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 >
