为什么 Flux.jl 会抛出“警告：缓慢的回退实现”和 DimensionMismatch？答案

【问题标题】：Why is Flux.jl throwing a "Warning: Slow Fallback implementation" and DimensionMismatch?为什么 Flux.jl 会抛出“警告：缓慢的回退实现”和 DimensionMismatch？
【发布时间】：2021-02-14 23:11:23
【问题描述】：

我正在尝试创建一个卷积神经网络，以在 Julia 中使用 Flux 对 MNIST 数据进行分类。我从以下链接将数据下载为 csv：https://www.kaggle.com/oddrationale/mnist-in-csv。我的代码如下：

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)

当我检查 test_X、test_y、train_X 和 train_y 的值时，它们的格式都是正确的，但是当我尝试运行代码时出现此错误：

┌ 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)")

堆栈跟踪指的是第 55 行，即带有渐变的行。任何帮助将不胜感激。

【问题讨论】：

标签： julia conv-neural-network ijulia-notebook

【解决方案1】：

您似乎需要检查您的类型，因为其中一个是 float32 而另一个是 float64。通常 Flux 默认使用 Float32，因为这对于深度学习任务来说已经足够精确了。您也可以使用 Flux.f64/f32。

【讨论】：

要为我解决这个问题，我只需要float32.(x) 其中x 是我的形象。