Hi zhou,
Training a neural network with two outputs for semantic segmentation using a custom training loop involves the following key steps:
- Define a network architecture that has two outputs. Here’s a sample network architecture with two output layers.
layers = [
imageInputLayer([256 256 3],'Name','input')
convolution2dLayer(3,64,'Padding','same','Name','conv1')
reluLayer('Name','relu1')
%numClasses1, numClasses2-Replace them with the number of classes in your datasets
convolution2dLayer(1, numClasses1, 'Name', 'convOut1')
softmaxLayer('Name','softmax1')
pixelClassificationLayer('Name','output1')
convolution2dLayer(1, numClasses2, 'Name', 'convOut2')
softmaxLayer('Name','softmax2')
pixelClassificationLayer('Name','output2')];
- Define a custom loss function that computes the loss for both outputs.
- Use a custom training loop to train the model and compute loss. Refer to the following link for training network using a custom training loop: https://www.mathworks.com/help/deeplearning/ug/train-network-using-custom-training-loop.html
- Periodically evaluate the network on a validation set to monitor performance.
Hope this helps!
