Neural Net


Synopsis

Learns a neural net from the input data.


Description

This operator learns a model by means of a feed-forward neural network trained by a backpropagation algorithm (multi-layer perceptron). The user can define the structure of the neural network with the parameter list "hidden_layers". Each list entry describes a new hidden layer. The key of each entry must correspond to the layer name. The value of each entry must be a number defining the size of the hidden layer. A size value of -1 indicates that the layer size should be calculated from the number of attributes of the input example set. In this case, the layer size will be set to (number of attributes + number of classes) / 2 + 1.

If the user does not specify any hidden layers, a default hidden layer with sigmoid type and size (number of attributes + number of classes) / 2 + 1 will be created and added to the net. If only a single layer without nodes is specified, the input nodes are directly connected to the output nodes and no hidden layer will be used.

The used activation function is the usual sigmoid function. Therefore, the values ranges of the attributes should be scaled to -1 and +1. This is also done by this operator if not specified otherwise by the corresponding parameter setting. The type of the output node is sigmoid if the learning data describes a classification task and linear for numerical regression tasks.


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ExampleProcess