3-D image input layer
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.
layer = image3dInputLayer(inputSize)InputSize
property.
layer = image3dInputLayer(inputSize,Name,Value)
InputSize — Size of the inputSize 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 to
1.
For RGB input, specify a vector with c equal to
3.
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 handleData 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 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 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.
Mean — Mean for zero-center and z-score normalization[] (default) | 4-D array | numeric scalarMean 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 scalarStandard 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 scalarMinimum 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 scalarMaximum 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
Name — Layer name'' (default) | character vector | string scalar
Layer name, specified as a character vector or a string scalar.
To include a layer in a layer graph, you must specify a nonempty unique layer name. If you train
a series network with the layer and Name is set to '',
then the software automatically assigns a name to the layer at training time.
Data Types: char | string
NumInputs — Number of inputsNumber 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 outputsNumber of outputs of the layer. This layer has a single output only.
Data Types: double
OutputNames — Output names{'out'} (default)Output names of the layer. This layer has a single output only.
Data Types: cell
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
AverageImage property will be removedNot 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.
imageInputLayer and image3dInputLayer, by default, use channel-wise normalizationBehavior 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'.
averagePooling3dLayer | convolution3dLayer | fullyConnectedLayer | imageInputLayer | maxPooling3dLayer | trainNetwork | transposedConv3dLayer
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