TastyPancakes/Flux.jl
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mx batch semantics

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This commit is contained in:
Mike J Innes 2017-01-30 23:35:15 +05:30
parent 16d6c9aed9
commit 1f3587e9dc
2 changed files with 6 additions and 2 deletions
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5
src/backend/mxnet/model.jl
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@ -1,3 +1,4 @@
using Flux: batchone, rebatch
type Model <: Flux.Model type Model <: Flux.Model
model::Any model::Any
@ -50,6 +51,10 @@ function runmodel(model::Model, input)
copy(model.exec.outputs[1]) copy(model.exec.outputs[1])
end end
(m::Model)(x::Batch) = rebatch(runmodel(m, rawbatch(x)))
(m::Model)(x) = first(m(batchone(x)))
function Flux.back!(model::Model, Δ, x) function Flux.back!(model::Model, Δ, x)
ndzero!(model.grads[:input]) ndzero!(model.grads[:input])
mx.backward(model.exec, tond(Δ)) mx.backward(model.exec, tond(Δ))

3
test/backend.jl
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@ -5,9 +5,8 @@ let dt = tf(d)
@test d(xs) ≈ dt(xs) @test d(xs) ≈ dt(xs)
end end
# TODO: batch semantics
let dm = mxnet(d, (1, 20)) let dm = mxnet(d, (1, 20))
@test d(xs)' ≈ dm(xs') @test d(xs) ≈ dm(xs)
end end
# TensorFlow native integration # TensorFlow native integration