Deep Learning Image - projectAndReshapeLayer

Hello!

I'm trying to use the example Adverse Generative Train Network (GAN) (https://www.mathworks.com/help/deeplearning/ug/train-generative-adversarial-network.html) and i and I have the following error

”Abstract classes cannot be instantiated. Class 'projectAndReshapeLayer' inherits abstract methods or properties but does not implement them. See the list of methods and properties that 'projectAndReshapeLayer'' must implement if you do not intend the class to be abstract„

Can someone help me understand how I can solve the problem?

Thanks!

Answer 1

[Yonglei GUI]

classdef projectAndReshapeLayer < nnet.layer.Layer
    properties
        % (Optional) Layer properties.
        OutputSize
    end
    properties (Learnable)
        % Layer learnable parameters.
        
        Weights
        Bias
    end
    
    methods
        function layer = projectAndReshapeLayer(outputSize, numChannels, name)
            % Create a projectAndReshapeLayer.
            
            % 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.
            fcSize = prod(outputSize);
            layer.Weights = initializeGlorot(fcSize, numChannels);
            layer.Bias = zeros(fcSize, 1, 'single');
        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 1-by-1-by-C-by-N 
            %                 dlarray, where N is the mini-batch size.
            % Outputs:
            %         Z     - Output of layer forward function returned as 
            %                 an sz(1)-by-sz(2)-by-sz(3)-by-N dlarray,
            %                 where sz is the layer output size and N is
            %                 the mini-batch size.
            % Fully connect.
            weights = layer.Weights;
            bias = layer.Bias;
            X = fullyconnect(X,weights,bias,'DataFormat','SSCB');
            
            % Reshape.
            outputSize = layer.OutputSize;
            Z = reshape(X, outputSize(1), outputSize(2), outputSize(3), []);
        end
    end
end
function weights = initializeGlorot(numOut, numIn)
% Initialize weights using uniform Glorot.
varWeights = sqrt( 6 / (numIn + numOut) );
weights = varWeights * (2 * rand([numOut, numIn], 'single') - 1);
end

[rui an]

how to use it?

[Yonglei GUI]

copy it into a individual *.m file, save it as "projectAndReshapLayer.m", then put the file in the folder where your main() function stays.