Check Custom Layer Validity

If you create a custom deep learning layer, then you can use the checkLayer function to check that the layer is valid. The function checks layers for validity, GPU compatibility, correctly defined gradients, and code generation compatibility. To check that a layer is valid, run the following command:

checkLayer(layer,layout)

layer is an instance of the layer and layout is a networkDataLayout object specifying the valid sizes and data formats for inputs to the layer. To check with multiple observations, use the ObservationDimension option. To run the check for code generation compatibility, set the CheckCodegenCompatibility option to 1 (true). For large input sizes, the gradient checks take longer to run. To speed up the check, specify a smaller valid input size.

Check Custom Layer Validity

Open Live Script

Check the validity of the example custom layer sreluLayer.

The custom layer sreluLayer, attached to this example as a supporting file, applies the SReLU operation to the input data. To access this layer, open this example as a live script.

Create an instance of the layer.

layer = sreluLayer;

Create a networkDataLayout 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 20 128], where the dimensions correspond to the height, width, number of channels, and number of observations of the previous layer output. Specify the data has format "SSCB" (spatial, spatial, channel, batch).

validInputSize = [24 24 20 128];
layout = networkDataLayout(validInputSize,"SSCB");

Check the layer validity using checkLayer. 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,layout)

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:
	 20 Passed, 0 Failed, 0 Incomplete, 14 Skipped.
	 Time elapsed: 0.1629 seconds.

The results show the number of passed, failed, and skipped tests. If you do not have a GPU, then the function skips the corresponding tests.

List of Tests

The checkLayer function uses these tests to check the validity of custom layers.

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.
`formattableLayerPredictIsFormatted` (since R2023b)	For layers that inherit from the `nnet.layer.Formattable` class, the `predict` function returns a formatted `dlarray` with a channel dimension.
`formattableLayerForwardIsFormatted` (since R2023b)	For layers that inherit from the `nnet.layer.Formattable` class, the `forward` function, if specified, returns a formatted `dlarray` with a channel dimension.
`initializeDoesNotChangeLearnableParametersWhenTheyAreNotEmpty` (since R2023b)	When you specify one or more `networkDataLayout` objects, the learnable parameters of the layer do not change after repeated initialization with the same `networkDataLayout` objects as input.
`initializeDoesNotChangeStatefulParametersWhenTheyAreNotEmpty` (since R2023b)	When you specify one or more `networkDataLayout` objects, the state parameters of the layer do not change after repeated initialization with the same `networkDataLayout` objects as input.
`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.

Generated Data

To check the layer validity, the checkLayer function generates data with values in the range [-1, 1].

To check for multiple observations, either specify a layout with a batch ("B") dimension or specify the observation dimension using the ObservationDimension option. 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 this name-value pair, then the function skips the tests that check that the layer functions are valid for multiple observations.

Diagnostics

If a test fails when you use checkLayer, then the function provides a test diagnostic and a framework diagnostic. The test diagnostic highlights any issues found with the layer. The framework diagnostic provides more detailed information.

Function Syntaxes

The test functionSyntaxesAreCorrect checks that the layer functions have correctly defined syntaxes.

Test Diagnostic	Description	Possible Solution
`Incorrect number of input arguments for 'predict' in Layer`.	The syntax for the `predict` function is not consistent with the number of layer inputs.	Specify the correct number of input and output arguments in `predict`. The `predict` function syntax depends on the type of layer. `Y = predict(layer,X)` forwards the input data `X` through the layer and outputs the result `Y`, where `layer` has a single input and a single output. `[Y,state] = predict(layer,X)` also outputs the updated state parameter `state`, where `layer` has a single state parameter. You can adjust the syntaxes for layers with multiple inputs, multiple outputs, or multiple state parameters: For layers with multiple inputs, replace `X` with `X1,...,XN`, where `N` is the number of inputs. The `NumInputs` property must match `N`. For layers with multiple outputs, replace `Y` with `Y1,...,YM`, where `M` is the number of outputs. The `NumOutputs` property must match `M`. For layers with multiple state parameters, replace `state` with `state1,...,stateK`, where `K` is the number of state parameters. Tip If the number of inputs to the layer can vary, then use `varargin` instead of `X1,…,XN`. In this case, `varargin` is a cell array of the inputs, where `varargin{i}` corresponds to `Xi`. If the number of outputs can vary, then use `varargout` instead of `Y1,…,YM`. In this case, `varargout` is a cell array of the outputs, where `varargout{j}` corresponds to `Yj`. Tip If the custom layer has a `dlnetwork` object for a learnable parameter, then in the `predict` function of the custom layer, use the `predict` function for the `dlnetwork`. When you do so, the `dlnetwork` object `predict` function uses the appropriate layer operations for prediction. If the `dlnetwork` has state parameters, then also return the network state.
`Incorrect number of output arguments for 'predict' in Layer`	The syntax for the `predict` function is not consistent with the number of layer outputs.
`Incorrect number of input arguments for 'forward' in Layer`	The syntax for the optional `forward` function is not consistent with the number of layer inputs.	Specify the correct number of input and output arguments in `forward`. The `forward` function syntax depends on the type of layer: `Y = forward(layer,X)` forwards the input data `X` through the layer and outputs the result `Y`, where `layer` has a single input and a single output. `[Y,state] = forward(layer,X)` also outputs the updated state parameter `state`, where `layer` has a single state parameter. `[__,memory] = forward(layer,X)` also returns a memory value for a custom `backward` function using any of the previous syntaxes. If the layer has both a custom `forward` function and a custom `backward` function, then the forward function must return a memory value. You can adjust the syntaxes for layers with multiple inputs, multiple outputs, or multiple state parameters: For layers with multiple inputs, replace `X` with `X1,...,XN`, where `N` is the number of inputs. The `NumInputs` property must match `N`. For layers with multiple outputs, replace `Y` with `Y1,...,YM`, where `M` is the number of outputs. The `NumOutputs` property must match `M`. For layers with multiple state parameters, replace `state` with `state1,...,stateK`, where `K` is the number of state parameters. Tip If the number of inputs to the layer can vary, then use `varargin` instead of `X1,…,XN`. In this case, `varargin` is a cell array of the inputs, where `varargin{i}` corresponds to `Xi`. If the number of outputs can vary, then use `varargout` instead of `Y1,…,YM`. In this case, `varargout` is a cell array of the outputs, where `varargout{j}` corresponds to `Yj`. Tip If the custom layer has a `dlnetwork` object for a learnable parameter, then in the `forward` function of the custom layer, use the `forward` function of the `dlnetwork` object. When you do so, the `dlnetwork` object `forward` function uses the appropriate layer operations for training.
`Incorrect number of output arguments for 'forward' in Layer`	The syntax for the optional `forward` function is not consistent with the number of layer outputs.
`Incorrect number of input arguments for 'backward' in Layer`	The syntax for the optional `backward` function is not consistent with the number of layer inputs and outputs.	Specify the correct number of input and output arguments in `backward`. The `backward` function syntax depends on the type of layer. `dLdX = backward(layer,X,Y,dLdY,memory)` returns the derivatives `dLdX` of the loss with respect to the layer input, where `layer` has a single input and a single output. `Y` corresponds to the forward function output and `dLdY` corresponds to the derivative of the loss with respect to `Y`. The function input `memory` corresponds to the memory output of the forward function. `[dLdX,dLdW] = backward(layer,X,Y,dLdY,memory)` also returns the derivative `dLdW` of the loss with respect to the learnable parameter, where `layer` has a single learnable parameter. `[dLdX,dLdSin] = backward(layer,X,Y,dLdY,dLdSout,memory)` also returns the derivative `dLdSin` of the loss with respect to the state input, where `layer` has a single state parameter and `dLdSout` corresponds to the derivative of the loss with respect to the layer state output. `[dLdX,dLdW,dLdSin] = backward(layer,X,Y,dLdY,dLdSout,memory)` also returns the derivative `dLdW` of the loss with respect to the learnable parameter and returns the derivative `dLdSin` of the loss with respect to the layer state input, where `layer` has a single state parameter and single learnable parameter. You can adjust the syntaxes for layers with multiple inputs, multiple outputs, multiple learnable parameters, or multiple state parameters: For layers with multiple inputs, replace `X` and `dLdX` with `X1,...,XN` and `dLdX1,...,dLdXN`, respectively, where `N` is the number of inputs. For layers with multiple outputs, replace `Y` and `dLdY` with `Y1,...,YM` and `dLdY1,...,dLdYM`, respectively, where `M` is the number of outputs. For layers with multiple learnable parameters, replace `dLdW` with `dLdW1,...,dLdWP`, where `P` is the number of learnable parameters. For layers with multiple state parameters, replace `dLdSin` and `dLdSout` with `dLdSin1,...,dLdSinK` and `dLdSout1,...,dLdSoutK`, respectively, where `K` is the number of state parameters. To reduce memory usage by preventing unused variables being saved between the forward and backward pass, replace the corresponding input arguments with `~`. Tip If the number of inputs to `backward` can vary, then use `varargin` instead of the input arguments after `layer`. In this case, `varargin` is a cell array of the inputs, where the first `N` elements correspond to the `N` layer inputs, the next `M` elements correspond to the `M` layer outputs, the next `M` elements correspond to the derivatives of the loss with respect to the `M` layer outputs, the next `K` elements correspond to the `K` derivatives of the loss with respect to the `K` state outputs, and the last element corresponds to `memory`. If the number of outputs can vary, then use `varargout` instead of the output arguments. In this case, `varargout` is a cell array of the outputs, where the first `N` elements correspond to the `N` the derivatives of the loss with respect to the `N` layer inputs, the next `P` elements correspond to the derivatives of the loss with respect to the `P` learnable parameters, and the next `K` elements correspond to the derivatives of the loss with respect to the `K` state inputs. Tip If the layer forward functions support `dlarray` objects, then the software automatically determines the backward function and you do not need to specify the `backward` function. For a list of functions that support `dlarray` objects, see List of Functions with dlarray Support.
`Incorrect number of output arguments for 'backward' in Layer`	The syntax for the optional `backward` function is not consistent with the number of layer outputs.

For layers with multiple inputs or outputs, you must set the values of the layer properties NumInputs (or alternatively, InputNames) and NumOutputs (or alternatively, OutputNames) in the layer constructor function, respectively.

Multiple Observations

The checkLayer function checks that the layer functions are valid for single and multiple observations. To check for multiple observations, either specify a layout with a batch ("B") dimension or specify the observation dimension using the ObservationDimension option. 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 this name-value pair, then the function skips the tests that check that the layer functions are valid for multiple observations.

Test Diagnostic Description Possible Solution

Test Diagnostic	Description	Possible Solution
`Skipping multi-observation tests. To enable checks with multiple observations, specify the 'ObservationDimension' parameter in checkLayer`.	If you do not specify the `ObservationDimension` option in `checkLayer`, then the function skips the tests that check data with multiple observations.	Use the command `checkLayer(layer,layout,ObservationDimension=dim)`, where `layer` is an instance of the custom layer, `validInputSize` is a vector specifying the valid input size to the layer, and `dim` specifies the dimension of the observations in the layer input. For more information, see Layer Input Sizes.

Skipping multi-observation tests. To enable checks with
                                    multiple observations, specify the 'ObservationDimension'
                                    parameter in checkLayer

If you do not specify the ObservationDimension option in checkLayer, then the function skips the tests that check data with multiple observations.

Use the command checkLayer(layer,layout,ObservationDimension=dim), where layer is an instance of the custom layer, validInputSize is a vector specifying the valid input size to the layer, and dim specifies the dimension of the observations in the layer input.

For more information, see Layer Input Sizes.

Functions Do Not Error

The tests predictDoesNotError, forwardDoesNotError, and backwardDoesNotError check that the layer functions do not error when passed inputs of valid size. If you specify an observation dimension, then the function checks the layer for both a single observation and multiple observations.

Test Diagnostic	Description	Possible Solution
`The function 'predict' threw an error:`	The `predict` function errors when passed data of the size defined by `layout`.	Address the error described in the `Framework Diagnostic` section. Tip If the layer forward functions support `dlarray` objects, then the software automatically determines the backward function and you do not need to specify the `backward` function. For a list of functions that support `dlarray` objects, see List of Functions with dlarray Support.
`The function 'forward' threw an error:`	The optional `forward` function errors when passed data of the size defined by `layout`.
`The function 'backward' threw an error:`	The optional `backward` function errors when passed the output of `predict`.

Outputs Are Consistent in Size

The test backwardIsConsistentInSize checks that the backward function outputs derivatives of the correct size.

The backward function syntax depends on the type of layer.

dLdX = backward(layer,X,Y,dLdY,memory) returns the derivatives dLdX of the loss with respect to the layer input, where layer has a single input and a single output. Y corresponds to the forward function output and dLdY corresponds to the derivative of the loss with respect to Y. The function input memory corresponds to the memory output of the forward function.
[dLdX,dLdW] = backward(layer,X,Y,dLdY,memory) also returns the derivative dLdW of the loss with respect to the learnable parameter, where layer has a single learnable parameter.
[dLdX,dLdSin] = backward(layer,X,Y,dLdY,dLdSout,memory) also returns the derivative dLdSin of the loss with respect to the state input, where layer has a single state parameter and dLdSout corresponds to the derivative of the loss with respect to the layer state output.
[dLdX,dLdW,dLdSin] = backward(layer,X,Y,dLdY,dLdSout,memory) also returns the derivative dLdW of the loss with respect to the learnable parameter and returns the derivative dLdSin of the loss with respect to the layer state input, where layer has a single state parameter and single learnable parameter.

You can adjust the syntaxes for layers with multiple inputs, multiple outputs, multiple learnable parameters, or multiple state parameters:

For layers with multiple inputs, replace X and dLdX with X1,...,XN and dLdX1,...,dLdXN, respectively, where N is the number of inputs.
For layers with multiple outputs, replace Y and dLdY with Y1,...,YM and dLdY1,...,dLdYM, respectively, where M is the number of outputs.
For layers with multiple learnable parameters, replace dLdW with dLdW1,...,dLdWP, where P is the number of learnable parameters.
For layers with multiple state parameters, replace dLdSin and dLdSout with dLdSin1,...,dLdSinK and dLdSout1,...,dLdSoutK, respectively, where K is the number of state parameters.

To reduce memory usage by preventing unused variables being saved between the forward and backward pass, replace the corresponding input arguments with ~.

Tip

If the number of inputs to backward can vary, then use varargin instead of the input arguments after layer. In this case, varargin is a cell array of the inputs, where the first N elements correspond to the N layer inputs, the next M elements correspond to the M layer outputs, the next M elements correspond to the derivatives of the loss with respect to the M layer outputs, the next K elements correspond to the K derivatives of the loss with respect to the K state outputs, and the last element corresponds to memory.

If the number of outputs can vary, then use varargout instead of the output arguments. In this case, varargout is a cell array of the outputs, where the first N elements correspond to the N the derivatives of the loss with respect to the N layer inputs, the next P elements correspond to the derivatives of the loss with respect to the P learnable parameters, and the next K elements correspond to the derivatives of the loss with respect to the K state inputs.

Test Diagnostic	Description	Possible Solution
`Incorrect size of 'dLdX' for 'backward'`.	The derivatives of the loss with respect to the layer inputs must be the same size as the corresponding layer input.	Return the derivatives `dLdX1,…,dLdXn` with the same size as the corresponding layer inputs `X1,…,Xn`.
`Incorrect size of the derivative of the loss with respect to the input 'in1' for 'backward'`
`The size of 'Y' returned from 'forward' must be the same as for 'predict'`.	The outputs of `predict` must be the same size as the corresponding outputs of `forward`.	Return the outputs `Y1,…,Ym` of `predict` with the same size as the corresponding outputs `Y1,…,Ym` of `forward`.
`Incorrect size of the derivative of the loss with respect to 'W' for 'backward'`.	The derivatives of the loss with respect to the learnable parameters must be the same size as the corresponding learnable parameters.	Return the derivatives `dLdW1,…,dLdWk` with the same size as the corresponding learnable parameters `W1,…,Wk`.

Tip

If the layer forward functions support dlarray objects, then the software automatically determines the backward function and you do not need to specify the backward function. For a list of functions that support dlarray objects, see List of Functions with dlarray Support.

Outputs Are Formatted

Since R2023b

The tests formattableLayerPredictIsFormatted and formattableLayerForwardIsFormatted check that the output of the layer functions are dlarray object with a channel dimension.

Test Diagnostic	Description	Possible Solution
`The layer output returned from 'predict' must be a formatted dlarray`.	The `predict` function does not return a formatted `dlarray`	Return the output of the `predict` function as a formatted `dlarray`.
`The layer output returned from 'forward' must be a formatted dlarray`.	The optional `forward` function does not return a formatted `dlarray`	Return the output of the optional `forward` function as a formatted `dlarray`.

Initialization

Since R2023b

These tests check that initialization does not overwrite nonempty parameters.

The tests initializeDoesNotChangeLearnableParametersWhenTheyAreNotEmpty and initializeDoesNotChangeStatefulParametersWhenTheyAreNotEmpty check that the custom initialize function does not overwrite nonempty learnable and stateful parameters.

Test Diagnostic	Description	Possible Solution
`The initialize function overwrites existing layer learnable parameters`.	The `initialize` function overwrites nonempty learnable parameters.	Initialize only empty parameters. To check whether a parameter is empty, use the `isempty` function. For example, `isempty(layer.Parameter)`.
`The initialize function overwrites existing layer state parameters`.	The `initialize` function overwrites nonempty state parameters.

Consistent Data Types and GPU Compatibility

The tests predictIsConsistentInType, forwardIsConsistentInType, and backwardIsConsistentInType check that the layer functions output variables of the correct data type. The tests check that the layer functions return consistent data types when given inputs of the data types single, double, and gpuArray with the underlying types single or double.

If the layer forward functions fully support dlarray objects, then the layer is GPU compatible. Otherwise, to be GPU compatible, the layer functions must support inputs and return outputs of type gpuArray (Parallel Computing Toolbox).

Many MATLAB^® built-in functions support gpuArray (Parallel Computing Toolbox) and dlarray input arguments. For a list of functions that support dlarray objects, see List of Functions with dlarray Support. For a list of functions that execute on a GPU, see Run MATLAB Functions on a GPU (Parallel Computing Toolbox). To use a GPU for deep learning, you must also have a supported GPU device. For information on supported devices, see GPU Computing Requirements (Parallel Computing Toolbox). For more information on working with GPUs in MATLAB, see GPU Computing in MATLAB (Parallel Computing Toolbox).

Tip

If you preallocate arrays using functions such as zeros, then you must ensure that the data types of these arrays are consistent with the layer function inputs. To create an array of zeros of the same data type as another array, use the "like" option of zeros. For example, to initialize an array of zeros of size sz with the same data type as the array X, use Y = zeros(sz,"like",X).

Test Diagnostic	Description	Possible Solution
`Incorrect type of 'Y' for 'predict'`.	The types of the outputs `Y1,…,Ym` of the `predict` function must be consistent with the inputs `X1,…,Xn`.	Return the outputs `Y1,…,Ym` with the same type as the inputs `X1,…,Xn`.
`Incorrect type of output 'out1' for 'predict'`.
`Incorrect type of 'Y' for 'forward'`.	The types of the outputs `Y1,…,Ym` of the optional `forward` function must be consistent with the inputs `X1,…,Xn`.
`Incorrect type of output 'out1' for 'forward'`.
`Incorrect type of 'dLdX' for 'backward'`.	The types of the derivatives `dLdX1,…,dLdXn` of the optional `backward` function must be consistent with the inputs `X1,…,Xn`.	Return the derivatives `dLdX1,…,dLdXn` with the same type as the inputs `X1,…,Xn`.
`Incorrect type of the derivative of the loss with respect to the input 'in1' for 'backward'`.
`Incorrect type of the derivative of loss with respect to 'W' for 'backward'`.	The type of the derivative of the loss of the learnable parameters must be consistent with the corresponding learnable parameters.	For each learnable parameter, return the derivative with the same type as the corresponding learnable parameter.

Tip

Correct Gradients

The test gradientsAreNumericallyCorrect checks that the gradients computed by the layer functions are numerically correct. The test backwardPropagationDoesNotError checks that the derivatives can be computed using automatic differentiation.

When the optional backward function is not specified, the test backwardPropagationDoesNotError checks that the derivatives can be computed using automatic differentiation. When the optional backward function is specified, the test gradientsAreNumericallyCorrect tests that the gradients computed in backward are numerically correct.

Test Diagnostic	Description	Possible Solution
`Expected a dlarray with no dimension labels, but instead found labels`.	When the optional `backward` function is not specified, the layer forward functions must output `dlarray` objects without dimension labels.	Ensure that any `dlarray` objects created in the layer forward functions do not contain dimension labels.
`Unable to backward propagate through the layer. Check that the 'forward' function fully supports automatic differentiation. Alternatively, implement the 'backward' function manually`.	One or more of the following: When the optional `backward` function is not specified, the layer forward functions do not support `dlarray` objects. When the optional `backward` function is not specified, the tracing of the input `dlarray` objects in the forward functions have been broken. For example, by using the `extractdata` function.	Check that the forward functions support `dlarray` objects. For a list of functions that support `dlarray` objects, see List of Functions with dlarray Support. Check that the derivatives of the input `dlarray` objects can be traced. To learn more about the derivative trace of `dlarray` objects, see Derivative Trace. Alternatively, define a custom backward function by creating a function named `backward`. To learn more, see Specify Custom Layer Backward Function.
`Unable to backward propagate through the layer. Check that the 'predict' function fully supports automatic differentiation. Alternatively, implement the 'backward' function manually`.
`The derivative 'dLdX' for 'backward' is inconsistent with the numerical gradient`.	One or more of the following: When the optional `backward` function is specified, the derivative is incorrectly computed The forward functions are non-differentiable at some input points Error tolerance is too small	If the layer forward functions support `dlarray` objects, then the software automatically determines the backward function and you can omit the backward function. For a list of functions that support `dlarray` objects, see List of Functions with dlarray Support. Check that the derivatives in `backward` are correctly computed. If the derivatives are correctly computed, then in the `Framework Diagnostic` section, manually check the absolute and relative error between the actual and expected values of the derivative. If the absolute and relative errors are within an acceptable margin of the tolerance, then you can ignore this test diagnostic.
`The derivative of the loss with respect to the input 'in1' for 'backward' is inconsistent with the numerical gradient`.
`The derivative of loss with respect to 'W' for 'backward' is inconsistent with the numerical gradient`.

Tip

Valid States

For layers with state properties, the test predictReturnsValidStates checks that the predict function returns valid states. When forward is specified, the test forwardReturnsValidStates checks that the forward function returns valid states. The test resetStateDoesNotError checks that the resetState function returns a layer with valid state properties.

Test Diagnostic	Description	Possible Solution
`Error using 'predict' in Layer. 'State' must be real-values numeric array or unformatted dlarray object`.	State outputs must be real-valued numeric arrays or unformatted `dlarray` objects.	Ensure that the states identified in the `Framework Diagnostic` are real-valued numeric arrays or unformatted `dlarray` objects.
`Error using 'resetState' in Layer. 'State' must be real-values numeric array or unformatted dlarray object`	State properties of returned layer must be real-valued numeric arrays or unformatted `dlarray` objects.

Code Generation Compatibility

If you set the CheckCodegenCompatibility option to 1 (true), then the checkLayer function checks the layer for code generation compatibility.

The test codegenPragmaDefinedInClassDef checks that the layer definition contains the code generation pragma %#codegen. The test layerPropertiesSupportCodegen checks that the layer properties support code generation. The test predictSupportsCodegen checks that the outputs of predict are consistent in dimension and batch size.

In addition, when generating code that uses third-party libraries:

Code generation supports custom layers with 2-D image or feature input only.
The inputs and output of the layer forward functions must have the same batch size.
Nonscalar properties must be a single, double, or character array.
Scalar properties must have type numeric, logical, or string.

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).

Test Diagnostic	Description	Possible Solution
`Specify '%#codegen' in the class definition of custom layer`	The layer definition does not include the pragma `"%#codegen"` for code generation.	Add the `%#codegen` directive (or pragma) to your layer definition to indicate that you intend to generate code for this layer. Adding this directive instructs the MATLAB Code Analyzer to help you diagnose and fix violations that result in errors during code generation.
`Nonscalar layer properties must be type single or double or character array for custom layer`	The layer contains non-scalar properties of type other than single, double, or character array.	Convert non-scalar properties to use a representation of type single, double, or character array. For example, convert a categorical array to an array of integers of type `double` representing the categories.
`Scalar layer properties must be numeric, logical, or string for custom layer`	The layer contains scalar properties of type other than numeric, logical, or string.	Convert scalar properties to use a numeric representation, or a representation of type logical or string. For example, convert a categorical scalar to an integer of type `double` representing the category.
`For code generation, 'Y' must have the same number of dimensions as the layer input`.	The number of dimensions of the output `Y` of `predict` does not match the number of dimensions of the layer inputs.	In the `predict` function, return the outputs with the same number of dimensions as the layer inputs.
`For code generation, 'Y' must have the same batch size as the layer input`.	The size of the batch size of the output `Y` of `predict` does not match the size of the batch size of the layer inputs.	In the `predict` function, return the outputs with the batch size as the layer inputs.