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Define Custom Deep Learning Layer with Formatted Inputs

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 the following steps:

  1. Name the layer — Give the layer a name so that you can use it in MATLAB®.

  2. Declare the layer properties — Specify the properties of the layer including learnable parameters and state parameters.

  3. Create a 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, and Type properties with [] and sets the number of layer inputs and outputs to 1.

  4. Create forward functions — Specify how data passes forward through the layer (forward propagation) at prediction time and at training time.

  5. Create reset state function (optional) — Specify how to reset state parameters.

  6. 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 defining 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 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).

dlarray objects also enable support for automatic differentiation. This means that if your forward functions fully support dlarray objects, then defining the backward function is optional.

This example shows how to create a project and reshape layer, which is a layer commonly used in generative adversarial networks (GANs) that takes an array of noise with format "CB" (channel, batch) and projects and reshapes it to a mini-batch of images with format "SSCB" (spatial, spatial, channel, batch) using fully connected, reshape, and relabel operations.

Intermediate Layer Template

Copy the intermediate layer template into a new file in MATLAB. 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.

  • The layer constructor function.

  • The predict function and the optional forward function.

  • The optional resetState function for layers with state properties.

  • The optional backward 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

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 projectAndReshapeLayer.

classdef projectAndRehapeLayer < nnet.layer.Layer % & nnet.layer.Formattable (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.

Because a project and reshape layer outputs data with different dimensions as the input data, that is, it outputs data with added spatial dimensions, the layer must also inherit from nnet.layer.Formattable. This enables the layer to receive and output formatted dlarray objects.

Next, specify to inherit from both the nnet.layer.Layer and nnet.layer.Formattable superclasses.

classdef projectAndRehapeLayer < nnet.layer.Layer & nnet.layer.Formattable
    ...
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 = projectAndReshapeLayer()           
            ...
        end

        ...
     end

Save the Layer

Save the layer class file in a new file named projectAndReshapeLayer.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 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.

A project and reshape layer requires an additional property that holds the layer output size. Specify a single property with name OutputSize in the properties section.

    properties
        % Output size
        OutputSize
    end

A project and reshape layer has two learnable parameters: the weights and the biases of the fully connect operation. Declare these learnable parameter in the properties (Learnable) section and call the parameters Weights and Bias, respectively.

    properties (Learnable)
    % Layer learnable parameters
        Weights 
        Bias 
    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 project and reshape layer constructor function requires two input arguments:

  • Layer output size

  • Number of channels

  • Layer name (optional, with default name '')

In the constructor function projectAndReshapeLayer, specify the two required input arguments named outputSize and numChannels, and the optional arguments as name-value pairs with the name NameValueArgs. Add a comment to the top of the function that explains the syntax of the function.

        function layer = projectAndReshapeLayer(outputSize,numChannels,NameValueArgs)
            % layer = projectAndReshapeLayer(outputSize,numChannels)
            % creates a projectAndReshapeLayer object that projects and
            % reshapes the input to the specified output size using and
            % specifies the number of input channels.
            %
            % layer = projectAndReshapeLayer(outputSize,numChannels,'Name',name)
            % also specifies the layer name.
            
            ...
        end

Parse Input Arguments

Parse the input arguments using an arguments block. List the arguments in the same order as the function syntax and specify the default values. Then, extract the values from the NameValueArgs input.

            % Parse input arguments.
            arguments
                outputSize
                numChannels
                NameValueArgs.Name = ''
            end
            
            name = NameValueArgs.Name;

Initialize Layer Properties

Initialize the layer properties, including learnable parameters in the constructor function. Replace the comment % Layer constructor function goes here with code that initializes the layer properties.

Set the Name property to the input argument name.

            % Set layer name.
            layer.Name = 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 and its size.

            % Set layer description.
            layer.Description = "Project and reshape layer with output size " + join(string(outputSize));

Specify the type of the layer by setting the Type property. The value of Type appears when the layer is displayed in a Layer array.

            % Set layer type.
            layer.Type = "Project and Reshape";

Set the layer property OutputSize to the specified input value.

            % Set output size.
            layer.OutputSize = outputSize;

A project and reshape layer applies a fully connect operation to project the input to batch of images. Initialize the weights using the Glorot initializer and initialize the bias with an array of zeros. The functions initializeGlorot and initializeZeros are attached to the example Train Generative Adversarial Network (GAN) as supporting files. To access these functions, open this example as a live script. For more information about initializing learnable parameters for deep learning operations, see Initialize Learnable Parameters for Model Function.

            % Initialize fully connect weights and bias.
            sz = [prod(outputSize) numChannels];
            numOut = prod(outputSize);
            numIn = numChannels;
            layer.Weights = initializeGlorot(sz,numOut,numIn);
            layer.Bias = initializeZeros([prod(outputSize) 1]);

View the completed constructor function.

        function layer = projectAndReshapeLayer(outputSize,numChannels,NameValueArgs)
            % layer = projectAndReshapeLayer(outputSize,numChannels)
            % creates a projectAndReshapeLayer object that projects and
            % reshapes the input to the specified output size using and
            % specifies the number of input channels.
            %
            % layer = projectAndReshapeLayer(outputSize,numChannels,'Name',name)
            % also specifies the layer name.
                                  
            % Parse input arguments.
            arguments
                outputSize
                numChannels
                NameValueArgs.Name = '';
            end
            
            name = NameValueArgs.Name;
            
            % Set layer name.
            layer.Name = name;

            % Set layer description.
            layer.Description = "Project and reshape layer with output size " + join(string(outputSize));
            
            % Set layer type.
            layer.Type = "Project and Reshape";
            
            % Set output size.
            layer.OutputSize = outputSize;
            
            % Initialize fully connect weights and bias.
            sz = [prod(outputSize) numChannels];
            numOut = prod(outputSize);
            numIn = numChannels;
            layer.Weights = initializeGlorot(sz,numOut,numIn);
            layer.Bias = initializeZeros([prod(outputSize) 1]);
        end

With this constructor function, the command projectAndReshapeLayer([4 4 512],100,'Name','proj'); creates a project and reshape layer with name 'proj' that projects the input arrays with size 100 to a batch of 512 4-by-4 images.

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.

  • 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.

Because a project and reshape layer has only one input and one output, the syntax for predict for a project and reshape layer is Z = 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 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.

Because the custom layer inherits from the nnet.layer.Formattable class, the layer receives formatted dlarray objects with labels corresponding to the output of the previous layer.

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:

  • 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 project and reshape operation consists of a three operations:

  • Apply a fully connect operations with the learnable weights and biases.

  • Reshape the output to the specified output size.

  • Relabel the dimensions so that the output has format 'SSCB' (spatial, spatial, channel, batch)

Implement this operation in the predict function. The project and reshape 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.

        function Z = predict(layer, X)
            % Forward input data through the layer at prediction time and
            % output the result.
            % 
            % Inputs:
            %         layer - Layer to forward propagate through
            %         X     - Input data, specified as a formatted dlarray
            %                 with a 'C' and optionally a 'B' dimension.
            % Outputs:
            %         Z     - Output of layer forward function returned as 
            %                 a formatted dlarray with format 'SSCB'.

            % Fully connect.
            weights = layer.Weights;
            bias = layer.Bias;
            X = fullyconnect(X,weights,bias);
            
            % Reshape.
            outputSize = layer.OutputSize;
            Z = reshape(X, outputSize(1), outputSize(2), outputSize(3), []);
            
            % Relabel.
            Z = dlarray(Z,'SSCB');
        end

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 Z = zeros(sz,"like",X).

Because the predict function only uses 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 projectAndReshapeLayer < nnet.layer.Layer & nnet.layer.Formattable
    % Example project and reshape layer.

    properties
        % Output size
        OutputSize
    end

    properties (Learnable)
    % Layer learnable parameters
        Weights 
        Bias 
    end
    
    methods
        function layer = projectAndReshapeLayer(outputSize,numChannels,NameValueArgs)
            % layer = projectAndReshapeLayer(outputSize,numChannels)
            % creates a projectAndReshapeLayer object that projects and
            % reshapes the input to the specified output size using and
            % specifies the number of input channels.
            %
            % layer = projectAndReshapeLayer(outputSize,numChannels,'Name',name)
            % also specifies the layer name.
                                  
            % Parse input arguments.
            arguments
                outputSize
                numChannels
                NameValueArgs.Name = '';
            end
            
            name = NameValueArgs.Name;
            
            % Set layer name.
            layer.Name = name;

            % Set layer description.
            layer.Description = "Project and reshape layer with output size " + join(string(outputSize));
            
            % Set layer type.
            layer.Type = "Project and Reshape";
            
            % Set output size.
            layer.OutputSize = outputSize;
            
            % Initialize fully connect weights and bias.
            sz = [prod(outputSize) numChannels];
            numOut = prod(outputSize);
            numIn = numChannels;
            layer.Weights = initializeGlorot(sz,numOut,numIn);
            layer.Bias = initializeZeros([prod(outputSize) 1]);
        end
        
        function Z = predict(layer, X)
            % Forward input data through the layer at prediction time and
            % output the result.
            % 
            % Inputs:
            %         layer - Layer to forward propagate through
            %         X     - Input data, specified as a formatted dlarray
            %                 with a 'C' and optionally a 'B' dimension.
            % Outputs:
            %         Z     - Output of layer forward function returned as 
            %                 a formatted dlarray with format 'SSCB'.

            % Fully connect.
            weights = layer.Weights;
            bias = layer.Bias;
            X = fullyconnect(X,weights,bias);
            
            % Reshape.
            outputSize = layer.OutputSize;
            Z = reshape(X, outputSize(1), outputSize(2), outputSize(3), []);
            
            % Relabel.
            Z = dlarray(Z,'SSCB');
        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 Support by Release (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.

Include Custom Layer in Network

Define the following generator network architecture for a GAN, which generates images from 1-by-1-by-100 arrays of random values:

This network:

  • Converts the random vectors of size 100 to 7-by-7-by-128 arrays using a project and reshape layer.

  • Upscales the resulting arrays to 64-by-64-by-3 arrays using a series of transposed convolution layers and ReLU layers.

Define this network architecture as a layer graph and specify the following network properties.

  • For the transposed convolution layers, specify 5-by-5 filters with a decreasing number of filters for each layer, a stride of 2, and cropping of the output on each edge.

  • For the final transposed convolution layer, specify three 5-by-5 filters corresponding to the three RGB channels of the generated images, and the output size of the previous layer.

  • At the end of the network, include a tanh layer.

To project and reshape the noise input, use the custom layer projectAndReshapeLayer.

filterSize = 5;
numFilters = 64;
numLatentInputs = 100;

projectionSize = [4 4 512];

layersG = [
    featureInputLayer(numLatentInputs,'Normalization','none','Name','in')
    projectAndReshapeLayer(projectionSize,numLatentInputs,'Name','proj');
    transposedConv2dLayer(filterSize,4*numFilters,'Name','tconv1')
    reluLayer('Name','relu1')
    transposedConv2dLayer(filterSize,2*numFilters,'Stride',2,'Cropping','same','Name','tconv2')
    reluLayer('Name','relu2')
    transposedConv2dLayer(filterSize,numFilters,'Stride',2,'Cropping','same','Name','tconv3')
    reluLayer('Name','relu3')
    transposedConv2dLayer(filterSize,3,'Stride',2,'Cropping','same','Name','tconv4')
    tanhLayer('Name','tanh')];

lgraphG = layerGraph(layersG);

To train the network with a custom training loop and enable automatic differentiation, convert the layer graph to a dlnetwork object.

dlnetG = dlnetwork(lgraphG);

See Also

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