checkLayer

Check validity of custom or function layer

Syntax

checkLayer(layer,validInputSize)

checkLayer(layer,validInputSize,Name=Value)

Description

checkLayer(layer,validInputSize) checks the validity of a custom or function layer using generated data of the sizes in 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(layer,validInputSize,Name=Value) specifies additional options using one or more name-value arguments.

Examples

Check Custom Layer Validity

Check the validity of the example custom layer preluLayer.

The custom layer preluLayer, attached to this 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.

layer = preluLayer;

Because the layer has a custom initialize function, initialize the layer using a networkDataFormat object that specifies the expected input size and format of a single observation of typical input to the layer.

Specify a valid input size of [24 24 20], where the dimensions correspond to the height, width, and number of channels of the previous layer output.

validInputSize = [24 24 20];
layout = networkDataLayout(validInputSize,"SSC");
layer = initialize(layer,layout);

Check the layer validity using checkLayer. Specify the valid input size as the size as the size as used to initialize the layer. When you pass data through the network, the layer expects 4-D 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.

checkLayer(layer,validInputSize)

Skipping multi-observation tests. To enable tests with multiple observations, specify the 'ObservationDimension' option.
For 2-D image data, set 'ObservationDimension' to 4.
For 3-D 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: 0.054851 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 multi-observation image input, the layer expects an array of observations of size h-by-w-by-c-by-N, 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.

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: 0.030498 seconds.

In this case, the function does not detect any issues with the layer.

Check Function Layer Validity

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
    Acceleratable: 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 h-by-w-by-c, 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 multi-observation tests. To enable tests with multiple observations, specify the 'ObservationDimension' option.
For 2-D image data, set 'ObservationDimension' to 4.
For 3-D 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: 0.29567 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 multi-observation image input, the layer expects an array of observations of size h-by-w-by-c-by-N, 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: 0.14659 seconds.

In this case, the function does not detect any issues with the layer.

Check Custom Layer for Code Generation Compatibility

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 4-D 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: 0.67454 seconds.

The function does not detect any issues with the layer.

Input Arguments

layer — Layer to check

nnet.layer.Layer object | nnet.layer.ClassificationLayer object | nnet.layer.RegressionLayer object

Layer to check, specified as an nnet.layer.Layer, nnet.layer.ClassificationLayer, nnet.layer.RegressionLayer, or FunctionLayer object.

If layer has learnable or state parameters, then the layer must be initialized. If the layer has a custom initialize function, then first initialize the layer using the initialize function using networkDataLayout objects.

The checkLayer function does not support layers that inherit from nnet.layer.Formattable.

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.

validInputSize — Valid input sizes

vector of positive integers | cell array of vectors of positive integers

Valid 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 5-by-5-by-10.

• 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 24-by-24-by-20 is a valid input size for the first input and 24-by-24-by-10 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 check, 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

Name-Value Arguments

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.

Example: ObservationDimension=4 sets the observation dimension to 4

ObservationDimension — Observation dimension

positive integer

Observation 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 h-by-w-by-c-by-N, 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 mini-batches 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 tests

0 (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 2-D 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

More About

Layer Input Sizes

For each layer, the valid input size and the observation dimension depend on the output of the previous layer.

Intermediate Layers

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 check, specify a smaller valid input size.

Layer Input	Input Size	Observation Dimension
Feature vectors	c-by-N, where c corresponds to the number of channels and N is the number of observations	2
2-D images	h-by-w-by-c-by-N, 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
3-D images	h-by-w-by-d-by-c-by-N, where h, w, d, and c correspond to the height, width, depth, and number of channels of the 3-D images, respectively, and N is the number of observations	5
Vector sequences	c-by-N-by-S, where c is the number of features of the sequences, N is the number of observations, and S is the sequence length	2
2-D image sequences	h-by-w-by-c-by-N-by-S, 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
3-D image sequences	h-by-w-by-d-by-c-by-N-by-S, where h, w, d, and c correspond to the height, width, depth, and number of channels of the 3-D images, respectively, N is the number of observations, and S is the sequence length	5

For example, for 2-D 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 2-D image input only.

Output Layers

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
2-D image classification	1-by-1-by-K-by-N, where K is the number of classes and N is the number of observations	4
3-D image classification	1-by-1-by-1-by-K-by-N, where K is the number of classes and N is the number of observations	5
Sequence-to-label classification	K-by-N, where K is the number of classes and N is the number of observations	2
Sequence-to-sequence classification	K-by-N-by-S, where K is the number of classes, N is the number of observations, and S is the sequence length	2

For example, for 2-D 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
2-D image regression	1-by-1-by-R-by-N, where R is the number of responses and N is the number of observations	4
2-D Image-to-image regression	h-by-w-by-c-by-N, 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
3-D image regression	1-by-1-by-1-by-R-by-N, where R is the number of responses and N is the number of observations	5
3-D Image-to-image regression	h-by-w-by-d-by-c-by-N, 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
Sequence-to-one regression	R-by-N, where R is the number of responses and N is the number of observations	2
Sequence-to-sequence regression	R-by-N-by-S, where R is the number of responses, N is the number of observations, and S is the sequence length	2

For example, for 2-D image regression problems, set validInputSize to [1 1 R], where R is the number of responses, and ObservationDimension to 4.

Algorithms

List of Tests

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.

Intermediate Layers

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 forward function does not error.
forwardPredictAreConsistentInSize	When forward is specified, forward and predict output values of the same size.
backwardDoesNotError	When specified, backward does not error.
backwardIsConsistentInSize	When backward is specified, the outputs of backward are consistent in size: The derivatives with respect to each input are the same size as the corresponding input. The derivatives with respect to each learnable parameter are the same size as the corresponding learnable parameter.
predictIsConsistentInType	The outputs of predict are consistent in type with the inputs.
forwardIsConsistentInType	When forward is specified, the outputs of forward are consistent in type with the inputs.
backwardIsConsistentInType	When backward is specified, the outputs of backward are consistent in type with the inputs.
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.
layerPropertiesSupportCodegen	The layer properties support code generation.
predictSupportsCodegen	predict is valid for code generation.
doesNotHaveStateProperties	For code generation, the layer does not have state properties.
functionLayerSupportsCodegen	For code generation, the layer function must be a named function on the path and the Formattable property must be 0 (false).

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.

Output Layers

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 forwardLoss is consistent in type: loss is the same type as the predictions Y.
backwardLossIsConsistentInType	When backwardLoss is specified, the output of backwardLoss is consistent in type: dLdY must be the same type as the predictions Y.
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.

Version History

Introduced in R2018a