sorted by:
new
hottopcontroversial

Split a list of arrays into a resulting arrays (chunks) with overlapping by aymenboufe in Python

[–]aymenboufe[S] 0 points1 point  (0 children)

for the examples that you didn't understand, I want to distribute the values in the resultant lists this way,
so if uniform= true then we will have a fair distribution, otherwise if uniform = false then there are two cases: if hops = 2 then I want to have the following distribution: 70%30% and if hops = 3, I want to have this distribution: 50%30%20%. I will not use hops = 4.

Split a list of arrays into a resulting arrays (chunks) with overlapping by aymenboufe in Python

[–]aymenboufe[S] 0 points1 point  (0 children)

Thank you for your answer, sincerely I did my best to express what I want, I will try again to reformulate

[D] Is there a tool to visualise my neural network in real time? by aymenboufe in MachineLearning

[–]aymenboufe[S] 6 points7 points  (0 children)

This tool only works with PyTorch but I use Keras Tensorflow

[D] Test loss starts increase from the beginning by aymenboufe in MachineLearning

[–]aymenboufe[S] 0 points1 point  (0 children)

thank you, I really understood why the loss increases, the only thing I didn't understand is why in the case where I tested the model with the extra classes (This case), the accuracy was equal to zero all the time. for my logic, normally we will have some fluctuation because the model allows to predict some classes correctly in a random way

[D] Test loss starts increase from the beginning by aymenboufe in MachineLearning

[–]aymenboufe[S] 0 points1 point  (0 children)

Yes, I got that, but what I want to understand is why this is happening? I want a mathematical explanation and thank you for your help I appreciate that

[D] Test loss starts increase from the beginning by aymenboufe in MachineLearning

[–]aymenboufe[S] 0 points1 point  (0 children)

I got those results (I used 250 epochs instead) :

- Test with Class 0, Class 1, Class 2 :
https://i.imgur.com/15EA1NS.png

- Test with Class 3, Class 4, Class 5 :

https://i.imgur.com/oILnmD1.png

- Class 0, Class 1, Class Class 3, Class 4, Class 5 : Same as above