George3d6

#Pkg.add("Plots")
#Pkg.add("GR")
using Plots


@everywhere function pixel_color(x, y, width, height, c)
    max_iter = 255
    z = ( ((y/width)*2.7 - 1.3) + ((x/height)*4.5 - 2.5) *im )
    for iter_nr = 1:max_iter
        z = z^2 + c

George3d6

Pkg.add("Knet")
Pkg.add("Plots")
Pkg.add("GR")

function julia(x, y, width, height, c)
  z = ((y/width)*2.7 - 1.3) + ((x/height)*4.5 - 2.5)im
  for i = 1:254
      z = z^2 + c
      if abs(z) >= 4
         return Float32(i)

George3d6

using Knet, Plots;
gr()
global atype = gpu()>=0 ? KnetArray{Float32} : Array{Float32}

George3d6

# Initialize weights and biases, original code: https://github.com/ekinakyurek/GAN-70-Lines-of-Julia/blob/66a60a6ea4532841ee647f08759ae9b1ace0c892/gan.jl#L25
function initmodel(hidden, input, output)
  𝗪 = [];
  x = input
  for h in [hidden... output]
    push!(𝗪, atype(xavier(h,x)), atype(zeros(h, 1))) #FC Layers weights and bias
    x = h
  end
  𝗪
 end

George3d6

leakyrelu(x;α=Float32(0.2)) = max(0,x) + α*min(0,x) # LeakyRelu activation
# A generic MLP forward prop function, original code: https://github.com/ekinakyurek/GAN-70-Lines-of-Julia/blob/66a60a6ea4532841ee647f08759ae9b1ace0c892/gan.jl#L6
function forward_prop(W,X;dropout_p=0.0)
    for i=1:2:length(W)
        X = W[i]*dropout(mat(X),dropout_p) .+ W[i+1] # mat(X) flattens X to an
        i < length(W)-1 && (X = leakyrelu.(X))
    end
    sigm.(X)
end
# Forward prop for the discriminator and generator respectively

George3d6

global const 𝜀=Float32(1e-8) #  a small number prevent from getting NaN  in logs
𝑱d(𝗪d,x,Gz) = -mean(log.(D(𝗪d,x)+𝜀)+log.(1-D(𝗪d,Gz)+𝜀))/2 # Discriminator Loss
𝑱g(𝗪g, 𝗪d, z) = -mean(log.(D(𝗪d,G(𝗪g,z))+𝜀))             # Generator Loss

∇d  = grad(𝑱d) # Discriminator gradient
∇g  = grad(𝑱g) # Generator gradient

George3d6

𝒩(input, batch) = atype(randn(Float32, input, batch)) # SampleNoise
function generate_and_save(𝗪,number,𝞗,gen;fldr="save_image_directory/")
    Gz = G(𝗪[1],𝒩(𝞗[:ginp],number)) #.> 0.5
    Gz = permutedims(reshape(Gz,(𝞗[:size],𝞗[:size],number)),(2,1,3))
    [png(heatmap(Gz[:,:,i]), "$(fldr)$(gen)-$(i).png") for i=1:number]
end

George3d6

#original code: https://github.com/ekinakyurek/GAN-70-Lines-of-Julia/blob/66a60a6ea4532841ee647f08759ae9b1ace0c892/gan.jl#L42
function train_model(𝗪, data, 𝞗, optim)
    gloss=dloss=counter=0.0;
    B =
    for generation=1:𝞗[:epochs]
        for n=1:32:(length(data[1,1,1,:]) - 33)
            x = data[:,:,:,n:n+31]

            Gz = G(𝗪[1],𝒩(𝞗[:ginp],𝞗[:batchsize]))
            update!(𝗪[2], ∇d(𝗪[2],x,Gz), optim[2])

George3d6

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]

George3d6

size = 58
X = get_training_data(6000,size)
main(size, X)