Julia 朱莉娅:在GPU上运行ModelZoo的示例时,会出现各种各样的CuArray错误
如果事实证明我只是忽略了一些事情,我首先道歉。我对Julia编程非常陌生,一周以来一直在努力让model zoo示例在GPU上运行 受此启发,我改为: 结果是:Julia 朱莉娅:在GPU上运行ModelZoo的示例时,会出现各种各样的CuArray错误,julia,gpu,flux,Julia,Gpu,Flux,如果事实证明我只是忽略了一些事情,我首先道歉。我对Julia编程非常陌生,一周以来一直在努力让model zoo示例在GPU上运行 受此启发,我改为: 结果是: [ Info: CUDA is on [ Info: Constructing Model... [ Info: Training... ERROR: LoadError: CuArray only supports bits types Stacktrace: [1] error(::String) at .\error.jl:33
[ Info: CUDA is on
[ Info: Constructing Model...
[ Info: Training...
ERROR: LoadError: CuArray only supports bits types
Stacktrace:
[1] error(::String) at .\error.jl:33
[2] CUDA.CuArray{CUDA.CuArray{Float32,1},1}(::UndefInitializer, ::Tuple{Int64}) at C:\Users\Fenmore\.julia\packages\CUDA\dZvbp\src\array.jl:115
[3] CUDA.CuArray{CUDA.CuArray{Float32,1},N} where N(::UndefInitializer, ::Tuple{Int64}) at C:\Users\Fenmore\.julia\packages\CUDA\dZvbp\src\array.jl:124
[4] similar(::Type{CUDA.CuArray{CUDA.CuArray{Float32,1},N} where N}, ::Tuple{Int64}) at .\abstractarray.jl:675
[5] similar(::Type{CUDA.CuArray{CUDA.CuArray{Float32,1},N} where N}, ::Tuple{Base.OneTo{Int64}}) at .\abstractarray.jl:674
[6] similar(::Base.Broadcast.Broadcasted{CUDA.CuArrayStyle{1},Tuple{Base.OneTo{Int64}},Zygote.var"#1177#1180"{Chain{Tuple{Dense{typeof(σ),CUDA.CuArray{Float32,2},CUDA.CuArray{Float32,1}},Flux.Recur{Flux.LSTMCell{CUDA.CuArray{Float32,2},CUDA.CuArray{Float32,1}}}}}},Tuple{Base.Broadcast.Extruded{CUDA.CuArray{Flux.OneHotVector,1},Tuple{Bool},Tuple{Int64}}}}, ::Type{CUDA.CuArray{Float32,1}}) at C:\Users\Fenmore\.julia\packages\CUDA\dZvbp\src\broadcast.jl:11
[7] copy at .\broadcast.jl:877 [inlined]
[8] materialize at .\broadcast.jl:837 [inlined]
[9] broadcast_forward at C:\Users\Fenmore\.julia\packages\Zygote\rqvFi\src\lib\broadcast.jl:190 [inlined]
[10] adjoint at C:\Users\Fenmore\.julia\packages\Zygote\rqvFi\src\lib\broadcast.jl:202 [inlined]
[11] _pullback at C:\Users\Fenmore\.julia\packages\ZygoteRules\6nssF\src\adjoint.jl:47 [inlined]
[12] adjoint at C:\Users\Fenmore\.julia\packages\Zygote\rqvFi\src\lib\lib.jl:175 [inlined]
[13] _pullback at C:\Users\Fenmore\.julia\packages\ZygoteRules\6nssF\src\adjoint.jl:47 [inlined]
[14] broadcasted at .\broadcast.jl:1257 [inlined]
[15] model at D:\Users\Fenmore\git\Term-Project\module.jl:55 [inlined]
[16] _pullback(::Zygote.Context, ::typeof(model), ::Flux.OneHotMatrix{CUDA.CuArray{Flux.OneHotVector,1}}, ::Chain{Tuple{Dense{typeof(σ),CUDA.CuArray{Float32,2},CUDA.CuArray{Float32,1}},Flux.Recur{Flux.LSTMCell{CUDA.CuArray{Float32,2},CUDA.CuArray{Float32,1}}}}}, ::Dense{typeof(identity),CUDA.CuArray{Float32,2},CUDA.CuArray{Float32,1}}) at C:\Users\Fenmore\.julia\packages\Zygote\rqvFi\src\compiler\interface2.jl:0
[17] loss at D:\Users\Fenmore\git\Term-Project\module.jl:75 [inlined]
[18] _pullback(::Zygote.Context, ::var"#loss#35", ::Flux.OneHotMatrix{CUDA.CuArray{Flux.OneHotVector,1}}, ::Flux.OneHotVector) at C:\Users\Fenmore\.julia\packages\Zygote\rqvFi\src\compiler\interface2.jl:0
[19] adjoint at C:\Users\Fenmore\.julia\packages\Zygote\rqvFi\src\lib\lib.jl:175 [inlined]
[20] _pullback at C:\Users\Fenmore\.julia\packages\ZygoteRules\6nssF\src\adjoint.jl:47 [inlined]
[21] #14 at C:\Users\Fenmore\.julia\packages\Flux\05b38\src\optimise\train.jl:83 [inlined]
[22] _pullback(::Zygote.Context, ::Flux.Optimise.var"#14#20"{var"#loss#35",Tuple{Flux.OneHotMatrix{CUDA.CuArray{Flux.OneHotVector,1}},Flux.OneHotVector}}) at C:\Users\Fenmore\.julia\packages\Zygote\rqvFi\src\compiler\interface2.jl:0
[23] pullback(::Function, ::Zygote.Params) at C:\Users\Fenmore\.julia\packages\Zygote\rqvFi\src\compiler\interface.jl:172
[24] gradient(::Function, ::Zygote.Params) at C:\Users\Fenmore\.julia\packages\Zygote\rqvFi\src\compiler\interface.jl:53
[25] macro expansion at C:\Users\Fenmore\.julia\packages\Flux\05b38\src\optimise\train.jl:82 [inlined]
[26] macro expansion at C:\Users\Fenmore\.julia\packages\Juno\hEPx8\src\progress.jl:119 [inlined]
[27] train!(::Function, ::Zygote.Params, ::Array{Tuple{Flux.OneHotMatrix{CUDA.CuArray{Flux.OneHotVector,1}},Flux.OneHotVector},1}, ::ADAM; cb::Flux.var"#throttled#42"{Flux.var"#throttled#38#43"{Bool,Bool,var"#34#38"{var"#testloss#36"{var"#loss#35"}},Int64}}) at C:\Users\Fenmore\.julia\packages\Flux\05b38\src\optimise\train.jl:80
[28] train(; kws::Base.Iterators.Pairs{Union{},Union{},Tuple{},NamedTuple{(),Tuple{}}}) at D:\Users\Fenmore\git\Term-Project\module.jl:82
[29] train() at D:\Users\Fenmore\git\Term-Project\module.jl:62
[30] top-level scope at D:\Users\Fenmore\git\Term-Project\module.jl:86
[31] include_string(::Function, ::Module, ::String, ::String) at .\loading.jl:1088
in expression starting at D:\Users\Fenmore\git\Term-Project\module.jl:86
我试过的另一个型号是:
using Flux
using Flux: onehot, chunk, batchseq, throttle, logitcrossentropy
using StatsBase: wsample
using Base.Iterators: partition
using Parameters: @with_kw
# Hyperparameter arguments
@with_kw mutable struct Args
lr::Float64 = 1e-2 # Learning rate
seqlen::Int = 50 # Length of batchseqences
nbatch::Int = 50 # number of batches text is divided into
throttle::Int = 30 # Throttle timeout
end
function getdata(args)
# Download the data if not downloaded as 'input.txt'
isfile("input.txt") ||
download("https://cs.stanford.edu/people/karpathy/char-rnn/shakespeare_input.txt","input.txt")
text = collect(String(read("input.txt")))
# an array of all unique characters
alphabet = [unique(text)..., '_']
text = map(ch -> onehot(ch, alphabet), text)
stop = onehot('_', alphabet)
N = length(alphabet)
# Partitioning the data as sequence of batches, which are then collected as array of batches
Xs = collect(partition(batchseq(chunk(text, args.nbatch), stop), args.seqlen))
Ys = collect(partition(batchseq(chunk(text[2:end], args.nbatch), stop), args.seqlen))
return Xs, Ys, N, alphabet
end
# Function to construct model
function build_model(N)
return Chain(
LSTM(N, 128),
LSTM(128, 128),
Dense(128, N))
end
function train(; kws...)
# Initialize the parameters
args = Args(; kws...)
# Get Data
Xs, Ys, N, alphabet = getdata(args)
# Constructing Model
m = build_model(N)
Xs = gpu.(Xs)
Ys = gpu.(Ys)
m = gpu(m)
function loss(xs, ys)
l = sum(logitcrossentropy.(m.(xs), ys))
return l
end
## Training
opt = ADAM(args.lr)
tx, ty = (Xs[5], Ys[5])
evalcb = () -> @show loss(tx, ty)
Flux.train!(loss, params(m), zip(Xs, Ys), opt, cb = throttle(evalcb, args.throttle))
return m, alphabet
end
# Sampling
function sample(m, alphabet, len; seed="")
m = cpu(m)
Flux.reset!(m)
buf = IOBuffer()
if seed == ""
seed = string(rand(alphabet))
end
write(buf, seed)
c = wsample(alphabet, softmax(m.(map(c -> onehot(c, alphabet), collect(seed)))[end]))
for i = 1:len
write(buf, c)
c = wsample(alphabet, softmax(m(onehot(c, alphabet))))
end
return String(take!(buf))
end
cd(@__DIR__)
m, alphabet = train()
sample(m, alphabet, 1000) |> println
因此:
ERROR: LoadError: Mutating arrays is not supported
Stacktrace:
[1] error(::String) at .\error.jl:33
[2] (::Zygote.var"#455#456")(::Nothing) at C:\Users\Fenmore\.julia\packages\Zygote\rqvFi\src\lib\array.jl:68
[3] (::Zygote.var"#2384#back#457"{Zygote.var"#455#456"})(::Nothing) at C:\Users\Fenmore\.julia\packages\ZygoteRules\6nssF\src\adjoint.jl:49
[4] CuArray at C:\Users\Fenmore\.julia\packages\CUDA\dZvbp\src\array.jl:206 [inlined]
[5] CuArray at C:\Users\Fenmore\.julia\packages\CUDA\dZvbp\src\array.jl:211 [inlined]
[6] (::typeof(∂(CUDA.CuArray{Float32,N} where N)))(::CUDA.CuArray{Float32,2}) at C:\Users\Fenmore\.julia\packages\Zygote\rqvFi\src\compiler\interface2.jl:0
[7] LSTMCell at C:\Users\Fenmore\.julia\packages\Flux\05b38\src\cuda\curnn.jl:45 [inlined]
[8] (::typeof(∂(λ)))(::Tuple{Nothing,CUDA.CuArray{Float32,2}}) at C:\Users\Fenmore\.julia\packages\Zygote\rqvFi\src\compiler\interface2.jl:0
[9] (::Zygote.var"#177#178"{typeof(∂(λ)),Tuple{Tuple{Nothing},Tuple{Nothing}}})(::Tuple{Nothing,CUDA.CuArray{Float32,2}}) at C:\Users\Fenmore\.julia\packages\Zygote\rqvFi\src\lib\lib.jl:178
[10] (::Zygote.var"#1730#back#179"{Zygote.var"#177#178"{typeof(∂(λ)),Tuple{Tuple{Nothing},Tuple{Nothing}}}})(::Tuple{Nothing,CUDA.CuArray{Float32,2}}) at C:\Users\Fenmore\.julia\packages\ZygoteRules\6nssF\src\adjoint.jl:49
[11] Recur at C:\Users\Fenmore\.julia\packages\Flux\05b38\src\layers\recurrent.jl:36 [inlined]
[12] (::typeof(∂(λ)))(::CUDA.CuArray{Float32,2}) at C:\Users\Fenmore\.julia\packages\Zygote\rqvFi\src\compiler\interface2.jl:0
[13] applychain at C:\Users\Fenmore\.julia\packages\Flux\05b38\src\layers\basic.jl:36 [inlined]
[14] (::typeof(∂(applychain)))(::CUDA.CuArray{Float32,2}) at C:\Users\Fenmore\.julia\packages\Zygote\rqvFi\src\compiler\interface2.jl:0
[15] Chain at C:\Users\Fenmore\.julia\packages\Flux\05b38\src\layers\basic.jl:38 [inlined]
[16] (::typeof(∂(λ)))(::CUDA.CuArray{Float32,2}) at C:\Users\Fenmore\.julia\packages\Zygote\rqvFi\src\compiler\interface2.jl:0
[17] #1157 at C:\Users\Fenmore\.julia\packages\Zygote\rqvFi\src\lib\broadcast.jl:142 [inlined]
[18] (::Base.var"#3#4"{Zygote.var"#1157#1164"})(::Tuple{typeof(∂(λ)),CUDA.CuArray{Float32,2}}) at .\generator.jl:36
[19] iterate at .\generator.jl:47 [inlined]
[20] collect(::Base.Generator{Base.Iterators.Zip{Tuple{Array{typeof(∂(λ)),1},Array{CUDA.CuArray{Float32,2},1}}},Base.var"#3#4"{Zygote.var"#1157#1164"}}) at .\array.jl:686
[21] map at .\abstractarray.jl:2248 [inlined]
[22] (::Zygote.var"#1156#1163"{Tuple{Array{Flux.OneHotMatrix{CUDA.CuArray{Flux.OneHotVector,1}},1}},Val{2},Array{typeof(∂(λ)),1}})(::Array{CUDA.CuArray{Float32,2},1}) at C:\Users\Fenmore\.julia\packages\Zygote\rqvFi\src\lib\broadcast.jl:142
[23] #3985#back at C:\Users\Fenmore\.julia\packages\ZygoteRules\6nssF\src\adjoint.jl:49 [inlined]
[24] (::Zygote.var"#177#178"{Zygote.var"#3985#back#1167"{Zygote.var"#1156#1163"{Tuple{Array{Flux.OneHotMatrix{CUDA.CuArray{Flux.OneHotVector,1}},1}},Val{2},Array{typeof(∂(λ)),1}}},Tuple{Tuple{Nothing,Nothing,Nothing},Tuple{}}})(::Array{CUDA.CuArray{Float32,2},1}) at C:\Users\Fenmore\.julia\packages\Zygote\rqvFi\src\lib\lib.jl:178
[25] #1730#back at C:\Users\Fenmore\.julia\packages\ZygoteRules\6nssF\src\adjoint.jl:49 [inlined]
[26] broadcasted at .\broadcast.jl:1257 [inlined]
[27] (::typeof(∂(broadcasted)))(::Array{CUDA.CuArray{Float32,2},1}) at C:\Users\Fenmore\.julia\packages\Zygote\rqvFi\src\compiler\interface2.jl:0
[28] loss at C:\Users\Fenmore\github\model-zoo\text\char-rnn\char-rnn.jl:60 [inlined]
[29] (::typeof(∂(λ)))(::Float32) at C:\Users\Fenmore\.julia\packages\Zygote\rqvFi\src\compiler\interface2.jl:0
[30] #177 at C:\Users\Fenmore\.julia\packages\Zygote\rqvFi\src\lib\lib.jl:178 [inlined]
[31] #1730#back at C:\Users\Fenmore\.julia\packages\ZygoteRules\6nssF\src\adjoint.jl:49 [inlined]
[32] #14 at C:\Users\Fenmore\.julia\packages\Flux\05b38\src\optimise\train.jl:83 [inlined]
[33] (::typeof(∂(λ)))(::Float32) at C:\Users\Fenmore\.julia\packages\Zygote\rqvFi\src\compiler\interface2.jl:0
[34] (::Zygote.var"#54#55"{Zygote.Params,Zygote.Context,typeof(∂(λ))})(::Float32) at C:\Users\Fenmore\.julia\packages\Zygote\rqvFi\src\compiler\interface.jl:177
[35] gradient(::Function, ::Zygote.Params) at C:\Users\Fenmore\.julia\packages\Zygote\rqvFi\src\compiler\interface.jl:54
[36] macro expansion at C:\Users\Fenmore\.julia\packages\Flux\05b38\src\optimise\train.jl:82 [inlined]
[37] macro expansion at C:\Users\Fenmore\.julia\packages\Juno\hEPx8\src\progress.jl:119 [inlined]
[38] train!(::Function, ::Zygote.Params, ::Base.Iterators.Zip{Tuple{Array{Array{Flux.OneHotMatrix{CUDA.CuArray{Flux.OneHotVector,1}},1},1},Array{Array{Flux.OneHotMatrix{CUDA.CuArray{Flux.OneHotVector,1}},1},1}}}, ::ADAM; cb::Flux.var"#throttled#42"{Flux.var"#throttled#38#43"{Bool,Bool,var"#25#27"{var"#loss#26",Array{Flux.OneHotMatrix{CUDA.CuArray{Flux.OneHotVector,1}},1},Array{Flux.OneHotMatrix{CUDA.CuArray{Flux.OneHotVector,1}},1}},Int64}}) at C:\Users\Fenmore\.julia\packages\Flux\05b38\src\optimise\train.jl:80
[39] train(; kws::Base.Iterators.Pairs{Union{},Union{},Tuple{},NamedTuple{(),Tuple{}}}) at C:\Users\Fenmore\github\model-zoo\text\char-rnn\char-rnn.jl:69
[40] train() at C:\Users\Fenmore\github\model-zoo\text\char-rnn\char-rnn.jl:47
[41] top-level scope at C:\Users\Fenmore\github\model-zoo\text\char-rnn\char-rnn.jl:91
[42] include_string(::Function, ::Module, ::String, ::String) at .\loading.jl:1088
in expression starting at C:\Users\Fenmore\github\model-zoo\text\char-rnn\char-rnn.jl:91
我想我不能简单地使用这些gpu()操作,但我不知道从哪里开始去弄清楚我还需要做什么。
谢谢你的帮助。
提前感谢。=) 你检查过这个问题吗?这似乎与你的问题有关
ERROR: LoadError: Mutating arrays is not supported
Stacktrace:
[1] error(::String) at .\error.jl:33
[2] (::Zygote.var"#455#456")(::Nothing) at C:\Users\Fenmore\.julia\packages\Zygote\rqvFi\src\lib\array.jl:68
[3] (::Zygote.var"#2384#back#457"{Zygote.var"#455#456"})(::Nothing) at C:\Users\Fenmore\.julia\packages\ZygoteRules\6nssF\src\adjoint.jl:49
[4] CuArray at C:\Users\Fenmore\.julia\packages\CUDA\dZvbp\src\array.jl:206 [inlined]
[5] CuArray at C:\Users\Fenmore\.julia\packages\CUDA\dZvbp\src\array.jl:211 [inlined]
[6] (::typeof(∂(CUDA.CuArray{Float32,N} where N)))(::CUDA.CuArray{Float32,2}) at C:\Users\Fenmore\.julia\packages\Zygote\rqvFi\src\compiler\interface2.jl:0
[7] LSTMCell at C:\Users\Fenmore\.julia\packages\Flux\05b38\src\cuda\curnn.jl:45 [inlined]
[8] (::typeof(∂(λ)))(::Tuple{Nothing,CUDA.CuArray{Float32,2}}) at C:\Users\Fenmore\.julia\packages\Zygote\rqvFi\src\compiler\interface2.jl:0
[9] (::Zygote.var"#177#178"{typeof(∂(λ)),Tuple{Tuple{Nothing},Tuple{Nothing}}})(::Tuple{Nothing,CUDA.CuArray{Float32,2}}) at C:\Users\Fenmore\.julia\packages\Zygote\rqvFi\src\lib\lib.jl:178
[10] (::Zygote.var"#1730#back#179"{Zygote.var"#177#178"{typeof(∂(λ)),Tuple{Tuple{Nothing},Tuple{Nothing}}}})(::Tuple{Nothing,CUDA.CuArray{Float32,2}}) at C:\Users\Fenmore\.julia\packages\ZygoteRules\6nssF\src\adjoint.jl:49
[11] Recur at C:\Users\Fenmore\.julia\packages\Flux\05b38\src\layers\recurrent.jl:36 [inlined]
[12] (::typeof(∂(λ)))(::CUDA.CuArray{Float32,2}) at C:\Users\Fenmore\.julia\packages\Zygote\rqvFi\src\compiler\interface2.jl:0
[13] applychain at C:\Users\Fenmore\.julia\packages\Flux\05b38\src\layers\basic.jl:36 [inlined]
[14] (::typeof(∂(applychain)))(::CUDA.CuArray{Float32,2}) at C:\Users\Fenmore\.julia\packages\Zygote\rqvFi\src\compiler\interface2.jl:0
[15] Chain at C:\Users\Fenmore\.julia\packages\Flux\05b38\src\layers\basic.jl:38 [inlined]
[16] (::typeof(∂(λ)))(::CUDA.CuArray{Float32,2}) at C:\Users\Fenmore\.julia\packages\Zygote\rqvFi\src\compiler\interface2.jl:0
[17] #1157 at C:\Users\Fenmore\.julia\packages\Zygote\rqvFi\src\lib\broadcast.jl:142 [inlined]
[18] (::Base.var"#3#4"{Zygote.var"#1157#1164"})(::Tuple{typeof(∂(λ)),CUDA.CuArray{Float32,2}}) at .\generator.jl:36
[19] iterate at .\generator.jl:47 [inlined]
[20] collect(::Base.Generator{Base.Iterators.Zip{Tuple{Array{typeof(∂(λ)),1},Array{CUDA.CuArray{Float32,2},1}}},Base.var"#3#4"{Zygote.var"#1157#1164"}}) at .\array.jl:686
[21] map at .\abstractarray.jl:2248 [inlined]
[22] (::Zygote.var"#1156#1163"{Tuple{Array{Flux.OneHotMatrix{CUDA.CuArray{Flux.OneHotVector,1}},1}},Val{2},Array{typeof(∂(λ)),1}})(::Array{CUDA.CuArray{Float32,2},1}) at C:\Users\Fenmore\.julia\packages\Zygote\rqvFi\src\lib\broadcast.jl:142
[23] #3985#back at C:\Users\Fenmore\.julia\packages\ZygoteRules\6nssF\src\adjoint.jl:49 [inlined]
[24] (::Zygote.var"#177#178"{Zygote.var"#3985#back#1167"{Zygote.var"#1156#1163"{Tuple{Array{Flux.OneHotMatrix{CUDA.CuArray{Flux.OneHotVector,1}},1}},Val{2},Array{typeof(∂(λ)),1}}},Tuple{Tuple{Nothing,Nothing,Nothing},Tuple{}}})(::Array{CUDA.CuArray{Float32,2},1}) at C:\Users\Fenmore\.julia\packages\Zygote\rqvFi\src\lib\lib.jl:178
[25] #1730#back at C:\Users\Fenmore\.julia\packages\ZygoteRules\6nssF\src\adjoint.jl:49 [inlined]
[26] broadcasted at .\broadcast.jl:1257 [inlined]
[27] (::typeof(∂(broadcasted)))(::Array{CUDA.CuArray{Float32,2},1}) at C:\Users\Fenmore\.julia\packages\Zygote\rqvFi\src\compiler\interface2.jl:0
[28] loss at C:\Users\Fenmore\github\model-zoo\text\char-rnn\char-rnn.jl:60 [inlined]
[29] (::typeof(∂(λ)))(::Float32) at C:\Users\Fenmore\.julia\packages\Zygote\rqvFi\src\compiler\interface2.jl:0
[30] #177 at C:\Users\Fenmore\.julia\packages\Zygote\rqvFi\src\lib\lib.jl:178 [inlined]
[31] #1730#back at C:\Users\Fenmore\.julia\packages\ZygoteRules\6nssF\src\adjoint.jl:49 [inlined]
[32] #14 at C:\Users\Fenmore\.julia\packages\Flux\05b38\src\optimise\train.jl:83 [inlined]
[33] (::typeof(∂(λ)))(::Float32) at C:\Users\Fenmore\.julia\packages\Zygote\rqvFi\src\compiler\interface2.jl:0
[34] (::Zygote.var"#54#55"{Zygote.Params,Zygote.Context,typeof(∂(λ))})(::Float32) at C:\Users\Fenmore\.julia\packages\Zygote\rqvFi\src\compiler\interface.jl:177
[35] gradient(::Function, ::Zygote.Params) at C:\Users\Fenmore\.julia\packages\Zygote\rqvFi\src\compiler\interface.jl:54
[36] macro expansion at C:\Users\Fenmore\.julia\packages\Flux\05b38\src\optimise\train.jl:82 [inlined]
[37] macro expansion at C:\Users\Fenmore\.julia\packages\Juno\hEPx8\src\progress.jl:119 [inlined]
[38] train!(::Function, ::Zygote.Params, ::Base.Iterators.Zip{Tuple{Array{Array{Flux.OneHotMatrix{CUDA.CuArray{Flux.OneHotVector,1}},1},1},Array{Array{Flux.OneHotMatrix{CUDA.CuArray{Flux.OneHotVector,1}},1},1}}}, ::ADAM; cb::Flux.var"#throttled#42"{Flux.var"#throttled#38#43"{Bool,Bool,var"#25#27"{var"#loss#26",Array{Flux.OneHotMatrix{CUDA.CuArray{Flux.OneHotVector,1}},1},Array{Flux.OneHotMatrix{CUDA.CuArray{Flux.OneHotVector,1}},1}},Int64}}) at C:\Users\Fenmore\.julia\packages\Flux\05b38\src\optimise\train.jl:80
[39] train(; kws::Base.Iterators.Pairs{Union{},Union{},Tuple{},NamedTuple{(),Tuple{}}}) at C:\Users\Fenmore\github\model-zoo\text\char-rnn\char-rnn.jl:69
[40] train() at C:\Users\Fenmore\github\model-zoo\text\char-rnn\char-rnn.jl:47
[41] top-level scope at C:\Users\Fenmore\github\model-zoo\text\char-rnn\char-rnn.jl:91
[42] include_string(::Function, ::Module, ::String, ::String) at .\loading.jl:1088
in expression starting at C:\Users\Fenmore\github\model-zoo\text\char-rnn\char-rnn.jl:91