I know what you mean. To take partial derivatives with respect to the filter parameters from a correlation (or convolution), it's simpler to go down to the component level. However, it's hard to get back up to the matrix/vector level after doing so (to write the operations in NumPy).
I'm developing a model (not exactly a convnet) that uses a correlation step. Because of the above problem and its resulting pure-python loops, I may have to cythonize or use the NumPy C API for the gradient evaluation. Do you know of any examples I could check out that implement partial derivatives w.r.t. a correlation (or convolution) in "raw numpy"?
I'm developing a model (not exactly a convnet) that uses a correlation step. Because of the above problem and its resulting pure-python loops, I may have to cythonize or use the NumPy C API for the gradient evaluation. Do you know of any examples I could check out that implement partial derivatives w.r.t. a correlation (or convolution) in "raw numpy"?