A neural network model that can approximate any non-linear function by using the random search algorithm for the optimization of the loss function.

Related tags

Machine learning neural-network random-search neural-network-from-scratch random-search-algorithm
Overview

random_search

A neural network model that can approximate any non-linear function by using the random search algorithm for the optimization of the loss function.

Important

If you are coming from the article on Medium, keep in mind that the code is still partially incomplete and, most importantly, not fully documented, but you can figure things out.

Experiments

experiments target_function S networks epochs layers_sizes STEP_SIZE raw mean loss mean loss %
5 sin(x) 300 16 25_000 32; 3 1e-4 0.0022145095302255496 0.221%
5 sin(x) 300 16 25_000 8; 10 1e-4 0.5271807633104308 52.718%
5 sin(x) 300 16 25_000 16; 5 1e-5 0.5271807632693248 52.718%
5 sin(x) 300 16 25_000 16; 4 1e-4 0.06721093900301409 6.721%
5 sin(x) 300 16 25_000 16; 3 1e-5 0.52699025347444 52.699%
5 sin(x) 300 128 25_000 32; 3 1e-4 0.00012897642800016362 0.013%
5 sin(x) 300 32 25_000 32; 4 1e-4 0.0009437770169172481 0.094%
5 x^3 - 2x^2 - 6x 300 32 25_000 32; 4 1e-4 1390.5025496471226 139050.255%
5 sin(x) 300 128 25_000 8; 6 1e-7 0.5271807632690662 52.718%
5 cos(x) 300 32 25_000 32; 3 1e-4 0.006531106146449706 0.653%
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