Julia -如何为FluxML mlp定义图像数组？

using Flux
using Flux: onehotbatch
using Flux.Data: DataLoader
using Images

train_data = []  # 训练数据集
test_data = []   # 测试数据集

# 读取训练数据集
for i = 1:num_train_samples
    image_path = "path/to/train/image_$(i).jpg"
    image = load(image_path)  # 使用Images.jl加载图像
    push!(train_data, image)
end

# 读取测试数据集
for i = 1:num_test_samples
    image_path = "path/to/test/image_$(i).jpg"
    image = load(image_path)  # 使用Images.jl加载图像
    push!(test_data, image)
end

preprocess(image) = (image .- 127.5) / 127.5  # 归一化到[-1, 1]之间

train_data = preprocess.(train_data)
test_data = preprocess.(test_data)

input_size = 784  # 输入层大小
hidden_size = 256  # 隐藏层大小
output_size = 10  # 输出层大小

model = Chain(
    Dense(input_size, hidden_size, relu),
    Dense(hidden_size, output_size),
    softmax
)

loss(x, y) = Flux.crossentropy(model(x), y)
optimizer = Flux.ADAM()

function train(model, train_data, epochs)
    for epoch = 1:epochs
        for (x, y) in train_data
            gs = Flux.gradient(params(model)) do
                l = loss(x, y)
            end
            Flux.update!(optimizer, params(model), gs)
        end
    end
end

train_images = [Float32.(channelview(image)) for image in train_data]
test_images = [Float32.(channelview(image)) for image in test_data]