在 Julia 中使用 MXNet 的神经网络示例

Question

我正在尝试构建一个可以回答异或问题的神经网络。我的代码如下：

using MXNet
using Distributions
using PyPlot

xor_data = zeros(4,2)
xor_data[1:0] = 1
xor_data[1:1] = 1
xor_data[2:0] = 1
xor_data[2:1] = 0
xor_data[3:0] = 0
xor_data[3:1] = 1
xor_data[4:0] = 0
xor_data[4:1] = 0

xor_labels = zeros(4)
xor_labels[1] = 0
xor_labels[2] = 1
xor_labels[3] = 1
xor_labels[4] = 0

batchsize = 4
trainprovider = mx.ArrayDataProvider(:data => xor_data, batch_size=batchsize, shuffle=true, :label => xor_labels)
evalprovider = mx.ArrayDataProvider(:data => xor_data, batch_size=batchsize, shuffle=true, :label => xor_labels)

data = mx.Variable(:data)
label = mx.Variable(:label)
net = @mx.chain     mx.Variable(:data) =>
                    mx.FullyConnected(num_hidden=2) =>
                    mx.Activation(act_type=:relu) =>
                    mx.FullyConnected(num_hidden=2) =>
                    mx.Activation(act_type=:relu) =>
                    mx.FullyConnected(num_hidden=1) =>
                    mx.Activation(act_type=:relu) =>

model = mx.FeedForward(net, context=mx.cpu())
optimizer = mx.SGD(lr=0.01, momentum=0.9, weight_decay=0.00001)
initializer = mx.NormalInitializer(0.0,0.1)
eval_metric = mx.MSE()

mx.fit(model, optimizer, initializer, eval_metric, trainprovider, eval_data = evalprovider, n_epoch = 100)
mx.fit(model, optimizer, eval_metric, trainprovider, eval_data = evalprovider, n_epoch = 100)

但我收到以下错误：

LoadError: AssertionError: Number of samples in label is mismatch with data in expression starting on line 22 in #ArrayDataProvider#6428(::Int64, ::Bool, ::Int64, ::Int64, ::Type{T}, ::Pair{Symbol,Array{Float64,2}}, ::Pair{Symbol,Array{Float64,1}}) at io.jl:324 in (::Core.#kw#Type)(::Array{Any,1}, ::Type{MXNet.mx.ArrayDataProvider}, ::Pair{Symbol,Array{Float64,2}}, ::Pair{Symbol,Array{Float64,1}}) at :0 in include_string(::String, ::String) at loading.jl:441 in include_string(::String, ::String) at sys.dylib:? in include_string(::Module, ::String, ::String) at eval.jl:32 in (::Atom.##59#62{String,String})() at eval.jl:81 in withpath(::Atom.##59#62{String,String}, ::String) at utils.jl:30 in withpath(::Function, ::String) at eval.jl:46 in macro expansion at eval.jl:79 [inlined] in (::Atom.##58#61{Dict{String,Any}})() at task.jl:60

我想将值（0 或 1）提供给网络并获得单个值。是我的错误吗？

Answer 1

xor_data 的维度是错误的，它应该有 4 列，而不是 4 行（顺便说一句，你没有按照你认为的方式初始化它，因为 Julia 中的数组是索引的来自 1，而不是来自 0）。

看：

julia> xor_data = [ [1. 1]; [0 1]; [1 0]; [0 0] ]
4×2 Array{Float64,2}:
 1.0  1.0
 0.0  1.0
 1.0  0.0
 0.0  0.0

julia> xor_labels
4-element Array{Float64,1}:
 0.0
 1.0
 1.0
 0.0

julia> mx.ArrayDataProvider(:data => xor_data, :labels => xor_labels)
ERROR: AssertionError: Number of samples in  labels is mismatch with data
 in #ArrayDataProvider#6428(::Int64, ::Bool, ::Int64, ::Int64, ::Type{T}, ::Pair{Symbol,Array{Float64,2}}, ::Pair{Symbol,Array{Float64,1}}) at /Users/alexey/.julia/v0.5/MXNet/src/io.jl:324
 in MXNet.mx.ArrayDataProvider(::Pair{Symbol,Array{Float64,2}}, ::Pair{Symbol,Array{Float64,1}}) at /Users/alexey/.julia/v0.5/MXNet/src/io.jl:280

julia> xor_data = [ [1. 0 1 0]; [1 1 0 0] ]
2×4 Array{Float64,2}:
 1.0  0.0  1.0  0.0
 1.0  1.0  0.0  0.0

julia> mx.ArrayDataProvider(:data => xor_data, :labels => xor_labels)
MXNet.mx.ArrayDataProvider(Array{Float32,N}[
Float32[1.0 0.0 1.0 0.0; 1.0 1.0 0.0 0.0]],Symbol[:data],Array{Float32,N}[
Float32[0.0 1.0 1.0 0.0]],Symbol[:labels],4,4,false,0.0f0,0.0f0,MXNet.mx.NDArray[mx.NDArray{Float32}(2,4)],MXNet.mx.NDArray[mx.NDArray{Float32}(4,)])

在 Julia 中使用 MXNet 的神经网络示例

Neural networks example using MXNet in Julia

neural-network

julia

mxnet