Neural Network Toolbox™

Sum net input function

Syntax

• N = netsum({Z1,Z2,...,Zn},FP)
  dN_dZj = netsum('dz',j,Z,N,FP)
  info = netsum(code)
  

Description

netsum is a net input function. Net input functions calculate a layer's net input by combining its weighted inputs and biases.

netsum({Z1,Z2,...,Zn},FP) takes Z1 to Zn and optional function parameters,

Zi	S x Q matrices in a row cell array
FP	Row cell array of function parameters (ignored)

and returns the elementwise sum of Z1 to Zn.

netsum('dz',j,{Z1,...,Zn},N,FP) returns the derivative of N with respect to Zj. If FP is not supplied, the default values are used. If N is not supplied or is [], it is calculated for you.

netsum('name') returns the name of this function.

netsum('type') returns the type of this function.

netsum('fpnames') returns the names of the function parameters.

netsum('fpdefaults') returns default function parameter values.

netsum('fpcheck', FP) throws an error for illegal function parameters.

netsum('fullderiv') returns 0 or 1, depending on whether the derivative is S x Q or N x S x Q.

Examples

Here netsum combines two sets of weighted input vectors and a bias. You must use concur to make B the same dimensions as Z1 and Z2.

• z1 = [1 2 4; 3 4 1}
  z2 = [-1 2 2; -5 -6 1]
  b = [0; -1]
  n = netsum({z1,z2,concur(b,3)})
  

Assign this net input function to layer i of a network.

• net.layers(i).netFcn = 'compet';
  

Use newp or newlin to create a standard network that uses netsum.

See Also

netprod, netinv

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