Why is there a "Warning: Slow Fallback implementation" and a DimensionMismatch error?

General Usage
, ,
1

I am trying to create a convolutional neural network to classify MNIST data with Flux in Julia. I downloaded the data as a csv from this link: MNIST in CSV | Kaggle.
My code is below:

using CSV
using DataFrames
using Images
using Base.Iterators: partition
using Flux

sqrt(x) = convert(Int64, floor(x^0.5))
matrixize(x) = cat([x[i:i+sqrt(length(x))-1] for i in 1:sqrt(length(x)):length(x)]..., dims=2)'
img(x) = Gray.(x)
process(row) = img(matrixize(row[2:length(row)])), convert(Int, 255*row[1]) |> gpu

train_data = DataFrame(CSV.File("MNIST_data/mnist_train.csv"))
train_X = []
train_y = Int64[]
for row in eachrow(train_data)
    row = convert(Array, row)
    row = [i/255 for i in row]
    X, y = process(row)
    push!(train_X, X)
    push!(train_y, y)
end
train_y = Flux.onehotbatch(train_y, 0:9)
train = [(cat(float.(train_X[i])..., dims=4), train_y[:, i]) for i in partition(1:size(train_data)[1], 1000)] |> gpu
test_data = DataFrame(CSV.File("MNIST_data/mnist_train.csv"))
test_X = []
test_y = Int64[]
for row in eachrow(test_data)
    row = convert(Array, row)
    row = [i/255 for i in row]
    X, y = process(row)
    push!(test_X, X)
    push!(test_y, y)
end
test_y = Flux.onehotbatch(test_y, 0:9)
println("Pre-processing Complete")

m = Chain(
    Conv((5, 5), 1=>16, relu),
    MaxPool((2, 2)),
    Conv((5, 5), 16=>8, relu),
    MaxPool((2, 2)),
    Flux.flatten,
    Dense(200, 100),
    Dense(100, 10),
    Flux.softmax
) |> gpu
loss(x, y) = Flux.Losses.crossentropy(m(x), y) |> gpu
opt = Momentum(0.01) |> gpu
println("Model Creation Complete")

println()
epochs = 10
for i in 1:epochs
    for j in train
        gs = gradient(params(m)) do
            l = loss(j...)
        end
        update!(opt, params(m), gs)
    end
    @show accuracy(test_X, test_y)
end

println()
@show accuracy(test_X, test_y)

When I check values of test_X, test_y, train_X, and train_y, they are all in the appropriate format, but I get this error when I try to run the code:

┌ Warning: Slow fallback implementation invoked for conv!  You probably don't want this; check your datatypes.
│   yT = Float64
│   T1 = Gray{Float64}
│   T2 = Float32
└ @ NNlib /Users/satvikd/.julia/packages/NNlib/PI8Xh/src/conv.jl:206
┌ Warning: Slow fallback implementation invoked for conv!  You probably don't want this; check your datatypes.
│   yT = Float64
│   T1 = Float64
│   T2 = Float32
└ @ NNlib /Users/satvikd/.julia/packages/NNlib/PI8Xh/src/conv.jl:206
DimensionMismatch("A has dimensions (100,200) but B has dimensions (128,1000)")

The stack trace refers to line 55, the one with the gradient.
Any help would be appreciated.

2

Your train_X and test_X have element type Any. Choose something concrete.