Type of c.outputs is Vector{Float64}在 2015年2月22日星期日 UTC+8上午12:39:53，[email protected]写道： > > What's the type of c.outputs? In train_one it seems to be Int64, in > prdict! it seems to be Float64. > > On Thursday, February 19, 2015 at 3:51:20 PM UTC+1, Zhixuan Yang wrote: >> >> Hello everyone, >> >> Recently I'm working on my first Julia project, a word embedding training >> program similar to Google's word2vec >> <https://code.google.com/p/word2vec/> (the code of word2vec is indeed >> very high-quality, but I want to add more features, so I decided to write a >> new one). Thanks to Julia's expressiveness, it cost me less than 2 days to >> write the entire program. But it runs really slow, about 100x slower than >> the C code of word2vec (the algorithm is the same). I've been trying to >> optimize my code for several days (adding type annotations, using BLAS to >> do computation, eliminating memory allocations ...), but it is still 30x >> slower than the C code. >> >> The critical part of my program is the following function (it also >> consumes most of the time according to the profiling result): >> >> function train_one(c :: LinearClassifier, x :: Array{Float64}, y :: >> Int64; α :: Float64 = 0.025, input_gradient :: Union(Nothing, >> Array{Float64}) = nothing) >> predict!(c, x) >> c.outputs[y] -= 1 >> >> if input_gradient != nothing >> # input_gradient = ( c.weights * outputs' )' >> BLAS.gemv!('N', α, c.weights, c.outputs, 1.0, input_gradient) >> end >> >> # c.weights -= α * x' * outputs; >> BLAS.ger!(-α, vec(x), c.outputs, c.weights) >> end >> >> function predict!(c :: LinearClassifier, x :: Array{Float64}) >> c.outputs = vec(softmax(x * c.weights)) >> end >> >> type LinearClassifier >> k :: Int64 # number of outputs >> n :: Int64 # number of inputs >> weights :: Array{Float64, 2} # k * n weight matrix >> >> outputs :: Vector{Float64} >> end >> >> And the entire program can be found here >> <https://github.com/yangzhixuan/embed>. Could you please check my code >> and tell me what I can do to get performance comparable to C. >> >> Regards. >> Yang Zhixuan >> >
