Training an Autoencoder with Flux


I’m trying to train a simple Autoencoder with the objective of using it for data imputation. The idea is that I have matrix of count with zeros values that are caused by a lack of sensitivity of the detector and want to replace this zeros based on information learned from other samples. This is tightly related to collaborative filtering. So far I use the Autoencoder from the Flux model zoo as a starting point. I wanted to see the effect of the Median of means estimator for the mean error so I modified the loss function of the Autoencoder to use the MOM estimator of the error on non-missing (non-zeros) values. However, when training I get this error that I don’t manage to solve:

ERROR: MethodError: no method matching (::var"#16#20"{Args,Chain{Tuple{Dense{typeof(leakyrelu),Array{Float32,2},Array{Float32,1}},Dense{typeof(leakyrelu),Array{Float32,2},Array{Float32,1}},var"#15#19"}}})(::Array{Float64,2}, ::BitArray{2})

Here is my code so far:

# use Variational Autoencoder to impute missing value
using Flux
using Flux: @epochs, onehotbatch, mse, throttle
using Base.Iterators: partition
using Parameters: @with_kw
using Random
using Statistics
using CUDA
if has_cuda() # false on my laptop so I don't use GPU
    @info "CUDA is on"
@with_kw mutable struct Args
    lr::Float64 = 1e-3		# Learning rate
    epochs::Int = 10		# Number of epochs
    N::Int = 100	        # Size of the encoding
    batchsize::Int = 500	# Batch size for training
    sample_len::Int = 34 	# Number sample in the data
    throttle::Int = 5		# Throttle timeout
    k::Int = 100            # number of block for the MOM estimation of the average error
function get_processed_data(X, args)
    # localisation of non-zeros values
    Ω = X .!= 0

    # Partition into batches of size batchsize
    perm = randperm(size(X, 1))
    train_data = [float(permutedims(X[x, :])) for x in partition(perm, args.batchsize)]
    train_nz = [permutedims(Ω[x, :]) for x in partition(perm, args.batchsize)]
    #train_data = gpu.(train_data)
    return train_data, train_nz
function MOMloss(x, m, k)
    # train_data = x[1]
    # train_nz = x[2]
    if k == 1
        return mse(m(x[1])[x[2]], x[1][x[2]])
        err = (m(x[1])[x[2]] - x[1][x[2]]) .^ 2
        n = length(err)
        return median([sum(err[idx]) / length(idx) for idx in partition(randperm(n), k)])
function train(X; kws...)
    args = Args(; kws...)

    train_data, train_nz = get_processed_data(X, args)

    @info("Constructing model......")
    encoder = Dense(args.sample_len, args.N, leakyrelu) #|> gpu
    decoder = Dense(args.N, args.sample_len, leakyrelu) #|> gpu 
    non_neg = x -> max.(x, 0.0)

    # Defining main model as a Chain of encoder and decoder models
    m = Chain(encoder, decoder, non_neg)

    @info("Training model.....")
    loss = x -> MOMloss(x, m, args.k)
    ## Training
    evalcb = throttle(() -> @show(loss((train_data[1], train_nz[1]))), args.throttle)
    opt = ADAM(
    @epochs args.epochs Flux.train!(loss, params(m), zip(train_data, train_nz), opt; cb = evalcb)
    return m, args
X = round.(Int, 100 .+ 100 .* randn(17334, 34))
X[X .< 10] .= 0
m, args = train(X)

I have check that the loss function actually works:

loss((train_data[1], train_nz[1]))

for i in zip(train_data, train_nz)

I will be really thankful for any help in solving this issue !

EDIT: I somehow manage to find the mistake Flux.train! actually “open” the zip object before passing it to the loss function as when I change loss to get a x and y argument as input I don’t get the error. However I get the ERROR: Mutating arrays is not supported error now that I need to solve :sweat_smile:

EDIT2: By working on the ERROR: Mutating arrays is not supported issue I’m almost there rewriting median and using deepcopy and Tullio.jl. However I get this error ERROR: MethodError: no method matching zero(::Type{Array{Float64,1}}) which I really have hard time to figure out where it come from… Here is the modification I made on the loss function:

function median_(x)
    n = length(x)
    if n % 2 == 1
        return 1.0 * x[div(n, 2) + 1]
        return 0.5 * (x[div(n, 2)] + x[div(n, 2) + 1])
function MOMloss(x, y, m, k)
    tmp = deepcopy(x)
    tmp2 = m(tmp)[y]
    tmp = tmp[y]
    if k == 1
        return mse(tmp2, tmp)
        err = (tmp2 - tmp) .^ 2
        n = length(err)
        tmp3 = collect(partition(randperm(n), k))
        @tullio (median_) tmp4 := .+(getindex(err, tmp3[k])) / length(tmp3[k]) (k in 1:length(tmp3)) grad = Dual
        return tmp4