在Flux(Julia)中运行VGG19模型时出现MethodError

Question

下面提到的代码取自模型动物园。我正在尝试使用flux库在julia中运行vgg19教程。

代码：

#model
using Flux
vgg19() = Chain(            
Conv((3, 3), 3 => 64, relu, pad=(1, 1), stride=(1, 1)),
Conv((3, 3), 64 => 64, relu, pad=(1, 1), stride=(1, 1)),
MaxPool((2,2)),
Conv((3, 3), 64 => 128, relu, pad=(1, 1), stride=(1, 1)),
Conv((3, 3), 128 => 128, relu, pad=(1, 1), stride=(1, 1)),
MaxPool((2,2)),
Conv((3, 3), 128 => 256, relu, pad=(1, 1), stride=(1, 1)),
Conv((3, 3), 256 => 256, relu, pad=(1, 1), stride=(1, 1)),
Conv((3, 3), 256 => 256, relu, pad=(1, 1), stride=(1, 1)),
MaxPool((2,2)),
Conv((3, 3), 256 => 512, relu, pad=(1, 1), stride=(1, 1)),
Conv((3, 3), 512 => 512, relu, pad=(1, 1), stride=(1, 1)),
Conv((3, 3), 512 => 512, relu, pad=(1, 1), stride=(1, 1)),
MaxPool((2,2)),
Conv((3, 3), 512 => 512, relu, pad=(1, 1), stride=(1, 1)),
Conv((3, 3), 512 => 512, relu, pad=(1, 1), stride=(1, 1)),
Conv((3, 3), 512 => 512, relu, pad=(1, 1), stride=(1, 1)),
BatchNorm(512),
MaxPool((2,2)),
flatten,
Dense(512, 4096, relu),
Dropout(0.5),
Dense(4096, 4096, relu),
Dropout(0.5),
Dense(4096, 10),
softmax
)
#data
using MLDatasets: CIFAR10
using Flux: onehotbatch
# Data comes pre-normalized in Julia
trainX, trainY = CIFAR10.traindata(Float64)
testX, testY = CIFAR10.testdata(Float64)
# One hot encode labels
trainY = onehotbatch(trainY, 0:9)
testY = onehotbatch(testY, 0:9)
#training
using Flux: crossentropy, @epochs
using Flux.Data: DataLoader
model = vgg19()
opt = Momentum(.001, .9)
loss(x, y) = crossentropy(model(x), y)
data = DataLoader(trainX, trainY, batchsize=64)
@epochs 100 Flux.train!(loss, params(model), data, opt)

当我在IJulia上执行这个文件时，会抛出以下错误：

MethodError: no method matching ∇maxpool(::Array{Float32,4}, ::Array{Float64,4}, ::Array{Float64,4}, ::PoolDims{2,(2, 2),(2, 2),(0, 0, 0, 0),(1, 1)})
Closest candidates are:
∇maxpool(::AbstractArray{T,N}, !Matched::AbstractArray{T,N}, !Matched::AbstractArray{T,N}, ::PoolDims; kwargs...) where {T, N}

请对此错误提出一些解决方案，如果可能的话，请提供简短的解释或参考。提前感谢！

Answer 1

正如@mcabbott所提到的，这个问题与数据的输入类型有关。这可以通过将type从Float64更改为Float32来解决，用于#data部分下的以下参数。

trainX, trainY = CIFAR10.traindata(Float32)
testX, testY = CIFAR10.testdata(Float32)