when shape contains negative integer, input_variable should throw an error about cntk HOT 1 OPEN

The issue you're encountering might be related to how CNTK handles negative integers in input shapes. In CNTK, the negative dimension is often used as a placeholder for sequences or variable-length dimensions. While other deep learning libraries might raise an error when encountering negative integers, CNTK might interpret it differently.

In your case, the shape (1, -1, 1) is likely interpreted as a sequence of vectors with one element, where the second dimension can vary in size (variable-length sequence), and each element has one value. This is a valid usage in CNTK for dealing with sequences.

from cntk.