agjn8.jl

function log_model(𝗪, data, 𝞗, generation)
    println("Running logging function for generation $(generation)")
    println("-----------------------------------------------------")
    gloss=dloss=counter=0.0

    for n=1:32:(length(data[1,1,1,:]) - 33)
        x = data[:,:,:,n:n+31]

        counter+=2*𝞗[:batchsize]

        Gz = G(𝗪[1],𝒩(𝞗[:ginp],𝞗[:batchsize]))
        dloss += 2𝞗[:batchsize]*𝑱d(𝗪[2],x,Gz)

        z=𝒩(𝞗[:ginp],2*𝞗[:batchsize])
        gloss += 2𝞗[:batchsize]*𝑱g(𝗪[1],𝗪[2],z)
    end

    println("Generator average loss: $(gloss/counter)")
    println("discriminator average loss: $(dloss/counter)")
    generate_and_save(𝗪,6,𝞗,generation)
    println("Saved images for generation: $(generation)")
    println("------------------------------------------------\n")
end