projectionSize = [4 1 1024];
embeddingDimension = 100;
imageInputLayer([1 1 numLatentInputs],'Normalization','none','Name','in')
projectAndReshapeLayer(projectionSize,numLatentInputs,'proj');
concatenationLayer(3,2,'Name','cat');
transposedConv2dLayer([5 1],8*numFilters,'Name','tconv1')
batchNormalizationLayer('Name','bn1','Epsilon',5e-5)
reluLayer('Name','relu1')
transposedConv2dLayer([10 1],4*numFilters,'Stride',4,'Cropping',[1 0],'Name','tconv2')
batchNormalizationLayer('Name','bn2','Epsilon',5e-5)
reluLayer('Name','relu2')
transposedConv2dLayer([12 1],2*numFilters,'Stride',4,'Cropping',[1 0],'Name','tconv3')
batchNormalizationLayer('Name','bn3','Epsilon',5e-5)
reluLayer('Name','relu3')
transposedConv2dLayer([5 1],numFilters,'Stride',4,'Cropping',[1 0],'Name','tconv4')
batchNormalizationLayer('Name','bn4','Epsilon',5e-5)
reluLayer('Name','relu4')
transposedConv2dLayer([7 1],1,'Stride',2,'Cropping',[1 0],'Name','tconv5')
];
'projectAndReshapeLayer' is used in the following examples:
Generate Synthetic Signals Using Conditional GAN
Train Variational Autoencoder (VAE) to Generate Images
Include Custom Layer in Network
Train Generative Adversarial Network (GAN)
Train Wasserstein GAN with Gradient Penalty (WGAN-GP)
lgraphGenerator = layerGraph(layersGenerator);
imageInputLayer([1 1],'Name','labels','Normalization','none')
embedAndReshapeLayer(projectionSize(1:2),embeddingDimension,numClasses,'emb')];
lgraphGenerator = addLayers(lgraphGenerator,layers);
lgraphGenerator = connectLayers(lgraphGenerator,'emb','cat/in2');
