Update trainerfunc.jl to handle error non poorly

_tutorials/trainerfunc.jl

@@ -26,18 +26,23 @@ function trainer(m, func, ln::Real=5)
     schedule = Interpolator(Step(initial_lr, 0.5, [25, 10]), es)
     optim = func(initial_lr)
     # callbacks
-    logger = TensorBoardBackend("tblogs")
-    # schcb = Scheduler(LearningRate => schedule)
-    logcb = (LogMetrics(logger),)# LogHyperParams(logger))
-    valcb = Metrics(Metric(accfn; phase = TrainingPhase, name = "train_acc"),
-                    Metric(accfn; phase = ValidationPhase, name = "val_acc"))
-    learner = Learner(m, lossfn;
-                    data = (trainloader, valloader),
-                    optimizer = optim,
-                    callbacks = [ToGPU(), logcb..., valcb])
 
-    FluxTraining.fit!(learner, ln)
-    close(logger.logger)
+    logger = TensorBoardBackend("tblog")
+    try
+
+        # schcb = Scheduler(LearningRate => schedule)
+        logcb = (LogMetrics(logger),)# LogHyperParams(logger))
+        valcb = Metrics(Metric(accfn; phase = TrainingPhase, name = "train_acc"),
+                        Metric(accfn; phase = ValidationPhase, name = "val_acc"))
+        learner = Learner(m, lossfn;
+                        data = (trainloader, valloader),
+                        optimizer = optim,
+                        callbacks = [ToGPU(), logcb..., valcb])
+
+        FluxTraining.fit!(learner, ln)
+    finally
+        close(logger.logger)
+    end
 
     ## save model
     m = learner.model |> cpu
@@ -56,4 +61,4 @@ end
 #m = resize_nobn(m)
 #m = desaturate(m)
 #m = rebn(m)
-#m = trainer(m, 1)
+#m = trainer(m, 1)