# clippedReluLayer

Clipped Rectified Linear Unit (ReLU) layer

## Description

A clipped ReLU layer performs a threshold operation, where any input value less than zero is set to zero and any value above the clipping ceiling is set to that clipping ceiling.

This operation is equivalent to:

`$f\left(x\right)=\left\{\begin{array}{ll}0,\hfill & x<0\hfill \\ x,\hfill & 0\le x`

This clipping prevents the output from becoming too large.

## Creation

### Syntax

``layer = clippedReluLayer(ceiling)``
``layer = clippedReluLayer(ceiling,'Name',Name)``

### Description

````layer = clippedReluLayer(ceiling)` returns a clipped ReLU layer with the clipping ceiling equal to `ceiling`.```

example

````layer = clippedReluLayer(ceiling,'Name',Name)` sets the optional `Name` property.```

## Properties

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### Clipped ReLU

Ceiling for input clipping, specified as a positive scalar.

Example: `10`

### Layer

Layer name, specified as a character vector or a string scalar. For `Layer` array input, the `trainNetwork`, `assembleNetwork`, `layerGraph`, and `dlnetwork` functions automatically assign names to layers with the name `''`.

Data Types: `char` | `string`

Number of inputs of the layer. This layer accepts a single input only.

Data Types: `double`

Input names of the layer. This layer accepts a single input only.

Data Types: `cell`

Number of outputs of the layer. This layer has a single output only.

Data Types: `double`

Output names of the layer. This layer has a single output only.

Data Types: `cell`

## Examples

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Create a clipped ReLU layer with the name `'clip1' `and the clipping ceiling equal to 10.

`layer = clippedReluLayer(10,'Name','clip1')`
```layer = ClippedReLULayer with properties: Name: 'clip1' Hyperparameters Ceiling: 10 ```

Include a clipped ReLU layer in a `Layer` array.

```layers = [ ... imageInputLayer([28 28 1]) convolution2dLayer(5,20) clippedReluLayer(10) maxPooling2dLayer(2,'Stride',2) fullyConnectedLayer(10) softmaxLayer classificationLayer]```
```layers = 7x1 Layer array with layers: 1 '' Image Input 28x28x1 images with 'zerocenter' normalization 2 '' 2-D Convolution 20 5x5 convolutions with stride [1 1] and padding [0 0 0 0] 3 '' Clipped ReLU Clipped ReLU with ceiling 10 4 '' 2-D Max Pooling 2x2 max pooling with stride [2 2] and padding [0 0 0 0] 5 '' Fully Connected 10 fully connected layer 6 '' Softmax softmax 7 '' Classification Output crossentropyex ```

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## References

[1] Hannun, Awni, Carl Case, Jared Casper, Bryan Catanzaro, Greg Diamos, Erich Elsen, Ryan Prenger, et al. "Deep speech: Scaling up end-to-end speech recognition." Preprint, submitted 17 Dec 2014. http://arxiv.org/abs/1412.5567

## Version History

Introduced in R2017b