# Documentation

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# netsum

Sum net input function

## Syntax

```N = netsum({Z1,Z2,...,Zn},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.

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

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

and returns the elementwise sum of `Z1` to `Zn`.

`info = netsum('code')` returns information about this function. The following codes are supported:

`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`-by-`Q` or `N`-by-`S`-by-`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 the first layer of a network.

```net = feedforwardnet(); net.layers{1}.netInputFcn = 'netsum'; ```

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