TastyPancakes/Flux.jl
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sort-of working mnist example

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This commit is contained in:
Mike J Innes 2016-09-29 21:28:53 +01:00
parent a2aade718d
commit 8335ab8134
2 changed files with 13 additions and 10 deletions
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10
examples/MNIST.jl
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@ -1,11 +1,8 @@
using Flux, MNIST using Flux, MNIST
@time begin data = [(trainfeatures(i), Vector{Float64}(onehot(trainlabel(i), 0:9))) for i = 1:60_000]
data = [(trainfeatures(i), onehot(trainlabel(i), 0:9)) for i = 1:60_000]
train = data[1:50_000] train = data[1:50_000]
test = data[50_001:60_000] test = data[50_001:60_000]
nothing
end
m = Chain( m = Chain(
Input(784), Input(784),
@ -13,6 +10,7 @@ m = Chain(
Dense( 64), relu, Dense( 64), relu,
Dense( 10), softmax) Dense( 10), softmax)
model = mxnet(m, 784) # Convert to TensorFlow
model = tf(m)
@time Flux.train!(model, train, test, epoch = 1, η=0.001) @time Flux.train!(model, train, test, η = 1e-3)

9
src/backend/tensorflow/tensorflow.jl
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@ -33,6 +33,9 @@ end
graph(::typeof(*), args...) = *(reverse(args)...) graph(::typeof(*), args...) = *(reverse(args)...)
graph(::typeof(+), args...) = +(args...) graph(::typeof(+), args...) = +(args...)
graph(::typeof(softmax), x) = nn.softmax(x) graph(::typeof(softmax), x) = nn.softmax(x)
graph(::typeof(relu), x) = nn.relu(x)
graph(::Input, x) = x
type Model type Model
session::Session session::Session
@ -73,12 +76,14 @@ function Flux.train!(m::Model, train, test=[]; epoch = 1, η = 0.1,
Y = placeholder(Float64) Y = placeholder(Float64)
Loss = loss(m.graph, Y) Loss = loss(m.graph, Y)
minimize_op = TensorFlow.train.minimize(opt, Loss) minimize_op = TensorFlow.train.minimize(opt, Loss)
run(m.session, initialize_all_variables())
for e in 1:epoch for e in 1:epoch
info("Epoch $e\n") info("Epoch $e\n")
@progress for (x, y) in train @progress for (x, y) in train
y, cur_loss, _ = run(m.session, vcat(m.graph, Loss, minimize_op), Dict(m.inputs[1]=>x', Y=>y')) y, cur_loss, _ = run(m.session, vcat(m.graph, Loss, minimize_op), Dict(m.inputs[1]=>x', Y=>y'))
i % 1000 == 0 && @show accuracy(m, test) if i % 5000 == 0
@show y
@show accuracy(m, test)
end
i += 1 i += 1
end end
end end