Adds state update for training with batch normalization.

conslearn/trainConstrainedNetwork.m

@@ -147,7 +147,8 @@
 
         % Evaluate the model gradients, and loss using dlfeval and the
         % modelLoss function and update the network state.
-        [lossTrain,gradients] = dlfeval(@iModelLoss,net,X,T,metric);
+        [lossTrain,gradients,state] = dlfeval(@iModelLoss,net,X,T,metric);
+        net.State = state;
 
         % Gradient Update
         [net,avgG,avgSqG] = adamupdate(net,gradients,avgG,avgSqG,epoch,learnRate);
@@ -180,8 +181,12 @@
 end
 
 %% Helpers
-function [loss,gradients] = iModelLoss(net,X,T,metric)
-Y = forward(net,X);
+function [loss,gradients,state] = iModelLoss(net,X,T,metric)
+
+% Make a forward pass
+[Y,state] = forward(net,X);
+
+% Compute the loss
 switch metric
     case "mse"
         loss = mse(Y,T);
@@ -190,6 +195,8 @@
     case "crossentropy"
         loss = crossentropy(softmax(Y),T);
 end
+
+% Compute the gradient of the loss with respect to the learnabless
 gradients = dlgradient(loss,net.Learnables);
 end