| 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 <nlay> <nn1> ... <nnN> | 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 |
| [-penalty <penalty>] | regularization coefficient |
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
