I have multiple dict in a list and I want loop through each dict and flatten the file. When I run this code its giving me the ‘NoneType’ object is not subscriptable error. And the custom fields in the below json will be having many other fields as well Project_details list will be consisting of info like this And My
Tag: normalization
getting “KeyError” while implementing Z-score on a dataset
I have been trying to implement z-score normalization to all of the numeric values present in combined_data with the following code: Here, combined_data is the combination of training and testing datasets as a dataframe and passed through one-hot encoding. I am seeing the following error: The dataset combined_data contains 257673 rows & 198 columns Here is the sample data of
Normalize two arrays with second array the base for normalizing first array
In order to find the quality indicators like Generational Distance, Inverted Generational Distance, Epsilon Indicator, and HyperVolume for a Pareto front I want to normalize the values of approximation front obtained on solving the algorithm based on reference front which I assume encloses the approximation front. I have used the code below for normalization. However, it is for one array
Trouble implementing “concurrent” softmax function from paper (PyTorch)
I am trying to implement the so called ‘concurrent’ softmax function given in the paper “Large-Scale Object Detection in the Wild from Imbalanced Multi-Labels”. Below is the definition of the concurrent softmax: NOTE: I have left the (1-rij) term out for the time being because I don’t think it applies to my problem given that my training dataset has a
How to fix “only integers, slices (`:`), ellipsis (`…`), numpy.newaxis (`None`) and integer or boolean arrays are valid indices” in this example
I have the following type of data. This is what I tried: And I got the error. Answer All you should need is:
Denormalization of output from neural network
I have used the MinMax normalization in order to normalize my dataset, both features and label. My question is, it’s correct to normalize also the label? If yes, how can I denormalize the output of the neural network (the one that I predict with the test set that is normalized)? I can’t upload the dataset, but it is composed by