# imageInputLayer

Image input layer

## Description

An image input layer inputs 2-D images to a neural network and applies data normalization.

For 3-D image input, use `image3dInputLayer`

.

## Creation

### Description

returns an image input layer and specifies the `layer`

= imageInputLayer(`inputSize`

)`InputSize`

property.

sets the optional `layer`

= imageInputLayer(`inputSize`

,`Name,Value`

)`Normalization`

, `NormalizationDimension`

, `Mean`

, `StandardDeviation`

, `Min`

, `Max`

, `SplitComplexInputs`

, and `Name`

properties using one or more name-value
arguments. Enclose the property names in quotes.

## Properties

### Image Input

`InputSize`

— Size of the input

row vector of integers

This property is read-only.

Size of the input data, specified as a row vector of integers
`[h w c]`

, where `h`

,
`w`

, and `c`

correspond to the
height, width, and number of channels respectively.

For grayscale images, specify a vector with

`c`

equal to`1`

.For RGB images, specify a vector with

`c`

equal to`3`

.For multispectral or hyperspectral images, specify a vector with

`c`

equal to the number of channels.

For 3-D image or volume input, use `image3dInputLayer`

.

**Example: **
`[224 224 3]`

`Normalization`

— Data normalization

`'zerocenter'`

(default) | `'zscore'`

| `'rescale-symmetric'`

| `'rescale-zero-one'`

| `'none'`

| function handle

This property is read-only.

Data normalization to apply every time data is forward propagated through the input layer, specified as one of the following:

`'zerocenter'`

— Subtract the mean specified by`Mean`

.`'zscore'`

— Subtract the mean specified by`Mean`

and divide by`StandardDeviation`

.`'rescale-symmetric'`

— Rescale the input to be in the range [-1, 1] using the minimum and maximum values specified by`Min`

and`Max`

, respectively.`'rescale-zero-one'`

— Rescale the input to be in the range [0, 1] using the minimum and maximum values specified by`Min`

and`Max`

, respectively.`'none'`

— Do not normalize the input data.function handle — Normalize the data using the specified function. The function must be of the form

`Y = func(X)`

, where`X`

is the input data and the output`Y`

is the normalized data.

**Tip**

The software, by default, automatically calculates the normalization statistics when using the
`trainnet`

and `trainNetwork`

functions. To save time when
training, specify the required statistics for normalization and set the `ResetInputNormalization`

option in `trainingOptions`

to `0`

(`false`

).

`NormalizationDimension`

— Normalization dimension

`'auto'`

(default) | `'channel'`

| `'element'`

| `'all'`

Normalization dimension, specified as one of the following:

`'auto'`

– If the training option is`false`

and you specify any of the normalization statistics (`Mean`

,`StandardDeviation`

,`Min`

, or`Max`

), then normalize over the dimensions matching the statistics. Otherwise, recalculate the statistics at training time and apply channel-wise normalization.`'channel'`

– Channel-wise normalization.`'element'`

– Element-wise normalization.`'all'`

– Normalize all values using scalar statistics.

**Data Types: **`char`

| `string`

`Mean`

— Mean for zero-center and z-score normalization

`[]`

(default) | 3-D array | numeric scalar

Mean for zero-center and z-score normalization, specified as a
*h*-by-*w*-by-*c*
array, a 1-by-1-by-*c* array of means per channel, a
numeric scalar, or `[]`

, where *h*,
*w*, and *c* correspond to the
height, width, and the number of channels of the mean,
respectively.

If you specify the `Mean`

property, then `Normalization`

must be `'zerocenter'`

or
`'zscore'`

. If `Mean`

is
`[]`

, then the `trainnet`

and
`trainNetwork`

functions calculate the mean. To train a
`dlnetwork`

object using a custom training loop or assemble a network
without training it using the `assembleNetwork`

function, you must set
the `Mean`

property to a numeric scalar or a numeric array.

**Data Types: **`single`

| `double`

| `int8`

| `int16`

| `int32`

| `int64`

| `uint8`

| `uint16`

| `uint32`

| `uint64`

`StandardDeviation`

— Standard deviation for z-score normalization

`[]`

(default) | 3-D array | numeric scalar

Standard deviation for z-score normalization, specified as a
*h*-by-*w*-by-*c*
array, a 1-by-1-by-*c* array of means per channel, a
numeric scalar, or `[]`

, where *h*,
*w*, and *c* correspond to the
height, width, and the number of channels of the standard deviation,
respectively.

If you specify the `StandardDeviation`

property, then
`Normalization`

must be `'zscore'`

. If
`StandardDeviation`

is `[]`

, then the
`trainnet`

and `trainNetwork`

functions calculate
the standard deviation. To train a `dlnetwork`

object using a custom training
loop or assemble a network without training it using the
`assembleNetwork`

function, you must set the `StandardDeviation`

property to a numeric scalar or a numeric array.

**Data Types: **`single`

| `double`

| `int8`

| `int16`

| `int32`

| `int64`

| `uint8`

| `uint16`

| `uint32`

| `uint64`

`Min`

— Minimum value for rescaling

`[]`

(default) | 3-D array | numeric scalar

Minimum value for rescaling, specified as a
*h*-by-*w*-by-*c*
array, a 1-by-1-by-*c* array of minima per channel, a
numeric scalar, or `[]`

, where *h*,
*w*, and *c* correspond to the
height, width, and the number of channels of the minima,
respectively.

If you specify the `Min`

property, then `Normalization`

must be `'rescale-symmetric'`

or
`'rescale-zero-one'`

. If `Min`

is
`[]`

, then the `trainnet`

and
`trainNetwork`

functions calculate the minima. To train a
`dlnetwork`

object using a custom training loop or assemble a network
without training it using the `assembleNetwork`

function, you must set
the `Min`

property to a numeric scalar or a numeric array.

**Data Types: **`single`

| `double`

| `int8`

| `int16`

| `int32`

| `int64`

| `uint8`

| `uint16`

| `uint32`

| `uint64`

`Max`

— Maximum value for rescaling

`[]`

(default) | 3-D array | numeric scalar

Maximum value for rescaling, specified as a
*h*-by-*w*-by-*c*
array, a 1-by-1-by-*c* array of maxima per channel, a
numeric scalar, or `[]`

, where *h*,
*w*, and *c* correspond to the
height, width, and the number of channels of the maxima,
respectively.

If you specify the `Max`

property, then `Normalization`

must be `'rescale-symmetric'`

or
`'rescale-zero-one'`

. If `Max`

is
`[]`

, then the `trainnet`

and
`trainNetwork`

functions calculate the maxima. To train a
`dlnetwork`

object using a custom training loop or assemble a network
without training it using the `assembleNetwork`

function, you must set
the `Max`

property to a numeric scalar or a numeric array.

**Data Types: **`single`

| `double`

| `int8`

| `int16`

| `int32`

| `int64`

| `uint8`

| `uint16`

| `uint32`

| `uint64`

`SplitComplexInputs`

— Flag to split input data into real and imaginary components

`0`

(`false`

) (default) | `1`

(`true`

)

This property is read-only.

Flag to split input data into real and imaginary components specified as one of these values:

`0`

(`false`

) – Do not split input data.`1`

(`true`

) – Split data into real and imaginary components.

When `SplitComplexInputs`

is `1`

, then the layer
outputs twice as many channels as the input data. For example, if the input data is
complex-values with `numChannels`

channels, then the layer outputs data
with `2*numChannels`

channels, where channels `1`

through `numChannels`

contain the real components of the input data and
`numChannels+1`

through `2*numChannels`

contain
the imaginary components of the input data. If the input data is real, then channels
`numChannels+1`

through `2*numChannels`

are all
zero.

To input complex-valued data into a neural network, the
`SplitComplexInputs`

option of the input layer must be
`1`

.

For an example showing how to train a network with complex-valued data, see Train Network with Complex-Valued Data.

`DataAugmentation`

— Data augmentation transforms

`'none'`

(default) | `'randcrop'`

| `'randfliplr'`

| cell array of `'randcrop'`

and
`'randfliplr'`

This property is read-only.

**Note**

The `DataAugmentation`

property is not
recommended. To preprocess images with cropping, reflection, and
other geometric transformations, use `augmentedImageDatastore`

instead.

Data augmentation transforms to use during training, specified as one of the following.

`'none'`

— No data augmentation`'randcrop'`

— Take a random crop from the training image. The random crop has the same size as the input size.`'randfliplr'`

— Randomly flip the input images horizontally with a 50% chance.Cell array of

`'randcrop'`

and`'randfliplr'`

. The software applies the augmentation in the order specified in the cell array.

Augmentation of image data is another way of reducing overfitting [1], [2].

**Data Types: **`string`

| `char`

| `cell`

### Layer

`Name`

— Layer name

`""`

(default) | character vector | string scalar

Layer name, specified as a character vector or a string scalar.
For `Layer`

array input, the `trainnet`

, `trainNetwork`

, `assembleNetwork`

, `layerGraph`

, and
`dlnetwork`

functions automatically assign
names to layers with the name `""`

.

The `ImageInputLayer`

object stores this property as a character vector.

**Data Types: **`char`

| `string`

`NumInputs`

— Number of inputs

0 (default)

This property is read-only.

Number of inputs of the layer. The layer has no inputs.

**Data Types: **`double`

`InputNames`

— Input names

`{}`

(default)

This property is read-only.

Input names of the layer. The layer has no inputs.

**Data Types: **`cell`

`NumOutputs`

— Number of outputs

`1`

(default)

This property is read-only.

Number of outputs from the layer, returned as `1`

. This layer has a
single output only.

**Data Types: **`double`

`OutputNames`

— Output names

`{'out'}`

(default)

This property is read-only.

Output names, returned as `{'out'}`

. This layer has a single output
only.

**Data Types: **`cell`

## Examples

**Create Image Input Layer**

Create an image input layer for 28-by-28 color images with name `'input'`

. By default, the layer performs data normalization by subtracting the mean image of the training set from every input image.

inputlayer = imageInputLayer([28 28 3],'Name','input')

inputlayer = ImageInputLayer with properties: Name: 'input' InputSize: [28 28 3] SplitComplexInputs: 0 Hyperparameters DataAugmentation: 'none' Normalization: 'zerocenter' NormalizationDimension: 'auto' Mean: []

Include an image input layer in a `Layer`

array.

layers = [ ... imageInputLayer([28 28 1]) convolution2dLayer(5,20) reluLayer 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 '' ReLU ReLU 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

## Algorithms

### Layer Output Formats

Layers in a layer array or layer graph pass data to subsequent layers as formatted `dlarray`

objects. The format of a `dlarray`

object is a string of characters, in which each character describes the corresponding dimension of the data. The formats consists of one or more of these characters:

`"S"`

— Spatial`"C"`

— Channel`"B"`

— Batch`"T"`

— Time`"U"`

— Unspecified

For example, 2-D image data represented as a 4-D array, where the first two dimensions correspond to the spatial dimensions of the images, the third dimension corresponds to the channels of the images, and the fourth dimension corresponds to the batch dimension, can be described as having the format `"SSCB"`

(spatial, spatial, channel, batch).

The input layer of a network specifies the layout of the data that the network expects. If you have data in a different layout, then specify the layout using the `InputDataFormats`

training option.

The layer inputs
*h*-by-*w*-by-*c*-by-*N*
arrays into the network, where *h*, *w*, and
*c* are the height, width, and number of channels of the
images, respectively, and *N* is the number of images. Data in this
layout has the data format `"SSCB"`

(spatial, spatial, channel,
batch).

## References

[1] Krizhevsky, Alex, Ilya Sutskever, and
Geoffrey E. Hinton. "ImageNet Classification with Deep Convolutional Neural Networks."
*Communications of the ACM* 60, no. 6 (May 24, 2017): 84–90. https://doi.org/10.1145/3065386

[2] Cireşan, D., U. Meier, J.
Schmidhuber. "Multi-column Deep Neural Networks for Image Classification".
*IEEE Conference on Computer Vision and Pattern Recognition*,
2012.

## Extended Capabilities

### C/C++ Code Generation

Generate C and C++ code using MATLAB® Coder™.

Usage notes and limitations:

Code generation does not support

`'Normalization'`

specified using a function handle.Code generation does not support complex input and does not support

`'SplitComplexInputs'`

option.

### GPU Code Generation

Generate CUDA® code for NVIDIA® GPUs using GPU Coder™.

Usage notes and limitations:

Code generation does not support

`'Normalization'`

specified using a function handle.Code generation does not support complex input and does not support

`'SplitComplexInputs'`

option.

## Version History

**Introduced in R2016a**

### R2019b: `AverageImage`

property will be removed

`AverageImage`

will be removed. Use `Mean`

instead. To update your code, replace all instances of `AverageImage`

with `Mean`

.
There are no differences between the properties that require additional updates to your
code.

### R2019b: `imageInputLayer`

and `image3dInputLayer`

, by default, use channel-wise normalization

Starting in R2019b, `imageInputLayer`

and `image3dInputLayer`

,
by default, use channel-wise normalization. In previous versions, these layers use
element-wise normalization. To reproduce this behavior, set the `NormalizationDimension`

option of these layers to
`'element'`

.

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