Generate more helpful exception messages for numpy/pytorch matrix algebra expressions
See article Clarifying exceptions and visualizing tensor operations in deep learning code and TensorSensor implementation slides (PDF).
One of the biggest challenges when writing code to implement deep learning networks, particularly for us newbies, is getting all of the tensor (matrix and vector) dimensions to line up properly. It’s really easy to lose track of tensor dimensionality in complicated expressions involving multiple tensors and tensor operations. Even when just feeding data into predefined Tensorflow network layers, we still need to get the dimensions right. When you ask for improper computations, you’re going to run into some less than helpful exception messages.
To help myself and other programmers debug tensor code, I built this library. TensorSensor clarifies exceptions by augmenting messages and visualizing Python