Check validity of custom or function layer
checkLayer(
checks the validity of a custom or function layer using generated data of the sizes
in layer
,validInputSize
)validInputSize
. For layers with a single input, set
validInputSize
to a typical size of input data to the
layer. For layers with multiple inputs, set validInputSize
to a
cell array of typical sizes, where each element corresponds to a layer input.
checkLayer(
specifies additional options using one or more namevalue arguments.layer
,validInputSize
,Name=Value
)
Check the validity of the example custom layer preluLayer
.
The custom layer preluLayer
, attached to this is example as a supporting file, applies the PReLU operation to the input data. To access this layer, open this example as a live script.
Create an instance of the layer and check that it is valid using checkLayer
. Set the valid input size to the typical size of a single observation input to the layer. For a single input, the layer expects observations of size hbywbyc, where h, w, and c are the height, width, and number of channels of the previous layer output, respectively.
Specify validInputSize
as the typical size of an input array.
layer = preluLayer(20); validInputSize = [5 5 20]; checkLayer(layer,validInputSize)
Skipping multiobservation tests. To enable tests with multiple observations, specify the 'ObservationDimension' option. For 2D image data, set 'ObservationDimension' to 4. For 3D image data, set 'ObservationDimension' to 5. For sequence data, set 'ObservationDimension' to 2. Skipping GPU tests. No compatible GPU device found. Skipping code generation compatibility tests. To check validity of the layer for code generation, specify the 'CheckCodegenCompatibility' and 'ObservationDimension' options. Running nnet.checklayer.TestLayerWithoutBackward .......... .. Done nnet.checklayer.TestLayerWithoutBackward __________ Test Summary: 12 Passed, 0 Failed, 0 Incomplete, 16 Skipped. Time elapsed: 0.18741 seconds.
The results show the number of passed, failed, and skipped tests. If you do not specify the ObservationsDimension
option, or do not have a GPU, then the function skips the corresponding tests.
Check Multiple Observations
For multiobservation input, the layer expects an array of observations of size hbywbycbyN, where h, w, and c are the height, width, and number of channels, respectively, and N is the number of observations.
To check the layer validity for multiple observations, specify the typical size of an observation and set the ObservationDimension
option to 4.
layer = preluLayer(20); validInputSize = [5 5 20]; checkLayer(layer,validInputSize,ObservationDimension=4)
Skipping GPU tests. No compatible GPU device found. Skipping code generation compatibility tests. To check validity of the layer for code generation, specify the 'CheckCodegenCompatibility' and 'ObservationDimension' options. Running nnet.checklayer.TestLayerWithoutBackward .......... ........ Done nnet.checklayer.TestLayerWithoutBackward __________ Test Summary: 18 Passed, 0 Failed, 0 Incomplete, 10 Skipped. Time elapsed: 0.19972 seconds.
In this case, the function does not detect any issues with the layer.
Create a function layer object that applies the softsign operation to the input. The softsign operation is given by the function $\mathit{f}\left(\mathit{x}\right)=\frac{\mathit{x}}{1+\left\mathit{x}\right}$.
layer = functionLayer(@(X) X./(1 + abs(X)))
layer = FunctionLayer with properties: Name: '' PredictFcn: @(X)X./(1+abs(X)) Formattable: 0 Learnable Parameters No properties. State Parameters No properties. Show all properties
Check that the layer it is valid using the checkLayer
function. Set the valid input size to the typical size of a single observation input to the layer. For example, for a single input, the layer expects observations of size hbywbyc, where h, w, and c are the height, width, and number of channels of the previous layer output, respectively.
Specify validInputSize
as the typical size of an input array.
validInputSize = [5 5 20]; checkLayer(layer,validInputSize)
Skipping multiobservation tests. To enable tests with multiple observations, specify the 'ObservationDimension' option. For 2D image data, set 'ObservationDimension' to 4. For 3D image data, set 'ObservationDimension' to 5. For sequence data, set 'ObservationDimension' to 2. Skipping GPU tests. No compatible GPU device found. Skipping code generation compatibility tests. To check validity of the layer for code generation, specify the 'CheckCodegenCompatibility' and 'ObservationDimension' options. Running nnet.checklayer.TestLayerWithoutBackward .......... .. Done nnet.checklayer.TestLayerWithoutBackward __________ Test Summary: 12 Passed, 0 Failed, 0 Incomplete, 16 Skipped. Time elapsed: 0.2085 seconds.
The results show the number of passed, failed, and skipped tests. If you do not specify the ObservationsDimension
option, or do not have a GPU, then the function skips the corresponding tests.
Check Multiple Observations
For multiobservation image input, the layer expects an array of observations of size hbywbycbyN, where h, w, and c are the height, width, and number of channels, respectively, and N is the number of observations.
To check the layer validity for multiple observations, specify the typical size of an observation and set the ObservationDimension
option to 4.
layer = functionLayer(@(X) X./(1 + abs(X))); validInputSize = [5 5 20]; checkLayer(layer,validInputSize,ObservationDimension=4)
Skipping GPU tests. No compatible GPU device found. Skipping code generation compatibility tests. To check validity of the layer for code generation, specify the 'CheckCodegenCompatibility' and 'ObservationDimension' options. Running nnet.checklayer.TestLayerWithoutBackward .......... ........ Done nnet.checklayer.TestLayerWithoutBackward __________ Test Summary: 18 Passed, 0 Failed, 0 Incomplete, 10 Skipped. Time elapsed: 0.13807 seconds.
In this case, the function does not detect any issues with the layer.
Check the code generation compatibility of the custom layer codegenPreluLayer
.
The custom layer codegenPreluLayer
, attached to this is example as a supporting file, applies the PReLU operation to the input data. To access this layer, open this example as a live script.
Create an instance of the layer and check its validity using checkLayer
. Specify the valid input size as the size of a single observation of typical input to the layer. The layer expects 4D array inputs, where the first three dimensions correspond to the height, width, and number of channels of the previous layer output, and the fourth dimension corresponds to the observations.
Specify the typical size of the input of an observation and set the 'ObservationDimension'
option to 4. To check for code generation compatibility, set the CheckCodegenCompatibility
option to true
. The checkLayer
function does not check for functions that are not compatible with code generation. To check that the custom layer definition is supported for code generation, first use the Code Generation Readiness app. For more information, see Check Code by Using the Code Generation Readiness Tool (MATLAB Coder).
layer = codegenPreluLayer(20,"prelu");
validInputSize = [24 24 20];
checkLayer(layer,validInputSize,ObservationDimension=4,CheckCodegenCompatibility=true)
Skipping GPU tests. No compatible GPU device found. Running nnet.checklayer.TestLayerWithoutBackward .......... .......... ... Done nnet.checklayer.TestLayerWithoutBackward __________ Test Summary: 23 Passed, 0 Failed, 0 Incomplete, 5 Skipped. Time elapsed: 1.2291 seconds.
The function does not detect any issues with the layer.
layer
— Layer to checknnet.layer.Layer
object  nnet.layer.ClassificationLayer
object  nnet.layer.RegressionLayer
objectLayer to check, specified as an nnet.layer.Layer
,
nnet.layer.ClassificationLayer
,
nnet.layer.RegressionLayer
, or
FunctionLayer
object.
For an example showing how to define your own custom layer, see Define Custom Deep Learning Layer with Learnable Parameters. To
create a layer that applies a specified function, use functionLayer
.
The checkLayer
function does not support layers that
inherit from nnet.layer.Formattable
.
validInputSize
— Valid input sizesValid input sizes of the layer, specified as a vector of positive integers or cell array of vectors of positive integers.
For layers with a single input, specify
validInputSize
as a vector of integers corresponding to the dimensions of
the input data. For example, [5 5 10]
corresponds to valid input data of size 5by5by10.
For layers with multiple inputs, specify
validInputSize
as a cell array of vectors, where each vector corresponds
to a layer input and the elements of the vectors correspond to the dimensions of the
corresponding input data. For example, {[24 24 20],[24 24
10]}
corresponds to the valid input sizes of two
inputs, where 24by24by20 is a valid input size for the first
input and 24by24by10 is a valid input size for the second
input.
For more information, see Layer Input Sizes.
For large input sizes, the gradient checks take longer to run. To speed up the tests, specify a smaller valid input size.
Example: [5 5 10]
Example: {[24 24 20],[24 24 10]}
Data Types: single
 double
 int8
 int16
 int32
 int64
 uint8
 uint16
 uint32
 uint64
 cell
Specify optional pairs of arguments as
Name1=Value1,...,NameN=ValueN
, where Name
is
the argument name and Value
is the corresponding value.
Namevalue arguments must appear after other arguments, but the order of the
pairs does not matter.
ObservationDimension=4
sets the observation dimension
to 4ObservationDimension
— Observation dimensionObservation dimension, specified as a positive integer.
The observation dimension specifies which dimension of the layer input data corresponds to observations. For example, if the layer expects input data is of size hbywbycbyN, where h, w, and c correspond to the height, width, and number of channels of the input data, respectively, and N corresponds to the number of observations, then the observation dimension is 4. For more information, see Layer Input Sizes.
If you specify the observation dimension, then the
checkLayer
function checks that the layer
functions are valid using generated data with minibatches of size 1 and
2. If you do not specify the observation dimension, then the function
skips the corresponding tests.
Data Types: single
 double
 int8
 int16
 int32
 int64
 uint8
 uint16
 uint32
 uint64
CheckCodegenCompatibility
— Flag to enable code generation tests0
(false) (default)  1
(true)Flag to enable code generation tests, specified as
0
(false) or 1
(true).
If CheckCodegenCompatibility
is
1
(true), then you must specify the ObservationDimension
option.
Code generation supports intermediate layers with 2D image or feature input only. Code generation does not support layers with state properties (properties with attribute State
).
The checkLayer
function does not check that functions used by the layer
are compatible with code generation. To check that functions used by the custom layer also
support code generation, first use the Code Generation Readiness app. For more
information, see Check Code by Using the Code Generation Readiness Tool (MATLAB Coder).
For an example showing how to define a custom layer that supports code generation, see Define Custom Deep Learning Layer for Code Generation.
Data Types: logical
For each layer, the valid input size and the observation dimension depend on the output of the previous layer.
For intermediate layers (layers of type nnet.layer.Layer
),
the valid input size and the observation dimension depend on the type of data
input to the layer.
For layers with a single input, specify
validInputSize
as a vector of integers corresponding to the dimensions of
the input data.
For layers with multiple inputs, specify
validInputSize
as a cell array of vectors, where each vector corresponds
to a layer input and the elements of the vectors correspond to the dimensions of the
corresponding input data.
For large input sizes, the gradient checks take longer to run. To speed up the tests, specify a smaller valid input size.
Layer Input  Input Size  Observation Dimension 

Feature vectors  cbyN, where c corresponds to the number of channels and N is the number of observations.  2 
2D images  hbywbycbyN, where h, w, and c correspond to the height, width, and number of channels of the images, respectively, and N is the number of observations.  4 
3D images  hbywbydbycbyN, where h, w, d, and c correspond to the height, width, depth, and number of channels of the 3D images, respectively, and N is the number of observations.  5 
Vector sequences  cbyNbyS, where c is the number of features of the sequences, N is the number of observations, and S is the sequence length.  2 
2D image sequences  hbywbycbyNbyS, where h, w, and c correspond to the height, width, and number of channels of the images, respectively, N is the number of observations, and S is the sequence length.  4 
3D image sequences  hbywbydbycbyNbyS, where h, w, d, and c correspond to the height, width, depth, and number of channels of the 3D images, respectively, N is the number of observations, and S is the sequence length.  5 
For example, for 2D image classification problems, set
validInputSize
to [h w c]
, where
h
, w
, and c
correspond to the height, width, and number of channels of the images,
respectively, and ObservationDimension
to
4
.
Code generation supports intermediate layers with 2D image input only.
For output layers (layers of type
nnet.layer.ClassificationLayer
or
nnet.layer.RegressionLayer
), set
validInputSize
to the typical size of a single input
observation Y
to the layer.
For classification problems, the valid input size and the observation
dimension of Y
depend on the type of problem:
Classification Task  Input Size  Observation Dimension 

2D image classification  1by1byKbyN, where K is the number of classes and N is the number of observations.  4 
3D image classification  1by1by1byKbyN, where K is the number of classes and N is the number of observations.  5 
Sequencetolabel classification  KbyN, where K is the number of classes and N is the number of observations.  2 
Sequencetosequence classification  KbyNbyS, where K is the number of classes, N is the number of observations, and S is the sequence length.  2 
For example, for 2D image classification problems, set
validInputSize
to [1 1 K]
, where
K
is the number of classes, and
ObservationDimension
to 4
.
For regression problems, the dimensions of Y
also depend on
the type of problem. The following table describes the dimensions of
Y
.
Regression Task  Input Size  Observation Dimension 

2D image regression  1by1byRbyN, where R is the number of responses and N is the number of observations.  4 
2D Imagetoimage regression  hbywbycbyN ,
where h, w, and
c are the height, width, and number of channels
of the output respectively, and N is the number of
observations.  4 
3D image regression  1by1by1byRbyN, where R is the number of responses and N is the number of observations.  5 
3D Imagetoimage regression  hbywbydbycbyN ,
where h, w, d,
and c are the height, width, depth, and number of
channels of the output respectively, and N is the
number of observations.  5 
Sequencetoone regression  RbyN, where R is the number of responses and N is the number of observations.  2 
Sequencetosequence regression  RbyNbyS, where R is the number of responses, N is the number of observations, and S is the sequence length.  2 
For example, for 2D image regression problems, set
validInputSize
to [1 1 R]
, where
R
is the number of responses, and
ObservationDimension
to 4
.
The checkLayer
function checks the validity of a custom layer
by performing a series of tests, described in these tables. For more information on
the tests used by checkLayer
, see Check Custom Layer Validity.
The checkLayer
function uses these tests to check the validity of custom
intermediate layers (layers of type nnet.layer.Layer
).
Test  Description 

functionSyntaxesAreCorrect  The syntaxes of the layer functions are correctly defined. 
predictDoesNotError  predict function does not error. 
forwardDoesNotError  When specified, the 
forwardPredictAreConsistentInSize  When 
backwardDoesNotError  When specified, backward does not error. 
backwardIsConsistentInSize  When

predictIsConsistentInType  The outputs of 
forwardIsConsistentInType  When 
backwardIsConsistentInType  When 
gradientsAreNumericallyCorrect  When backward is specified, the gradients computed
in backward are consistent with the numerical
gradients. 
backwardPropagationDoesNotError  When backward is not specified, the derivatives
can be computed using automatic differentiation. 
predictReturnsValidStates  For layers with state properties, the predict
function returns valid states. 
forwardReturnsValidStates  For layers with state properties, the forward
function, if specified, returns valid states. 
resetStateDoesNotError  For layers with state properties, the resetState
function, if specified, does not error and resets the states to valid
states. 
codegenPragmaDefinedInClassDef  The pragma "%#codegen" for code generation is
specified in class file. 
checkForSupportedLayerPropertiesForCodegen  The layer properties support code generation. 
predictIsValidForCodeGeneration  predict is valid for code generation. 
doesNotHaveStateProperties  For code generation, the layer does not have state properties. 
supportedFunctionLayer  For code generation, the layer is not a
FunctionLayer object. 
Some tests run multiple times. These tests also check different data types and for GPU compatibility:
predictIsConsistentInType
forwardIsConsistentInType
backwardIsConsistentInType
To execute the layer functions on a GPU, the functions must support inputs and outputs of
type gpuArray
with the underlying data type
single
.
The checkLayer
function uses these tests to check the
validity of custom output layers (layers of type
nnet.layer.ClassificationLayer
or
nnet.layer.RegressionLayer
).
Test  Description 

forwardLossDoesNotError  forwardLoss does not error. 
backwardLossDoesNotError  backwardLoss does not error. 
forwardLossIsScalar  The output of forwardLoss is scalar. 
backwardLossIsConsistentInSize  When backwardLoss is specified, the output of
backwardLoss is consistent in size:
dLdY is the same size as the predictions
Y . 
forwardLossIsConsistentInType  The output of 
backwardLossIsConsistentInType  When 
gradientsAreNumericallyCorrect  When backwardLoss is specified, the gradients computed
in backwardLoss are numerically correct. 
backwardPropagationDoesNotError  When backwardLoss is not specified, the derivatives
can be computed using automatic differentiation. 
The forwardLossIsConsistentInType
and
backwardLossIsConsistentInType
tests also check for GPU compatibility. To
execute the layer functions on a GPU, the functions must support inputs and outputs of type
gpuArray
with the underlying data type single
.
trainNetwork
 trainingOptions
 analyzeNetwork
다음 MATLAB 명령에 해당하는 링크를 클릭했습니다.
명령을 실행하려면 MATLAB 명령 창에 입력하십시오. 웹 브라우저는 MATLAB 명령을 지원하지 않습니다.
Choose a web site to get translated content where available and see local events and offers. Based on your location, we recommend that you select: .
Select web siteYou can also select a web site from the following list:
Select the China site (in Chinese or English) for best site performance. Other MathWorks country sites are not optimized for visits from your location.