CNTK evaluation for image classification
Reshaping your input would not affect the output of the model.  If it is only predicting one class for every image, it is an issue with model training.  I would suggest you try predicting on your training data to see if it only predicts one class on the training data.  If that is the case, it is definitely a model training issue.
I built an image classifier using CNTK. The images are grayscale. Therefore, I entered the number of channels as 1. So, the model requires (1x64x64) data (64 being the image height and width).
The problem is, when I try to predict the class of a new image, it is seen as (64x64) only. So, the code errors out due to data mismatch.
Therefore, I reshaped the image using:
image_data = image_data.reshape((1, image_data.shape[0], image_data.shape[1]))
This generated (1x64x64) - which worked. Though the predictions are coming the same class for every image I select. I wonder if it is because of this reshaping or not. Can someone chime in? Thanks!