NN Getting Same Training and Testing Accuracies?

General Usage
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16

Ok so, for the datas I did something weird don’t ask,

im_width = 128
im_height = 128
function get_data(width,height,type)
    folder = readdir("alzheimers/$type")
    labels = copy(folder)
    D = Dict()
    for label in labels
        fold = joinpath("alzheimers/$type",label)
        files = readdir(fold)
        D[label] = zeros(Float32,width,height,1,length(files))
        for (i,file) in enumerate(files)
            im = imresize(load(joinpath(fold,file)),(width,height))
            D[label][:,:,1,i] .= channelview(im)
        end
    end    
    labels,D
end
class,D = get_data(im_width,im_height,"train")
total_im = mapreduce(v -> size(v,4) ,+,values(D))
datas = zeros(Float32,im_width,im_height,1,total_im);
labels = Vector{String}(undef,total_im)
c = 1
for i in eachindex(class)
    Di = D[class[i]]
    for j in axes(Di,4)
        labels[c] = class[i]
        datas[:,:,:,c] .= Di[:,:,:,j] 
        c+=1
    end
end
labels
datas = gpu(datas)
label_for_flux = Flux.onehotbatch(labels,class) |> gpu
datas_for_flux = Flux.DataLoader((datas,label_for_flux),batchsize=32,shuffle=true)

For the model,

model = Flux.Chain(
    Conv((3,3),1=>4,relu,pad=1),
    MaxPool((2,2)),
    Conv((3,3),4=>8,relu,pad=1),
    MaxPool((2,2)),
    Conv((3,3),8=>16,relu,pad=1),
    MaxPool((2,2)),
    Conv((3,3),16=>32,relu,pad=1),
    MaxPool((2,2)),
    Flux.flatten,
    Dense(floor(Int,im_width/16*im_height/16*32)=>20,Flux.relu),
    Flux.Dropout(0.1),
    Dense(20=>4),
    Flux.softmax
) |> gpu

opt = Flux.setup(Adam(0.001),model)
loss(m,x,y) = Flux.crossentropy(m(x),y)

for i in 1:100
    CUDA.synchronize()
    Flux.train!(loss,model,datas_for_flux,opt)
    @info i,loss(model,datas,label_for_flux)
end

And to tests,

class,D = get_data(im_width,im_height,"test")
Flux.testmode!(model)
purc = Float64[]
for i in 1:4
    Di = D[class[i]]
    lab_re = [ class[i] for _ in axes(Di,4)]
    lab_pred = model(gpu(Di)) |> cpu |> x-> Flux.onecold(x,class)
    push!(purc,(lab_re .== lab_pred ) |> sum |> x-> x/length(lab_re)*100)
end
purc

you will need

using Flux,Images,CUDA,cuDNN
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