Train a GAN example difficulty

Question

mark palmer 2020-5-22

1
链接

此问题的直接链接

https://ww2.mathworks.cn/matlabcentral/answers/530433-train-a-gan-example-difficulty

评论： zahoor m 2023-12-12

采纳的回答： Xiangxue Wang

在 MATLAB Online 中打开

I am working through the example for using a GAN given here:

https://www.mathworks.com/help/deeplearning/ug/train-generative-adversarial-network.html

And I get an error at the point where it says projectAndReshapeLayer.

Undefined function 'projectAndReshapeLayer' for input arguments of type
'double'.

When I click on the word projectAndReshapeLayer, I get this:

You clicked a link that corresponds to this MATLAB command:

edit(fullfile(matlabroot,'examples','nnet','main','projectAndReshapeLayer.m'))

Run the command by entering it in the MATLAB Command Window. Web browsers do not support MATLAB commands.

So when I paste this link into the Command Window, it simply creates a blank projectAndReshapeLayer.m file and the error persists.

What am I doing wrong?

To project and reshape the noise input, use the custom layer projectAndReshapeLayer, attached to this example as a supporting file. The projectAndReshapeLayer layer upscales the input using a fully connected operation and reshapes the output to the specified size.

filterSize = 5;
numFilters = 64;
numLatentInputs = 100;
projectionSize = [4 4 512];
layersGenerator = [
    imageInputLayer([1 1 numLatentInputs],'Normalization','none','Name','in')
    projectAndReshapeLayer(projectionSize,numLatentInputs,'proj');
    transposedConv2dLayer(filterSize,4*numFilters,'Name','tconv1')
    batchNormalizationLayer('Name','bnorm1')
    reluLayer('Name','relu1')
    transposedConv2dLayer(filterSize,2*numFilters,'Stride',2,'Cropping','same','Name','tconv2')
    batchNormalizationLayer('Name','bnorm2')
    reluLayer('Name','relu2')
    transposedConv2dLayer(filterSize,numFilters,'Stride',2,'Cropping','same','Name','tconv3')
    batchNormalizationLayer('Name','bnorm3')
    reluLayer('Name','relu3')
    transposedConv2dLayer(filterSize,3,'Stride',2,'Cropping','same','Name','tconv4')
    tanhLayer('Name','tanh')];
lgraphGenerator = layerGraph(layersGenerator);

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Yonglei GUI 2021-2-21

在 MATLAB Online 中打开

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

Rahul Gowtham Poola 2023-2-5

Error using dlnetwork/validateForwardInputs

Layer 'in': Invalid input data. Invalid number of spatial dimensions. Layer expects 2 but received 0.

zahoor m 2023-12-12

Layer 'input': Invalid input data. Invalid number of spatial dimensions. Layer expects 2 but received

0.

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Answer 1

Xiangxue Wang 2020-6-4

4
链接

此回答的直接链接

https://ww2.mathworks.cn/matlabcentral/answers/530433-train-a-gan-example-difficulty#answer_446264

在 MATLAB Online 中打开

Trying to add the projectAndReshapeLayer path to your matlab searching path. By default, the deep learning example are not in 2020 path.

Hope this will work for you

>> fullfile(matlabroot,'examples','nnet','main','projectAndReshapeLayer.m')
ans =
    'C:\Program Files\MATLAB\R2020a\examples\nnet\main\projectAndReshapeLayer.m'
    
% so adding path to by:
addpath('C:\Program Files\MATLAB\R2020a\examples\nnet\main')

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Answer 2

soliya 2020-6-11

2
链接

此回答的直接链接

https://ww2.mathworks.cn/matlabcentral/answers/530433-train-a-gan-example-difficulty#answer_449597

https://viewer.mathworks.com/?viewer=plain_code&url=https%3A%2F%2Fww2.mathworks.cn%2Fmatlabcentral%2Fmlc-downloads%2Fdownloads%2F04c02184-6b40-49d0-be14-8fae65da6ea0%2Fed1525d6-681b-446a-8b35-4501180bc312%2Ffiles%2Ffiles%2FprojectAndReshapeLayer.m&embed=web

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Yonglei GUI 2021-2-21

Hello, my matlab has no the file minibatchqueue.m, so may you give me a URL like this? or you can send email narutomst@163.com. Thank you very much!

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Answer 3

Ieuan Evans 2020-6-25

0
链接

此回答的直接链接

https://ww2.mathworks.cn/matlabcentral/answers/530433-train-a-gan-example-difficulty#answer_457024

编辑：Ieuan Evans 2020-6-25

This example was updated in R2020a to use this custom layer. If you use the command openExample('nnet/TrainGenerativeAdversarialNetworkGANExample') in MATLAB, then it will open the correct version of this example for your version of MATLAB.

Hope this helps.