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naivegaexperiments's Introduction

NaiveGAExperiments

Just a bunch of experiments to test ideas for NaiveGAflux.

Lamarckism

Notebook with some experiments on the usefulness of lamarckism, i.e. the idea that new models to the largest extent possible keep the weights from their parents.

Fitness noise

Notebook with some experiments on the effects of fitness noise and how to mitigate it

naivegaexperiments's People

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drchainsaw avatar

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naivegaexperiments's Issues

About ResNet9 benchmark

Hi DrChainsaw (response to your question), for now I haven't replicate benchmark, I only use my ResNet9 implementation with my own dataset, not CIFAR10.
However, I will happily exchange my future results with you. I am currently stuck with last version Flux (v0.11.1), because when I try to use a convolutionnal layer without biases there is some GPU error.

I notice that your image normalization seems different from original benchmark (as far I understand your code properly). Maybe you could try something like that :

using MLDatasets
using Images
using Augmentor
using Statistics
using Flux: onehotbatch

# Compute train dataset statistics
if !isdefined(Main, :meanC10) || !isdefined(Main, :stdC10)
    r = CIFAR10.traintensor()[:,:,1,:]
    g = CIFAR10.traintensor()[:,:,2,:]
    b = CIFAR10.traintensor()[:,:,3,:]
    const meanC10 = [mean(r), mean(g), mean(b)]
    const stdC10 = [std(r), std(g), std(b)]
end

function normalize(img)
    tmp = Float32.((channelview(img) .- meanC10) ./ stdC10)
    return permutedims(tmp, (3, 2, 1))
end

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