I am not sure if this information might help but when I calculated the confusion matrix for the testing data
I got this results
 val_loss: 63.5753 - val_acc: 63.3333 - val_roc: 67.1875
 avg_correct: 63.3333 - avg_wrong: 36.6667 -

this is the way of calculating the metric
cm = metrics.confusion_matrix(y,yy.round(),labels= [0,1])
where y is the true labels and yy is the predict value
        y_pred=F.softmax(y_pred)
        yy=y_pred.detach().numpy()[:,1].flatten()