out.0 NDA> save output