How to do Inception-v3 transfer learning in cntk?

I have been studying transfer learning with cntk tutorial.

After I did this tutorial, I tried to apply another model, and I download InceptionV3_ImageNet_CNTK.model in this page.

After downloading, when I did

node_outputs = C.logging.get_node_outputs(C.load_model('InceptionV3_ImageNet_CNTK.model'))
for l in node_outputs: print("  {0} {1}".format(l.name, l.shape))

to strip off the final features layer and attach a new Dense layer for classification, I found that InceptionV3_ImageNet_CNTK.model doesn't have node name.

Loading InceptionV3_ImageNet_CNTK.model and printing all layers:
(1000,)
(1,)
aggregateLoss ()
(1,)
aggregateEvalMetric ()
(1000,)
(2048, 1, 1)
(2048, 1, 1)
(2048, 8, 8)
(320, 8, 8)
(320, 8, 8)    
(320, 8, 8)
...

Therefore, I can't do

feature_node = C.logging.find_by_name(base_model, node_name = model_details['feature_node_name'])
last_node = C.logging.find_by_name(base_model, node_name =  model_details['last_hidden_node_name'])

and freeze and attach a new Dense layer.

How to solve this problem? I've searched all over the internet (Github, StackOverflow, Google...) but I can't seam to find something useful for a novice.

Thanks so much!