GAN intermediate layer for image multiplication
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So I have created a GAN that uses a regular noise input for image synthesis. However, I want to multiply or get the product of the generated image with an outside image to speed up training/ training convergence. Here is the basic idea:
%This is the layer setup I use for my GAN
numFilters = 128;
numLatentInputs = 256;
inputSize = [128 128 3];
projectionSize = [4 4 3];
layersGenerator = [
featureInputLayer(numLatentInputs)
projectAndReshapeLayer(projectionSize)
resize2dLayer('OutputSize',[4 4],'Method','bilinear')
batchNormalizationLayer
leakyReluLayer
resize2dLayer('OutputSize',[16 16],'Method','bilinear')
batchNormalizationLayer
leakyReluLayer
resize2dLayer('OutputSize',[32 32],'Method','bilinear')
batchNormalizationLayer
leakyReluLayer
resize2dLayer('OutputSize',[64 64],'Method','bilinear')
batchNormalizationLayer
leakyReluLayer
resize2dLayer('OutputSize',[128 128],'Method','bilinear')
batchNormalizationLayer
tanhLayer];
layersDiscriminator = [
imageInputLayer(inputSize,Normalization="none")
dropoutLayer(dropoutProb)
resize2dLayer('OutputSize',[128 128],'Method','bilinear')
convolution2dLayer(filterSize,numFilters,Stride=2,Padding="same")
leakyReluLayer(scale)
resize2dLayer('OutputSize',[64 64],'Method','bilinear')
convolution2dLayer(filterSize,2*numFilters,Stride=2,Padding="same")
batchNormalizationLayer
leakyReluLayer(scale)
resize2dLayer('OutputSize',[32 32],'Method','bilinear')
convolution2dLayer(filterSize,4*numFilters,Stride=2,Padding="same")
batchNormalizationLayer
leakyReluLayer(scale)
resize2dLayer('OutputSize',[16 16],'Method','bilinear')
convolution2dLayer(filterSize,8*numFilters,Stride=2,Padding="same")
batchNormalizationLayer
leakyReluLayer(scale)
resize2dLayer('OutputSize',[8 8],'Method','bilinear')
convolution2dLayer(8,1)
sigmoidLayer];
numEpochs = 2000;
miniBatchSize = 16;
learnRate = 0.0003;
gradientDecayFactor = 0.5;
squaredGradientDecayFactor = 0.999;
flipProb = 0.75;
validationFrequency = 5;
%The custom layer that I have already made:
classdef imageProduct < nnet.layer.Layer
properties (Constant)
ImageinputT = imread("image.jpg");
end
methods
function layer = imageProduct()
layer.Type = "Image Product";
layer.Description = "Combine Images";
layer.OutputNames = "out";
end
function [Z] = predict(~,X)
%gets the image size so the input image can be resized. Basically the target scale
[WidthX, HeightX] = size(X);
%resizes the imported image
GinputIm_scaled = imresize(crossAttention.ImageinputT, [WidthX HeightX]);
%converts images to double
GinputIm_scaled_single = single(GinputIm_scaled);
Xsingle = single(X);
%Gets the tensor product of the two images
Xnew = tensorprod(GinputIm_scaled_single, Xsingle);
Z = Xnew; %output image
end
end
end
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2023-2-23
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