training from scratch, random seed about squeezenet HOT 1 CLOSED

This post might help:
https://www.google.com/amp/s/www.researchgate.net/post/Whenever_i_run_my_neural_network_I_get_different_result/amp

If you don't set a seed for the random number generator, it will have a new seed every time you train. If you want numerically repeatable results (useful for debugging), then it is necessary to select a seed.

DNNs tend to be pretty sensitive to their parameter initialization, and some seeds just end up not converging for some models and datasets. It's a common issue in machine learning in general.

In practice, people often train a few models with different seeds and find one that converges well.

from squeezenet.