文件名称:rpe
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Train a two layer neural network with a recursive prediction error
％ algorithm ("recursive Gauss-Newton"). Also pruned (i.e., not fully
％ connected) networks can be trained.
％
％ The activation functions can either be linear or tanh. The network
％ architecture is defined by the matrix NetDef , which has of two
％ rows. The first row specifies the hidden layer while the second
％ specifies the output layer.-Train a two layer neural network with a recursive prediction error algorithm ( recursive Gauss-Newton ). Also pruned (ie, not fully connected) networks can be trained. The activation functions can either be linear or tanh. The network architecture is defined by the matrix NetDef, which has of two rows. The first row specifies the hidden layer while the second specifies the output layer.
％ algorithm ("recursive Gauss-Newton"). Also pruned (i.e., not fully
％ connected) networks can be trained.
％
％ The activation functions can either be linear or tanh. The network
％ architecture is defined by the matrix NetDef , which has of two
％ rows. The first row specifies the hidden layer while the second
％ specifies the output layer.-Train a two layer neural network with a recursive prediction error algorithm ( recursive Gauss-Newton ). Also pruned (ie, not fully connected) networks can be trained. The activation functions can either be linear or tanh. The network architecture is defined by the matrix NetDef, which has of two rows. The first row specifies the hidden layer while the second specifies the output layer.
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