Define Custom Deep Learning Layer with Learnable Parameters
If Deep Learning Toolbox™ does not provide the layer you require for your task, then you can define your own custom layer using this example as a guide. For a list of built-in layers, see List of Deep Learning Layers.
To define a custom deep learning layer, you can use the template provided in this example, which takes you through these steps:
Name the layer — Give the layer a name so that you can use it in MATLAB®.
Declare the layer properties — Specify the properties of the layer, including learnable parameters and state parameters.
Create the constructor function (optional) — Specify how to construct the layer and initialize its properties. If you do not specify a constructor function, then at creation, the software initializes the
Name
,Description
, andType
properties with[]
and sets the number of layer inputs and outputs to1
.Create initialize function (optional) — Specify how to initialize the learnable and state parameters when the software initializes the network. If you do not specify an initialize function, then the software does not initialize parameters when it initializes the network.
Create forward functions — Specify how data passes forward through the layer (forward propagation) at prediction time and at training time.
Create reset state function (optional) — Specify how to reset state parameters.
Create a backward function (optional) — Specify the derivatives of the loss with respect to the input data and the learnable parameters (backward propagation). If you do not specify a backward function, then the forward functions must support
dlarray
objects.
When you define the layer functions, you can use dlarray
objects.
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 the format "SSCB"
(spatial, spatial, channel,
batch) and output a mini-batch of sequences with the 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 the formats "SSCB"
(spatial, spatial, channel,
batch) and "CBT"
(channel, batch, time).
dlarray
objects also enable support for automatic differentiation.
Consequently, if your forward functions fully support dlarray
objects, then
defining the backward function is optional.
To enable support for using formatted dlarray
objects in custom layer forward functions, also inherit from the nnet.layer.Formattable
class when defining the custom layer. For an example, see Define Custom Deep Learning Layer with Formatted Inputs.
This example shows how to create a SReLU layer, which is a layer with four learnable parameters and use it in a neural network. A SReLU layer performs a thresholding operation, where for each channel, the layer scales values outside an interval. The interval thresholds and scaling factors are learnable parameters. [1].
The SReLU operation is given by
where xi is the input on channel i, tli and tri are the left and right thresholds on channel i, respectively, and ali and ari are the left and right scaling factors on channel i, respectively. These threshold values and scaling factors are learnable parameter, which the layer learns during training.
Custom Layer Template
Copy the custom layer template into a new file in MATLAB. This template gives the structure of a layer class definition. It outlines:
The optional
properties
blocks for the layer properties, learnable parameters, and state parameters.The optional layer constructor function.
The optional
initialize
function.The
predict
function and the optionalforward
function.The optional
resetState
function for layers with state properties.The optional
backward
function.
classdef myLayer < nnet.layer.Layer % ... % & nnet.layer.Formattable ... % (Optional) % & nnet.layer.Acceleratable % (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 parameters. % 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 layer = initialize(layer,layout) % (Optional) Initialize layer learnable and state parameters. % % Inputs: % layer - Layer to initialize % layout - Data layout, specified as a networkDataLayout % object % % Outputs: % layer - Initialized layer % % - For layers with multiple inputs, replace layout with % layout1,...,layoutN, where N is the number of inputs. % Define layer initialization function here. end function [Y,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: % Y - 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 Y with % Y1,...,YM, 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 [Y,state,memory] = forward(layer,X) % (Optional) Forward input data through the layer at training % time and output the result, the updated state, and a memory % value. % % Inputs: % layer - Layer to forward propagate through % X - Layer input data % Outputs: % Y - 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 Y with % Y1,...,YM, 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,Y,dLdY,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 % Y - Layer output data % dLdY - 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 Y and dLdY with % Y1,...,YM and dLdY,...,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. % Define layer backward function here. end end end
Name Layer and Specify Superclasses
First, give the layer a name. In the first line of the class file, replace the
existing name myLayer
with sreluLayer
.
classdef sreluLayer < nnet.layer.Layer % ... % & nnet.layer.Formattable ... % (Optional) % & nnet.layer.Acceleratable % (Optional) ... end
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.
The layer functions support acceleration, so also inherit from nnet.layer.Acceleratable
. For
more information about accelerating custom layer functions, see Custom Layer Function Acceleration. The layer does not require formattable
inputs, so remove the optional nnet.layer.Formattable
superclass.
classdef sreluLayer < nnet.layer.Layer ... & nnet.layer.Acceleratable ... end
Next, rename the myLayer
constructor function (the first function
in the methods
section) so that it has the same name as the
layer.
methods function layer = sreluLayer() ... end ... end
Save the Layer
Save the layer class file in a new file named sreluLayer.m
.
The file name must match the layer name. To use the layer, you must save the file in
the current folder or in a folder on the MATLAB path.
Declare Properties and Learnable Parameters
Declare the layer properties in the properties
section and declare
learnable parameters by listing them in the properties (Learnable)
section.
By default, custom layers have these properties. Do not declare these properties in the
properties
section.
Property | Description |
---|---|
Name | Layer name, specified as a character vector or string scalar.
For Layer array input, the trainnet and
dlnetwork functions automatically assign
names to layers with the name "" . |
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 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 If you do not specify a layer type, then the software displays the layer class name. |
NumInputs | Number 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. |
InputNames | Input 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'} . |
NumOutputs | Number 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. |
OutputNames | Output 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.
A SReLU layer does not require any additional properties, so you can remove the
properties
section.
A SReLU layer has four learnable parameters: the left and right scaling and threshold
factors, respectively. Declare these learnable parameters in the properties
(Learnable)
section and name them LeftSlope
,
RightSlope
, LeftThreshold
, and
RightThreshold
, respectively.
properties (Learnable)
% Layer learnable parameters
LeftSlope
RightSlope
LeftThreshold
RightThreshold
end
Create Constructor Function
Create the function that constructs the layer and initializes the layer properties. Specify any variables required to create the layer as inputs to the constructor function.
The SReLU layer constructor function requires one optional argument (the layer name).
Specify one input argument named args
in the
sreluLayer
function that corresponds to the optional name-value
argument. Add a comment to the top of the function that explains the syntax of the
function.
function layer = sreluLayer(args) % layer = sreluLayercreates a SReLU layer. % % layer = sreluLayer(Name=name) also specifies the % layer name ... end
Initialize Layer Properties
Initialize the layer properties in the constructor function. Replace the comment % Layer constructor function goes here
with code that initializes the layer properties. Do not initialize learnable or state parameters in the constructor function, initialize them in the initialize
function instead.
Parse the input arguments using an arguments
block and set the
Name
property.
arguments args.Name = ""; end % Set layer name. layer.Name = args.Name;
Give the layer a one-line description by setting the
Description
property of the layer. Set the description to
describe the type of layer.
% Set layer description. layer.Description = "SReLU";
View the completed constructor function.
function layer = sreluLayer(args)
% layer = sreluLayer creates a SReLU layer.
%
% layer = sreluLayer(Name=name) also specifies the
% layer name.
arguments
args.Name = "";
end
% Set layer name.
layer.Name = args.Name;
% Set layer description.
layer.Description = "SReLU";
end
With this constructor function, the command
sreluLayer(Name="srelu")
creates a SReLU layer with the name
"srelu"
.
Create Initialize Function
Create the function that initializes the layer learnable and state parameters when the software initializes the network. Ensure that the function only initializes learnable and state parameters when the property is empty, otherwise the software can overwrite when you load the network from a MAT file.
To initialize the learnable parameters, generate a random vectors with the same number of channels as the input data.
Because the size of the input data is unknown until the network is ready to use, you must create an initialize function that initializes the learnable and state parameters using networkDataLayout
objects that the software provides to the function. Network data layout objects contain information about the sizes and formats of expected input data. Create an initialize function that uses the size and format information to initialize learnable and state parameters such that they have the correct size.
The learnable parameters have the same number of dimensions as the input observations,
where the channel dimension has the same size as the channel dimension of the input
data, and the remaining dimensions are singleton. Create an
initialize
function that extracts the size and format information
from the input networkDataLayout
object and initializes the learnable
parameters with the same number of channels.
function layer = initialize(layer,layout)
% layer = initialize(layer,layout) initializes the layer
% learnable parameters using the specified input layout.
% Find number of channels.
idx = finddim(layout,"C");
numChannels = layout.Size(idx);
% Initialize empty learnable parameters.
sz = ones(1,numel(layout.Size);
sz(idx) = numChannels;
if isempty(layer.LeftSlope)
layer.LeftSlope = rand(sz);
end
if isempty(layer.RightSlope)
layer.RightSlope = rand(sz);
end
if isempty(layer.LeftThreshold)
layer.LeftThreshold = rand(sz);
end
if isempty(layer.RightThreshold)
layer.RightThreshold = rand(sz);
end
end
Create Forward Functions
Create the layer forward functions to use at prediction time and training time.
Create a function named predict
that propagates the data forward
through the layer at prediction time and outputs the result.
The predict
function syntax depends on the type of layer.
Y = predict(layer,X)
forwards the input dataX
through the layer and outputs the resultY
, wherelayer
has a single input and a single output.[Y,state] = predict(layer,X)
also outputs the updated state parameterstate
, wherelayer
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
withX1,...,XN
, whereN
is the number of inputs. TheNumInputs
property must matchN
.For layers with multiple outputs, replace
Y
withY1,...,YM
, whereM
is the number of outputs. TheNumOutputs
property must matchM
.For layers with multiple state parameters, replace
state
withstate1,...,stateK
, whereK
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.
Because a SReLU layer has only one input and one output, the syntax for
predict
for a SReLU layer is Y =
predict(layer,X)
.
By default, the layer uses predict
as the forward function at
training time. To use a different forward function at training time, or retain a value
required for a custom backward function, you must also create a function named
forward
.
The dimensions of the inputs depend on the type of data and the output of the connected layers:
Layer Input | Example | |
---|---|---|
Shape | Data Format | |
2-D images |
h-by-w-by-c-by-N numeric array, where h, w, c and N are the height, width, number of channels of the images, and number of observations, respectively. | "SSCB" |
3-D images | h-by-w-by-d-by-c-by-N numeric array, where h, w, d, c and N are the height, width, depth, number of channels of the images, and number of image observations, respectively. | "SSSCB" |
Vector sequences |
c-by-N-by-s matrix, where c is the number of features of the sequence, N is the number of sequence observations, and s is the sequence length. | "CBT" |
2-D image sequences |
h-by-w-by-c-by-N-by-s array, where h, w, and c correspond to the height, width, and number of channels of the image, respectively, N is the number of image sequence observations, and s is the sequence length. | "SSCBT" |
3-D image sequences |
h-by-w-by-d-by-c-by-N-by-s array, where h, w, d, and c correspond to the height, width, depth, and number of channels of the image, respectively, N is the number of image sequence observations, and s is the sequence length. | "SSSCBT" |
Features | c-by-N array, where c is the number of features, and N is the number of observations. | "CB" |
For layers that output sequences, the layers can output sequences of any length or output data with no time dimension.
The forward
function propagates the data forward through the layer
at training time and also outputs a memory value.
The forward
function syntax depends on the type of layer:
Y = forward(layer,X)
forwards the input dataX
through the layer and outputs the resultY
, wherelayer
has a single input and a single output.[Y,state] = forward(layer,X)
also outputs the updated state parameterstate
, wherelayer
has a single state parameter.[__,memory] = forward(layer,X)
also returns a memory value for a custombackward
function using any of the previous syntaxes. If the layer has both a customforward
function and a custombackward
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
withX1,...,XN
, whereN
is the number of inputs. TheNumInputs
property must matchN
.For layers with multiple outputs, replace
Y
withY1,...,YM
, whereM
is the number of outputs. TheNumOutputs
property must matchM
.For layers with multiple state parameters, replace
state
withstate1,...,stateK
, whereK
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.
The SReLU operation is given by
where xi is the input on channel i, tli and tri are the left and right thresholds on channel i, respectively, and ali and ari are the left and right scaling factors on channel i, respectively. These threshold values and scaling factors are learnable parameter, which the layer learns during training.
Implement this operation in predict
. The SReLU layer does not
require memory or a different forward function for training, so you can remove the
forward
function from the class file. Add a comment to the top of
the function that explains the syntaxes of the function.
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)
.
function Y = predict(layer, X)
% Y = predict(layer, X) forwards the input data X through the
% layer and outputs the result Y.
tl = layer.LeftThreshold;
al = layer.LeftSlope;
tr = layer.RightThreshold;
ar = layer.RightSlope;
Y = (X <= tl) .* (tl + al.*(X-tl)) ...
+ ((tl < X) & (X < tr)) .* X ...
+ (tr <= X) .* (tr + ar.*(X-tr));
end
Because the predict
function uses only functions that support
dlarray
objects, defining the backward
function is
optional. For a list of functions that support dlarray
objects, see List of Functions with dlarray Support.
Completed Layer
View the completed layer class file.
classdef sreluLayer < nnet.layer.Layer ... & nnet.layer.Acceleratable % Example custom SReLU layer. properties (Learnable) % Layer learnable parameters LeftSlope RightSlope LeftThreshold RightThreshold end methods function layer = sreluLayer(args) % layer = sreluLayer creates a SReLU layer. % % layer = sreluLayer(Name=name) also specifies the % layer name. arguments args.Name = ""; end % Set layer name. layer.Name = args.Name; % Set layer description. layer.Description = "SReLU"; end function layer = initialize(layer,layout) % layer = initialize(layer,layout) initializes the layer % learnable parameters using the specified input layout. % Find number of channels. idx = finddim(layout,"C"); numChannels = layout.Size(idx); % Initialize empty learnable parameters. sz = ones(1,numel(layout.Size); sz(idx) = numChannels; if isempty(layer.LeftSlope) layer.LeftSlope = rand(sz); end if isempty(layer.RightSlope) layer.RightSlope = rand(sz); end if isempty(layer.LeftThreshold) layer.LeftThreshold = rand(sz); end if isempty(layer.RightThreshold) layer.RightThreshold = rand(sz); end end function Y = predict(layer, X) % Y = predict(layer, X) forwards the input data X through the % layer and outputs the result Y. tl = layer.LeftThreshold; al = layer.LeftSlope; tr = layer.RightThreshold; ar = layer.RightSlope; Y = (X <= tl) .* (tl + al.*(X-tl)) ... + ((tl < X) & (X < tr)) .* X ... + (tr <= X) .* (tr + ar.*(X-tr)); end end end
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 Computing Requirements (Parallel Computing Toolbox). For more information on working with GPUs in MATLAB, see GPU Computing in MATLAB (Parallel Computing Toolbox).
In this example, the MATLAB functions used in predict
all support
dlarray
objects, so the layer is GPU compatible.
Check Validity of Custom Layer Using checkLayer
Check the layer validity of the 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 networkDataFormat
object that specifies the expected input size and format 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 format as "SSCB"
(spatial, spatial, channel, batch).
validInputSize = [24 24 20 128];
layout = networkDataLayout(validInputSize,"SSCB");
Check the layer validity using checkLayer
.
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: 0.1942 seconds.
The function does not detect any issues with the layer.
Include Custom Layer in Network
You can use a custom layer in the same way as any other layer in Deep Learning Toolbox. This section shows how to create and train a network for digit classification using the SReLU layer you created earlier.
Load the example training data.
load DigitsDataTrain
Create a layer array containing the custom layer sreluLayer
, attached to this example as a supporting file. To access this layer, open this example as a live script.
layers = [ imageInputLayer([28 28 1]) convolution2dLayer(5,20) batchNormalizationLayer sreluLayer fullyConnectedLayer(10) softmaxLayer];
Set the training options and train the neural network using the trainnet
function. For classification, use cross-entropy loss. By default, the trainnet
function uses a GPU if one is available. Training on a GPU requires a Parallel Computing Toolbox™ license and a supported GPU device. For information on supported devices, see GPU Computing Requirements (Parallel Computing Toolbox). Otherwise, the trainnet
function uses the CPU. To specify the execution environment, use the ExecutionEnvironment
training option.
options = trainingOptions("adam",MaxEpochs=10,Metrics="accuracy"); net = trainnet(XTrain,labelsTrain,layers,"crossentropy",options);
Iteration Epoch TimeElapsed LearnRate TrainingLoss TrainingAccuracy _________ _____ ___________ _________ ____________ ________________ 1 1 00:00:01 0.001 2.6767 10.156 50 2 00:00:04 0.001 0.68513 74.219 100 3 00:00:07 0.001 0.46812 86.719 150 4 00:00:10 0.001 0.24365 91.406 200 6 00:00:12 0.001 0.095949 99.219 250 7 00:00:15 0.001 0.04571 100 300 8 00:00:18 0.001 0.050645 100 350 9 00:00:20 0.001 0.03325 100 390 10 00:00:23 0.001 0.032926 100 Training stopped: Max epochs completed
Load the test data.
load DigitsDataTest
Test the neural network using the testnet
function. For single-label classification, evaluate the accuracy. By default, the testnet
function uses a GPU if one is available. To select the execution environment manually, use the ExecutionEnvironment
argument of the testnet
function.
accuracy = testnet(net,XTest,labelsTest,"accuracy")
accuracy = 95.9800
References
[1] Hu, Xiaobin, Peifeng Niu, Jianmei Wang, and Xinxin Zhang. “A Dynamic Rectified Linear Activation Units.” IEEE Access 7 (2019): 180409–16. https://doi.org/10.1109/ACCESS.2019.2959036.
See Also
trainnet
| trainingOptions
| dlnetwork
| functionLayer
| checkLayer
| setLearnRateFactor
| setL2Factor
| getLearnRateFactor
| getL2Factor
| findPlaceholderLayers
| replaceLayer
| PlaceholderLayer
| networkDataLayout
Related Topics
- Define Custom Deep Learning Layers
- Define Custom Deep Learning Layer with Multiple Inputs
- Define Custom Deep Learning Layer with Formatted Inputs
- Define Custom Recurrent Deep Learning Layer
- Specify Custom Layer Backward Function
- Define Custom Deep Learning Layer for Code Generation
- Define Nested Deep Learning Layer Using Network Composition
- Check Custom Layer Validity