image3dInputLayer
3-D image input layer
Description
A 3-D image input layer inputs 3-D images or volumes to a neural network and applies data normalization.
For 2-D image input, use imageInputLayer
.
Creation
Description
sets optional properties using one or more name-value arguments.layer
= image3dInputLayer(inputSize
,Name=Value
)
Input Arguments
inputSize
— Size of the input
row vector of integers
Size of the input data, specified as a row vector of integers [h w d
c]
, where h
, w
,
d
, and c
correspond to the height, width, depth,
and number of channels respectively.
For grayscale input, specify a vector with
c
equal to1
.For RGB input, specify a vector with
c
equal to3
.For multispectral or hyperspectral input, specify a vector with
c
equal to the number of channels.
For 2-D image input, use imageInputLayer
.
Example:
[132 132 116 3]
Specify optional pairs of arguments as
Name1=Value1,...,NameN=ValueN
, where Name
is
the argument name and Value
is the corresponding value.
Name-value arguments must appear after other arguments, but the order of the
pairs does not matter.
Before R2021a, use commas to separate each name and value, and
enclose
Name
in quotes.
Example: image3dInputLayer([132 132 116 3],Name="input")
creates a
3-D image input layer for 132-by-132-by-116 color 3-D images with name
'input'
.
Normalization
— Data normalization
"zerocenter"
(default) | "zscore"
| "rescale-symmetric"
| "rescale-zero-one"
| "none"
| function handle
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 byMean
."zscore"
— Subtract the mean specified byMean
and divide byStandardDeviation
."rescale-symmetric"
— Rescale the input to be in the range [-1, 1] using the minimum and maximum values specified byMin
andMax
, respectively."rescale-zero-one"
— Rescale the input to be in the range [0, 1] using the minimum and maximum values specified byMin
andMax
, 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 = f(X)
, whereX
is the input data and the outputY
is the normalized data.
This layer supports complex-valued data. (since R2024a)
To input complex-valued data into the network, the
Normalization
option must be "zerocenter"
,
"zscore"
, "none"
, or a function
handle.
Tip
The software, by default, automatically calculates the normalization
statistics when you use the trainnet
function. To save time when training, specify the required
statistics for normalization and set the ResetInputNormalization
option in trainingOptions
to 0
(false
).
The Image3DInputLayer
object stores the
Normalization
property as a character vector or a function
handle.
NormalizationDimension
— Normalization dimension
"auto"
(default) | "channel"
| "element"
| "all"
Normalization dimension, specified as one of the following:
"auto"
– If theResetInputNormalization
training option is0
(false
) and you specify any of the normalization statistics (Mean
,StandardDeviation
,Min
, orMax
), 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.
The Image3DInputLayer
object stores the
NormalizationDimension
property as a character
vector.
Mean
— Mean for zero-center and z-score normalization
[]
(default) | 4-D array | numeric scalar
Mean for zero-center and z-score normalization, specified as a
h-by-w-by-d-by-c
array, a 1-by-1-by-1-by-c array of means per channel, a numeric
scalar, or []
, where h, w,
d, and c correspond to the height, width,
depth, and the number of channels of the mean, respectively.
To specify the Mean
property, the Normalization
property must be
"zerocenter"
or "zscore"
. If Mean
is []
, then the software
automatically sets the property at training or initialization time:
The
trainnet
function calculates the mean using the training data and uses the resulting value.The
initialize
function and thedlnetwork
function when theInitialize
option is1
(true
) sets the property to0
.
Before R2024a: This option does not support complex-valued data.
Data Types: single
| double
| int8
| int16
| int32
| int64
| uint8
| uint16
| uint32
| uint64
Complex Number Support: Yes
StandardDeviation
— Standard deviation for z-score normalization
[]
(default) | 4-D array | numeric scalar
Standard deviation for z-score normalization, specified as a
h-by-w-by-d-by-c
array, a 1-by-1-by-1-by-c array of means per channel, a numeric
scalar, or []
, where h, w,
d, and c correspond to the height, width,
depth, and the number of channels of the standard deviation, respectively.
To specify the StandardDeviation
property,
the Normalization
property must be
"zscore"
. If StandardDeviation
is []
, then the software
automatically sets the property at training or initialization time:
The
trainnet
function calculates the standard deviation using the training data and uses the resulting value.The
initialize
function and thedlnetwork
function when theInitialize
option is1
(true
) sets the property to1
.
Before R2024a: This option does not support complex-valued data.
Data Types: single
| double
| int8
| int16
| int32
| int64
| uint8
| uint16
| uint32
| uint64
Complex Number Support: Yes
Min
— Minimum value for rescaling
[]
(default) | 4-D array | numeric scalar
Minimum value for rescaling, specified as a
h-by-w-by-d-by-c
array, a 1-by-1-by-1-by-c array of minima per channel, a numeric
scalar, or []
, where h, w,
d, and c correspond to the height, width,
depth, and the number of channels of the minima, respectively.
To specify the Min
property, the Normalization
must be
"rescale-symmetric"
or "rescale-zero-one"
.
If Min
is []
, then the
software automatically sets the property at training or initialization time:
The
trainnet
function calculates the minimum value using the training data and uses the resulting value.The
initialize
function and thedlnetwork
function when theInitialize
option is1
(true
) sets the property to-1
and0
whenNormalization
is"rescale-symmetric"
and"rescale-zero-one"
, respectively.
Data Types: single
| double
| int8
| int16
| int32
| int64
| uint8
| uint16
| uint32
| uint64
Max
— Maximum value for rescaling
[]
(default) | 4-D array | numeric scalar
Maximum value for rescaling, specified as a
h-by-w-by-d-by-c
array, a 1-by-1-by-1-by-c array of maxima per channel, a numeric
scalar, or []
, where h, w,
d, and c correspond to the height, width,
depth, and the number of channels of the maxima, respectively.
To specify the Max
property, the Normalization
must be
"rescale-symmetric"
or "rescale-zero-one"
.
If Max
is []
, then the
software automatically sets the property at training or initialization time:
The
trainnet
function calculates the maximum value using the training data and uses the resulting value.The
initialize
function and thedlnetwork
function when theInitialize
option is1
(true
) sets the property to1
.
Data Types: single
| double
| int8
| int16
| int32
| int64
| uint8
| uint16
| uint32
| uint64
Name
— Layer name
""
(default) | character vector | string scalar
Properties
3-D Image Input
InputSize
— Size of the input
row vector of integers
Size of the input data, specified as a row vector of integers [h w d
c]
, where h
, w
,
d
, and c
correspond to the height, width, depth,
and number of channels respectively.
For grayscale input, specify a vector with
c
equal to1
.For RGB input, specify a vector with
c
equal to3
.For multispectral or hyperspectral input, specify a vector with
c
equal to the number of channels.
For 2-D image input, use imageInputLayer
.
Example:
[132 132 116 3]
Normalization
— Data normalization
"zerocenter"
(default) | "zscore"
| "rescale-symmetric"
| "rescale-zero-one"
| "none"
| function handle
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 byMean
."zscore"
— Subtract the mean specified byMean
and divide byStandardDeviation
."rescale-symmetric"
— Rescale the input to be in the range [-1, 1] using the minimum and maximum values specified byMin
andMax
, respectively."rescale-zero-one"
— Rescale the input to be in the range [0, 1] using the minimum and maximum values specified byMin
andMax
, 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 = f(X)
, whereX
is the input data and the outputY
is the normalized data.
This layer supports complex-valued data. (since R2024a) To
input complex-valued data into the network, the Normalization
option must be "zerocenter"
, "zscore"
,
"none"
, or a function handle.
Tip
The software, by default, automatically calculates the normalization statistics
when you use the trainnet
function. To save time when training, specify the required statistics for
normalization and set the ResetInputNormalization
option in trainingOptions
to 0
(false
).
The Image3DInputLayer
object stores this property as a character vector or a
function handle.
NormalizationDimension
— Normalization dimension
"auto"
(default) | "channel"
| "element"
| "all"
Normalization dimension, specified as one of the following:
"auto"
– If theResetInputNormalization
training option is0
(false
) and you specify any of the normalization statistics (Mean
,StandardDeviation
,Min
, orMax
), 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.
The Image3DInputLayer
object stores this property as a character vector.
Mean
— Mean for zero-center and z-score normalization
[]
(default) | 4-D array | numeric scalar
Mean for zero-center and z-score normalization, specified as a
h-by-w-by-d-by-c
array, a 1-by-1-by-1-by-c array of means per channel, a numeric
scalar, or []
, where h, w,
d, and c correspond to the height, width,
depth, and the number of channels of the mean, respectively.
To specify the Mean
property, the Normalization
property must be "zerocenter"
or "zscore"
. If Mean
is
[]
, then the software automatically sets the property at training or
initialization time:
The
trainnet
function calculates the mean using the training data and uses the resulting value.The
initialize
function and thedlnetwork
function when theInitialize
option is1
(true
) sets the property to0
.
Before R2024a: This option does not support complex-valued data.
Data Types: single
| double
| int8
| int16
| int32
| int64
| uint8
| uint16
| uint32
| uint64
Complex Number Support: Yes
StandardDeviation
— Standard deviation for z-score normalization
[]
(default) | 4-D array | numeric scalar
Standard deviation for z-score normalization, specified as a
h-by-w-by-d-by-c
array, a 1-by-1-by-1-by-c array of means per channel, a numeric
scalar, or []
, where h, w,
d, and c correspond to the height, width,
depth, and the number of channels of the standard deviation, respectively.
To specify the StandardDeviation
property, the
Normalization
property must be
"zscore"
. If StandardDeviation
is
[]
, then the software automatically sets the property at training or
initialization time:
The
trainnet
function calculates the standard deviation using the training data and uses the resulting value.The
initialize
function and thedlnetwork
function when theInitialize
option is1
(true
) sets the property to1
.
Before R2024a: This option does not support complex-valued data.
Data Types: single
| double
| int8
| int16
| int32
| int64
| uint8
| uint16
| uint32
| uint64
Complex Number Support: Yes
Min
— Minimum value for rescaling
[]
(default) | 4-D array | numeric scalar
Minimum value for rescaling, specified as a
h-by-w-by-d-by-c
array, a 1-by-1-by-1-by-c array of minima per channel, a numeric
scalar, or []
, where h, w,
d, and c correspond to the height, width,
depth, and the number of channels of the minima, respectively.
To specify the Min
property, the Normalization
must be "rescale-symmetric"
or
"rescale-zero-one"
. If Min
is
[]
, then the software automatically sets the property at training or
initialization time:
The
trainnet
function calculates the minimum value using the training data and uses the resulting value.The
initialize
function and thedlnetwork
function when theInitialize
option is1
(true
) sets the property to-1
and0
whenNormalization
is"rescale-symmetric"
and"rescale-zero-one"
, respectively.
Data Types: single
| double
| int8
| int16
| int32
| int64
| uint8
| uint16
| uint32
| uint64
Max
— Maximum value for rescaling
[]
(default) | 4-D array | numeric scalar
Maximum value for rescaling, specified as a
h-by-w-by-d-by-c
array, a 1-by-1-by-1-by-c array of maxima per channel, a numeric
scalar, or []
, where h, w,
d, and c correspond to the height, width,
depth, and the number of channels of the maxima, respectively.
To specify the Max
property, the Normalization
must be "rescale-symmetric"
or
"rescale-zero-one"
. If Max
is
[]
, then the software automatically sets the property at training or
initialization time:
The
trainnet
function calculates the maximum value using the training data and uses the resulting value.The
initialize
function and thedlnetwork
function when theInitialize
option is1
(true
) sets the property to1
.
Data Types: single
| double
| int8
| int16
| int32
| int64
| uint8
| uint16
| uint32
| uint64
Layer
Name
— Layer name
""
(default) | character vector | string scalar
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 3-D Image Input Layer
Create a 3-D image input layer for 132-by-132-by-116 color 3-D images
layer = image3dInputLayer([132 132 116])
layer = Image3DInputLayer with properties: Name: '' InputSize: [132 132 116 1] Hyperparameters Normalization: 'zerocenter' NormalizationDimension: 'auto' Mean: []
Include a 3-D image input layer in a Layer
array.
layers = [ image3dInputLayer([28 28 28 3]) convolution3dLayer(5,16,Stride=4) reluLayer maxPooling3dLayer(2,Stride=4) fullyConnectedLayer(10) softmaxLayer]
layers = 6x1 Layer array with layers: 1 '' 3-D Image Input 28x28x28x3 images with 'zerocenter' normalization 2 '' 3-D Convolution 16 5x5x5 convolutions with stride [4 4 4] and padding [0 0 0; 0 0 0] 3 '' ReLU ReLU 4 '' 3-D Max Pooling 2x2x2 max pooling with stride [4 4 4] and padding [0 0 0; 0 0 0] 5 '' Fully Connected 10 fully connected layer 6 '' Softmax softmax
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 consist of one or
more of these characters:
"S"
— Spatial"C"
— Channel"B"
— Batch"T"
— Time"U"
— Unspecified
For example, you can describe 2-D image data that is 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, 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-d-by-c-by-N
arrays to the network, where h, w,
d, and c are the height, width, depth, and number of
channels of the images, respectively, and N is the number of images. Data
in this layout has the data format "SSSCB"
(spatial, spatial, spatial,
channel, batch).
Complex Numbers
Image3DInputLayer
objects support passing
complex-valued data to subsequent layers. (since R2024a)
If the input data is complex-valued, then the Normalization
option
must be "zerocenter"
, "zscore"
,
"none"
, or a function handle. The Mean
and
StandardDeviation
properties of the layer also support complex-valued
data for "zerocenter"
and "zscore"
normalization
options.
For an example showing how to train a network with complex-valued data, see Train Network with Complex-Valued Data.
Version History
Introduced in R2019aR2024a: Complex-valued outputs
For complex-valued input to the neural network, the layer passes complex-valued data to subsequent layers.
If the input data is complex-valued, then the Normalization
option
must be "zerocenter"
, "zscore"
,
"none"
, or a function handle. The Mean
and
StandardDeviation
properties of the layer also support complex-valued
data for "zerocenter"
and "zscore"
normalization
options.
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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