I am using a u-net with input images of size 240x240x35 and with four different output images of size 240x240, so in all the outputlayer is 240x240x4. However, the different output images show different min, max values. The first output image is in range of 0-4096 and the other three are in range of 0-256. After training the network, only the first output images resulted in small deviations between true and predicted, however, the others showed much larger variations. I think this is due to the different ranges but I cannot work around it. I could devide the first output image by 10 but I think this is not a nice solution.
Now my question is if I can make a normalization (factor 10) for the first output layer similar to this post, when using trainNetwork():
net.performParam.normalization = 'standard';
My Network is defined as:
lgraph = unetLayers([240 240 35] , 3,'encoderDepth',3);
lgraph = lgraph.removeLayers('Softmax-Layer');
lgraph = lgraph.removeLayers('Segmentation-Layer');
lgraph = lgraph.removeLayers('Final-ConvolutionLayer');
lgraph = lgraph.addLayers(convolution2dLayer(1,4,'NumChannels',64,'name','Final-ConvolutionLayer'));
lgraph = lgraph.addLayers(regressionLayer('name','regressionLayer'));
lgraph = lgraph.connectLayers('Decoder-Stage-3-ReLU-2','Final-ConvolutionLayer');
lgraph = lgraph.connectLayers('Final-ConvolutionLayer','regressionLayer');
Thanks for your help!
Ingo
