# initzero

Zero weight and bias initialization function

## Syntax

`W = initzero(S,PR)b = initzero(S,[1 1])`

## Description

`W = initzero(S,PR)` takes two arguments,

 `S` Number of rows (neurons) `PR` `R`-by-`2` matrix of input value ranges = `[Pmin Pmax]`

and returns an `S`-by-`R` weight matrix of zeros.

`b = initzero(S,[1 1])` returns an `S`-by-`1` bias vector of zeros.

## Examples

Here initial weights and biases are calculated for a layer with two inputs ranging over `[0 1]` and ```[-2 2]``` and four neurons.

```W = initzero(5,[0 1; -2 2]) b = initzero(5,[1 1]) ```

## Network Use

You can create a standard network that uses `initzero` to initialize its weights by calling `newp` or `newlin`.

To prepare the weights and the bias of layer `i` of a custom network to be initialized with `midpoint`,

1. Set `net.initFcn` to `'initlay'`. (`net``.``initParam` automatically becomes `initlay`'s default parameters.)

2. Set `net.layers{i}.initFcn` to `'initwb'`.

3. Set each `net.inputWeights{i,j}.initFcn` to `'initzero'`.

4. Set each `net.layerWeights{i,j}.initFcn` to `'initzero'`.

5. Set each `net.biases{i}.initFcn` to `'initzero'`.

To initialize the network, call `init`.

See `help newp` and `help newlin` for initialization examples.