Input Distribution Coverage (IDC) is a framework for measuring the test adequacy of neural networks. IDC rejects invalid inputs and converts the valid inputs into feature vectors, which represent the feature abstractions of the test inputs in a low-dimensional space. IDC applies combinatorial interaction testing measures over the space of the feature vectors to measure test coverage. Experimental studies demonstrate that the test adequacy measures calculated by IDC capture the feature interactions present in the test suites.