image3dInputLayer
3-D image input layer
Description
A 3-D image input layer inputs 3-D images or volumes to a network and applies data normalization.
For 2-D image input, use imageInputLayer
.
Creation
Description
returns a 3-D image input layer and specifies the layer
= image3dInputLayer(inputSize
)InputSize
property.
sets the optional properties using name-value pairs. You can specify multiple name-value
pairs. Enclose each property name in single quotes.layer
= image3dInputLayer(inputSize
,Name,Value
)
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 = func(X)
, whereX
is the input data and the outputY
is the normalized data.
Tip
The software, by default, automatically calculates the normalization statistics at training time. To save time when training, specify the required statistics for normalization and set the 'ResetInputNormalization'
option in trainingOptions
to false
.
NormalizationDimension
— Normalization dimension
'auto'
(default) | 'channel'
| 'element'
| 'all'
Normalization dimension, specified as one of the following:
'auto'
– If the training option isfalse
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.
Data Types: char
| string
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.
If you specify the Mean
property, then
Normalization
must be
'zerocenter'
or 'zscore'
. If Mean
is []
, then the software calculates
the mean at training time.
You can set this property when creating networks without
training (for example, when assembling networks using assembleNetwork
).
Data Types: single
| double
| int8
| int16
| int32
| int64
| uint8
| uint16
| uint32
| uint64
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.
If you specify the StandardDeviation
property,
then Normalization
must be
'zscore'
. If StandardDeviation
is []
, then the software calculates the standard deviation at
training time.
You can set this property when creating networks without
training (for example, when assembling networks using assembleNetwork
).
Data Types: single
| double
| int8
| int16
| int32
| int64
| uint8
| uint16
| uint32
| uint64
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.
If you specify the Min
property, then Normalization
must be
'rescale-symmetric'
or 'rescale-zero-one'
. If
Min
is []
, then the software
calculates the minimum at training time.
You can set this property when creating networks without
training (for example, when assembling networks using assembleNetwork
).
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.
If you specify the Min
property, then Normalization
must be
'rescale-symmetric'
or 'rescale-zero-one'
. If
Max
is []
, then the software
calculates the maximum at training time.
You can set this property when creating networks without
training (for example, when assembling networks using assembleNetwork
).
Data Types: single
| double
| int8
| int16
| int32
| int64
| uint8
| uint16
| uint32
| uint64
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 trainNetwork
,
assembleNetwork
, layerGraph
, and
dlnetwork
functions automatically assign names to layers with name
''
.
Data Types: char
| string
NumInputs
— Number of inputs
0 (default)
Number of inputs of the layer. The layer has no inputs.
Data Types: double
InputNames
— Input names
{}
(default)
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 of the layer. This layer has a single output only.
Data Types: double
OutputNames
— Output names
{'out'}
(default)
This property is read-only.
Output names of the layer. 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 with name 'input'
. By default, the layer performs data normalization by subtracting the mean image of the training set from every input image.
layer = image3dInputLayer([132 132 116],'Name','input')
layer = Image3DInputLayer with properties: Name: 'input' 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 classificationLayer]
layers = 7x1 Layer array with layers: 1 '' 3-D Image Input 28x28x28x3 images with 'zerocenter' normalization 2 '' 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 7 '' Classification Output crossentropyex
Version History
Introduced in R2019aR2019b: AverageImage
property will be removed
Not recommended starting in R2019b
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
Behavior change in future release
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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