how loss and metric are calculated in cntk

I am new to cntk and python. I have created a python program based on TrainResNet_CIFAR10.py to train 4736 of (64x64x3) images and test 2180 images with 4 classes. After train 160 epochs, I got loss = 0.663 and metric = 29.37%. Finished evaluation metric = 18.94%. When I evaluate the train model based on CNTKLibraryCSEvalExamples.cs to test 2180 images, almost all 2180 are classified as one class (second class). My questions are:

1. I assume loss is calculated from cross_entropy_with_softmax(z, label_var) and metric is using classification_error(z, label_var). Am I correct and how are they actually determined?
2. What does mean of metric = 29.37% and evaluation metric = 18.94%? Are they from train and test images, respectively?
3. what could cause totally wrong evaluate results?

Any help will be greatly appreciated.