updates for new broadcasting approach

This commit is contained in:
Mike J Innes 2017-05-24 12:02:03 +01:00
parent cb4d8cf9a6
commit 9909af72a4
4 changed files with 23 additions and 11 deletions

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@ -20,15 +20,20 @@ node(x::mx.SymbolicNode) = x
graph(::typeof(tuple), args...) = (args...,)
graph(::typeof(identity), x) = x
graph(::typeof(getindex), t::Tuple, n::Integer) = t[n]
graph(::typeof(.+), args...) = mx.broadcast_plus(args...)
graph(::typeof(.*), args...) = mx.broadcast_mul(args...)
graph(::typeof(.-), args...) = mx.broadcast_sub(args...)
graph(::typeof(*), xs...) = mx.dot(reverse(xs)...) # Work around MXNet shape hack
graph(::typeof(σ), x) = mx.Activation(x, act_type = :sigmoid)
graph(::typeof(relu), x) = mx.Activation(x, act_type = :relu)
graph(::typeof(tanh), x) = mx.Activation(x, act_type = :tanh)
graph(::typeof(flatten), x) = mx.Flatten(x)
graph(::typeof(broadcast), ::typeof(+), args...) = mx.broadcast_plus(args...)
graph(::typeof(broadcast), ::typeof(*), args...) = mx.broadcast_mul(args...)
graph(::typeof(broadcast), ::typeof(-), args...) = mx.broadcast_sub(args...)
# Old broadcasters
graph(::typeof(.+), args...) = mx.broadcast_plus(args...)
graph(::typeof(.*), args...) = mx.broadcast_mul(args...)
graph(::typeof(.-), args...) = mx.broadcast_sub(args...)
graph(::typeof(softmax), xs) =
mx.broadcast_div(exp(xs), mx.sum(exp(xs), axis = 1, keepdims=true))

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@ -34,6 +34,10 @@ for op in (*, .*, .+, .^, log, exp, ceil, floor, sqrt, abs, cos,
@eval graph(::typeof($op), args...) = $op(args...)
end
for op in (+, -, *, /)
@eval graph(::typeof(broadcast), ::typeof($op), args...) = broadcast($op, args...)
end
graph(::typeof(.-), args...) = -(args...)
# reshape hack due to https://github.com/malmaud/TensorFlow.jl/issues/79

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@ -17,13 +17,14 @@ end
ihint(f, ctx::Context, h::Hint, x) = vertex(h, x)
ihint(f, args...) = f(args...)
hintify(ctx, c::Constant) = hintify(ctx, state(c.value))
hintify(ctx, c::Constant{<:Union{Param,AbstractArray}}) = hintify(ctx, state(c.value))
hintify(ctx, xs::AbstractArray) = vertex(Hint(size(xs)), constant(:_))
hintify(ctx, c::Constant) = vertex(c)
interpshape = mux(ilinev, ihint, iargs, hintify)
function hintify(ctx, f, xs...)
sh = infer(f, map(gethint, xs)...)
sh = infer(f, gethint.(xs)...)
sh nothing ? vertex(Hint(sh), vertex(f, xs...)) :
!any(x->x==nothing, xs) && graph(f) nothing ? interpret(Context(interpshape), graph(f), xs...) :
vertex(f, xs...)
@ -50,6 +51,8 @@ function infer(::typeof(*), a::Dims{2}, b::Dims{2})
(a[1], b[2])
end
infer(::typeof(broadcast), f, xs::Dims...) = Base.Broadcast.broadcast_shape(xs...)
# Old broadcast versions
infer(::typeof(.+), xs::Dims...) = Base.Broadcast.broadcast_shape(xs...)
# Shapes macro

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@ -10,13 +10,13 @@ d = Affine(10, 20)
d1 = @net x -> x * d.W + d.b
@test d(xs) == d1(xs)
Flux.infer(d, (1, 10))
let
# In 0.6 `.+` evaluates to an anon function, so we must match on that.
@capture(syntax(d), _Frame(_Line(bplus_(x_[1] * W_, b_))))
@test isa(x, DataFlow.Input) && isa(W, Param) && isa(b, Param)
end
# Skip this before new DataFlow is released.
# let
# @test @capture(syntax(d), _Frame(_Line((+).(x_[1] * W_, b_))))
# @test isa(x, DataFlow.Input) && isa(W, Param) && isa(b, Param)
# end
let a1 = Affine(10, 20), a2 = Affine(20, 15)
tlp = TLP(a1, a2)