## Define Custom Deep Learning Intermediate Layers

Tip

This topic explains how to define custom deep learning layers for your problems. For a list of built-in layers in Deep Learning Toolbox™, see List of Deep Learning Layers.

To learn how to define custom output layers, see Define Custom Deep Learning Output Layers.

If Deep Learning Toolbox does not provide the layer that you require for your task, then you can define your own custom layer using this topic as a guide. After defining the custom layer, you can automatically check that the layer is valid, GPU compatible, and outputs correctly defined gradients.

### Intermediate Layer Architecture

During training, the software iteratively performs forward and backward passes through the network.

When making a forward pass through the network, each layer takes the outputs of the previous layers, applies a function, and then outputs (forward propagates) the results to the next layers. Stateful layers, such as LSTM layers, also update the layer state.

Layers can have multiple inputs or outputs. For example, a layer can take X1, …, XN from multiple previous layers and forward propagate the outputs Z1, …, ZM to subsequent layers.

At the end of a forward pass of the network, the output layer calculates the loss L between the predictions Y and the targets T.

During the backward pass of a network, each layer takes the derivatives of the loss with respect to the outputs of the layer, computes the derivatives of the loss L with respect to the inputs, and then backward propagates the results. If the layer has learnable parameters, then the layer also computes the derivatives of the layer weights (learnable parameters). The layer uses the derivatives of the weights to update the learnable parameters.

The following figure describes the flow of data through a deep neural network and highlights the data flow through a layer with a single input X, a single output Z, and a learnable parameter W.

### Intermediate Layer Template

To define a custom intermediate layer, use this class definition template. This template outlines the structure of an intermediate layer class definition. It outlines:

• The optional properties blocks for the layer properties, learnable parameters, and state parameters. For more information, see Intermediate Layer Properties.

• The layer constructor function.

• The predict function and the optional forward function. For more information, see Forward Functions.

• The optional resetState function for layers with state properties. For more information, see Reset State Function.

classdef myLayer < nnet.layer.Layer % & nnet.layer.Formattable (Optional)

properties
% (Optional) Layer properties.

% Declare layer properties here.
end

properties (Learnable)
% (Optional) Layer learnable parameters.

% Declare learnable parameters here.
end

properties (State)
% (Optional) Layer state parameters.

% Declare state parameters here.
end

properties (Learnable, State)
% (Optional) Nested dlnetwork objects with both learnable
% parameters and state.

% Declare nested networks with learnable and state parameters here.
end

methods
function layer = myLayer()
% (Optional) Create a myLayer.
% This function must have the same name as the class.

% Define layer constructor function here.
end

function [Z,state] = predict(layer,X)
% Forward input data through the layer at prediction time and
% output the result and updated state.
%
% Inputs:
%         layer - Layer to forward propagate through
%         X     - Input data
% Outputs:
%         Z     - Output of layer forward function
%         state - (Optional) Updated layer state.
%
%  - For layers with multiple inputs, replace X with X1,...,XN,
%    where N is the number of inputs.
%  - For layers with multiple outputs, replace Z with
%    Z1,...,ZM, where M is the number of outputs.
%  - For layers with multiple state parameters, replace state
%    with state1,...,stateK, where K is the number of state
%    parameters.

% Define layer predict function here.
end

function [Z,state,memory] = forward(layer,X)
% (Optional) Forward input data through the layer at training
% time and output the result, updated state, and a memory
% value.
%
% Inputs:
%         layer - Layer to forward propagate through
%         X     - Layer input data
% Outputs:
%         Z      - Output of layer forward function
%         state  - (Optional) Updated layer state
%         memory - (Optional) Memory value for custom backward
%                  function
%
%  - For layers with multiple inputs, replace X with X1,...,XN,
%    where N is the number of inputs.
%  - For layers with multiple outputs, replace Z with
%    Z1,...,ZM, where M is the number of outputs.
%  - For layers with multiple state parameters, replace state
%    with state1,...,stateK, where K is the number of state
%    parameters.

% Define layer forward function here.
end

function layer = resetState(layer)
% (Optional) Reset layer state.

% Define reset state function here.
end

function [dLdX,dLdW,dLdSin] = backward(layer,X,Z,dLdZ,dLdSout,memory)
% (Optional) Backward propagate the derivative of the loss
% function through the layer.
%
% Inputs:
%         layer   - Layer to backward propagate through
%         X       - Layer input data
%         Z       - Layer output data
%         dLdZ    - Derivative of loss with respect to layer
%                   output
%         dLdSout - (Optional) Derivative of loss with respect
%                   to state output
%         memory  - Memory value from forward function
% Outputs:
%         dLdX   - Derivative of loss with respect to layer input
%         dLdW   - (Optional) Derivative of loss with respect to
%                  learnable parameter
%         dLdSin - (Optional) Derivative of loss with respect to
%                  state input
%
%  - For layers with state parameters, the backward syntax must
%    include both dLdSout and dLdSin, or neither.
%  - 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 Z and dlZ with
%    Z1,...,ZM and dLdZ,...,dLdZM, 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.

% Define layer backward function here.
end
end
end

### Formatted Inputs and Outputs

Using dlarray objects makes working with high dimensional data easier by allowing you to label the dimensions. For example, you can label which dimensions correspond to spatial, time, channel, and batch dimensions using the "S", "T", "C", and "B" labels, respectively. For unspecified and other dimensions, use the "U" label. For dlarray object functions that operate over particular dimensions, you can specify the dimension labels by formatting the dlarray object directly, or by using the DataFormat option.

Using formatted dlarray objects in custom layers also allows you to define layers where the inputs and outputs have different formats, such as layers that permute, add, or remove dimensions. For example, you can define a layer that takes as input a mini-batch of images with format "SSCB" (spatial, spatial, channel, batch) and output a mini-batch of sequences with format "CBT" (channel, batch, time). Using formatted dlarray objects also allows you to define layers that can operate on data with different input formats, for example, layers that support inputs with formats "SSCB" (spatial, spatial, channel, batch) and "CBT" (channel, batch, time).

If you do not specify a backward function, then the layer functions, by default, receive unformatted dlarray objects as input. To specify that the layer receives formatted dlarray objects as input and also outputs formatted dlarray objects, also inherit from the nnet.layer.Formattable class when defining the custom layer.

For an example showing how to define a custom layer with formatted inputs, see Define Custom Deep Learning Layer with Formatted Inputs.

### Intermediate Layer Properties

Declare the layer properties in the properties section of the class definition.

By default, custom intermediate layers have these properties. Do not declare these properties in the properties section.

PropertyDescription
NameLayer 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 set to ''.
Description

One-line description of the layer, specified as a string scalar or a character vector. This description appears when the layer is displayed in a Layer array.

If you do not specify a layer description, then the software displays the layer class name.

Type

Type of the layer, specified as a character vector or a string scalar. The value of Type appears when the layer is displayed in a Layer array.

If you do not specify a layer type, then the software displays the layer class name.

NumInputsNumber of inputs of the layer, specified as a positive integer. If you do not specify this value, then the software automatically sets NumInputs to the number of names in InputNames. The default value is 1.
InputNamesInput names of the layer, specified as a cell array of character vectors. If you do not specify this value and NumInputs is greater than 1, then the software automatically sets InputNames to {'in1',...,'inN'}, where N is equal to NumInputs. The default value is {'in'}.
NumOutputsNumber of outputs of the layer, specified as a positive integer. If you do not specify this value, then the software automatically sets NumOutputs to the number of names in OutputNames. The default value is 1.
OutputNamesOutput names of the layer, specified as a cell array of character vectors. If you do not specify this value and NumOutputs is greater than 1, then the software automatically sets OutputNames to {'out1',...,'outM'}, where M is equal to NumOutputs. The default value is {'out'}.

If the layer has no other properties, then you can omit the properties section.

Tip

If you are creating a layer with multiple inputs, then you must set either the NumInputs or InputNames properties in the layer constructor. If you are creating a layer with multiple outputs, then you must set either the NumOutputs or OutputNames properties in the layer constructor. For an example, see Define Custom Deep Learning Layer with Multiple Inputs.

#### Learnable Parameters

Declare the layer learnable parameters in the properties (Learnable) section of the class definition. You can specify numeric arrays or dlnetwork objects as learnable parameters. If the dlnetwork object has both learnable and state parameters (for example, a dlnetwork object that contains an LSTM layer), then you must specify it in the properties (Learnable, State) section. If the layer has no learnable parameters, then you can omit the properties sections with the Learnable attribute.

Optionally, you can specify the learning rate factor and the L2 factor of the learnable parameters. By default, each learnable parameter has its learning rate factor and L2 factor set to 1. For both built-in and custom layers, you can set and get the learn rate factors and L2 regularization factors using the following functions.

FunctionDescription
setLearnRateFactorSet the learn rate factor of a learnable parameter.
setL2FactorSet the L2 regularization factor of a learnable parameter.
getLearnRateFactorGet the learn rate factor of a learnable parameter.
getL2FactorGet the L2 regularization factor of a learnable parameter.

To specify the learning rate factor and the L2 factor of a learnable parameter, use the syntaxes layer = setLearnRateFactor(layer,parameterName,value) and layer = setL2Factor(layer,parameterName,value), respectively.

To get the value of the learning rate factor and the L2 factor of a learnable parameter, use the syntaxes getLearnRateFactor(layer,parameterName) and getL2Factor(layer,parameterName) respectively.

For example, this syntax sets the learn rate factor of the learnable parameter with the name "Alpha" to 0.1.

layer = setLearnRateFactor(layer,"Alpha",0.1);

#### State Parameters

For stateful layers, such as recurrent layers, declare the layer state parameters in the properties (State) section of the class definition. For dlnetwork objects that have both learnable and state parameters (for example, a dlnetwork object that contains an LSTM layer), then you must specify it in the properties (Learnable, State) section. If the layer has no state parameters, then you can omit the properties sections with the State attribute.

If the layer has state parameters, then the forward functions must also return the updated layer state. For more information, see Forward Functions.

To specify a custom reset state function, include a function with syntax layer = resetState(layer) in the class definition. For more information, see Reset State Function.

### Forward Functions

Some layers behave differently during training and during prediction. For example, a dropout layer performs dropout only during training and has no effect during prediction. A layer uses one of two functions to perform a forward pass: predict or forward. If the forward pass is at prediction time, then the layer uses the predict function. If the forward pass is at training time, then the layer uses the forward function. If you do not require two different functions for prediction time and training time, then you can omit the forward function. In this case, the layer uses predict at training time.

If the layer has state parameters, then the forward functions must also return the updated layer state parameters as numeric arrays.

If you define both a custom forward function and a custom backward function, then the forward function must return a memory output.

The predict function syntax depends on the type of layer.

• Z = predict(layer,X) forwards the input data X through the layer and outputs the result Z, where layer has a single input, a single output.

• [Z,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 Z with Z1,...,ZM, 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 Z1,…,ZN. In this case, varargout is a cell array of the outputs, where varargout{j} corresponds to Zj.

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. Using the dlnetwork object predict function ensures that the software uses the correct layer operations for prediction.

The forward function syntax depends on the type of layer:

• Z = forward(layer,X) forwards the input data X through the layer and outputs the result Z, where layer has a single input, a single output.

• [Z,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 Z with Z1,...,ZM, 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 Z1,…,ZN. In this case, varargout is a cell array of the outputs, where varargout{j} corresponds to Zj.

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. Using the dlnetwork object forward function ensures that the software uses the correct layer operations for training.

The dimensions of the inputs depend on the type of data and the output of the connected layers:

Layer InputInput SizeObservation Dimension
Feature vectorsc-by-N, where c corresponds to the number of channels and N is the number of observations.2
2-D imagesh-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 imagesh-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 sequencesc-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 sequencesh-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 sequencesh-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 layers that output sequences, the layers can output sequences of any length or output data with no time dimension. Note that when training a network that outputs sequences using the trainNetwork function, the lengths of the input and output sequences must match.

The outputs of the custom layer forward functions must not be complex. If the predict or forward functions of your custom layer involve complex numbers, convert all outputs to real values before returning them. Using complex numbers in the predict or forward functions of your custom layer can lead to complex learnable parameters. If you are using automatic differentiation (in other words, you are not writing a backward function for your custom layer) then convert all learnable parameters to real values at the beginning of the function computation. Doing so ensures that the outputs of automatically generated backward functions are not complex.

### Reset State Function

When DAGNetwork or SeriesNetwork objects contain layers with state parameters, you can make predictions and update the layer states using the predictAndUpdateState and classifyAndUpdateState functions. You can reset the network state using the resetState function.

The resetState function for DAGNetwork, SeriesNetwork, and dlnetwork objects, by default, has no effect on custom layers with state parameters. To define the layer behavior for the resetState function for network objects, define the optional layer resetState function in the layer definition that resets the state parameters.

The resetState function must have the syntax layer = resetState(layer), where the returned layer has the state properties reset.

### Backward Function

The layer backward function computes the derivatives of the loss with respect to the input data and then outputs (backward propagates) results to the previous layer. If the layer has learnable parameters (for example, layer weights), then backward also computes the derivatives of the learnable parameters. When using the trainNetwork function, the layer automatically updates the learnable parameters using these derivatives during the backward pass.

Defining the backward function is optional. If you do not specify a backward function, and the layer forward functions support dlarray objects, then the software automatically determines the backward function using automatic differentiation. For a list of functions that support dlarray objects, see List of Functions with dlarray Support. Define a custom backward function when you want to:

• Use a specific algorithm to compute the derivatives.

• Use operations in the forward functions that do not support dlarray objects.

Custom layers with learnable dlnetwork objects do not support custom backward functions.

To define a custom backward function, create a function named backward.

The backward function syntax depends on the type of layer.

• dLdX = backward(layer,X,Z,dLdZ,memory) returns the derivatives dLdX of the loss with respect to the layer input, where layer has a single input and a single output. Z corresponds to the forward function output and dLdZ corresponds to the derivative of the loss with respect to Z. The function input memory corresponds to the memory output of the forward function.

• [dLdX,dLdW] = backward(layer,X,Z,dLdZ,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,Z,dLdZ,dLdSout,memory) also returns the derivative dLdSin of the loss with respect to the state input using any of the previous syntaxes, 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,Z,dLdZ,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 using any of the previous syntaxes, 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 Z and dLdZ with Z1,...,ZM and dLdZ1,...,dLdZM, 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 states 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.

The values of X and Z are the same as in the forward functions. The dimensions of dLdZ are the same as the dimensions of Z.

The dimensions and data type of dLdX are the same as the dimensions and data type of X. The dimensions and data types of dLdW are the same as the dimensions and data types of W.

To calculate the derivatives of the loss, you can use the chain rule:

$\frac{\partial L}{\partial {X}^{\left(i\right)}}=\sum _{j}\frac{\partial L}{\partial {Z}_{j}}\frac{\partial {Z}_{j}}{\partial {X}^{\left(i\right)}}$

$\frac{\partial L}{\partial {W}_{i}}=\sum _{j}\frac{\partial L}{\partial {Z}_{j}}\frac{\partial {Z}_{j}}{\partial {W}_{i}}$

When using the trainNetwork function, the layer automatically updates the learnable parameters using the derivatives dLdW during the backward pass.

For an example showing how to define a custom backward function, see Specify Custom Layer Backward Function.

The outputs of the custom layer backward function must not be complex. If your backward function involves complex numbers, then convert all outputs of the backward function to real values before returning them.

#### GPU Compatibility

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 Support by Release (Parallel Computing Toolbox). For more information on working with GPUs in MATLAB, see GPU Computing in MATLAB (Parallel Computing Toolbox).

#### Code Generation Compatibility

To create a custom layer that supports code generation:

• The layer must specify the pragma %#codegen in the layer definition.

• The inputs of predict must be:

• Consistent in dimension. Each input must have the same number of dimensions.

• Consistent in batch size. Each input must have the same batch size.

• The outputs of predict must be consistent in dimension and batch size with the layer inputs.

• Nonscalar properties must have type single, double, or character array.

• Scalar properties must have type numeric, logical, or string.

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).

For an example showing how to create a custom layer that supports code generation, see Define Custom Deep Learning Layer for Code Generation.

#### Network Composition

To create a custom layer that itself defines a layer graph, you can declare a dlnetwork object as a learnable parameter in the properties (Learnable) section of the layer definition. This method is known as network composition. You can use network composition to:

• Create a single custom layer that represents a block of learnable layers, for example, a residual block.

• Create a network with control flow, for example, a network with a section that can dynamically change depending on the input data.

• Create a network with loops, for example, a network with sections that feed the output back into itself.

For nested networks that have both learnable and state parameters, for example, networks with batch normalization or LSTM layers, declare the network in the properties (Learnable, State) section of the layer definition.

### GPU Compatibility

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 Support by Release (Parallel Computing Toolbox). For more information on working with GPUs in MATLAB, see GPU Computing in MATLAB (Parallel Computing Toolbox).

### Check Validity of Layer

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,validInputSize)
where layer is an instance of the layer, validInputSize is a vector or cell array specifying the valid input sizes to the layer. To check with multiple observations, use the ObservationDimension option. To 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 tests, specify a smaller valid input size.

#### Check Validity of Custom Layer Using checkLayer

Check the layer validity of the 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 its validity using checkLayer. Specify the valid input size to be 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.

layer = preluLayer(20);
validInputSize = [24 24 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: 1.9312 seconds.

Here, the function does not detect any issues with the layer.