Closed. This question needs debugging details. It is not currently accepting answers. Edit the question to include desired behavior, a specific problem or error, and the shortest code necessary to reproduce the problem. This will help others answer the question. Closed last year. Improve this question I have a model with several dense layers that behaves normally in all aspects.
Tag: weighted
Edit Distance w/ operational weights in Python
I am learning about edit distance for the first time and have only been coding for a few months. I’m trying to modify the algorithm such that the different editing operations carry different weights as follows: insertion weighs 20, deletion weighs 20 and replacement weighs 5. I have been able to implement the basic code that calculates minimum edit distance
Evaluating DenseNet model in Keras with weighted classes
I am doing a binary classification in Keras, using DenseNet. Created weighted classes: As a result, I have I fitted the model with class_weight But when I want to evaluate the model, I am not sure how to evaluate the weighted model, because the class_weight is a part of the history. How to update this code, using instead of default
Weighted standard deviation in NumPy
numpy.average() has a weights option, but numpy.std() does not. Does anyone have suggestions for a workaround? Answer How about the following short “manual calculation”?