statistics.org

Experiment: ImageNet Statistics

Compute Statistics of each Conceptual View of the ImageNet Models

Imports

import pandas as pd
import os

Get Models

Separation

Init

stats = pd.DataFrame(models)

Neurons

stats["neurons"] = [to_class_scale(m).shape[1] for m in models]
stats.set_index(0,inplace=True)

Separation

stats["sep"] = [classes_seperation(to_class_scale(m)) for m in models]

Mean and Std

stats["w"] = [(to_class_scale(m).values.mean(),to_class_scale(m).values.std()) 
              for m in models]
stats["b"] = [(to_bias_scale(m).values.mean(),to_class_scale(m).values.std()) 
              for m in models]
stats["o"] = [(to_object_scale(m).values.mean(),to_object_scale(m).values.std()) 
              for m in models]

Save

stats.to_csv("scales/stats_imagenet.csv")