I have an external function which takes y and y_prediction (in matrix format), and computes a metric which depicts how good or bad the prediction actually is.
Unfortunately the metric is no simple y - ypred or confusion matrix, but still very useful and important. How can I use this number computed for the loss or as an argument for optimizer.minimize?
If i understood correctly i think there is two way to do this:
Either the loss you want to compute can be writen as tensorflow ops which gradient is defined (for exemple SVD has no gradient defined in tensorflow library saddly) then the optimisation is direct.
Or you can always write your loss function with numpy operators and use tf.py_func() https://www.tensorflow.org/api_docs/python/tf/py_func and then you have to explicit the gradient by hand as said in here : How to make a custom activation function with only Python in Tensorflow? But you have to know an explicit formula of your gradient ...
