Comments (4)
from knet.jl.
Thanks for the update! I kind of understand that CuArray
only works when nvidia GPU detected.
I don't understand why ytype
could NOT be set as CuArray
while xtype
could.
In a GPU available machine, there are no problem in making ytype
as CuArray
.
array_type = (CUDA.functional() ? CuArray{Float32} : Array{Float32})
dtrn = minibatch(xtrn, ytrn, 100; xsize = (784,:), shuffle = true, xtype = array_type, ytype = array_type);
dtst = minibatch(xtst, ytst, 100; xsize = (784,:), shuffle = true, xtype = array_type, ytype = array_type);
#- Note ytype is CuArray{Float32}
dtrn
# 600-element Data{Tuple{CuArray{Float32}, CuArray{Float32}}}
dtst
100-element Data{Tuple{CuArray{Float32}, CuArray{Float32}}}
model = Chain(Layer0(784, 64), Layer0(64, 10))
mlp1 = trainresults("mlp113a.jld2", model);
However, it raised error as in the first thread.
from knet.jl.
from knet.jl.
Thanks very much for the details! Actually my naive understanding was that all data should be stored as Cudarray to take adavantage of GPU computing. Maybe I should learn more of the Cudarray.
Thanks again for the excellent packages and documentations!
from knet.jl.
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