TastyPancakes/Flux.jl
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update mnist example

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This commit is contained in:
Mike J Innes 2017-02-02 10:09:41 +05:30
parent f932f4bd9f
commit f3a9934858
3 changed files with 5 additions and 4 deletions
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4
examples/MNIST.jl
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@ -1,6 +1,6 @@
using Flux, MNIST using Flux, MNIST
data = [(Vector{Float32}(trainfeatures(i)), onehot(Float32, 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]
@ -16,7 +16,7 @@ model = tf(m)
# An example prediction pre-training # An example prediction pre-training
model(data[1][1]) model(data[1][1])
@time Flux.train!(model, train, test, η = 1e-3) @time Flux.train!(model, train, test, η = 1e-4)
# An example prediction post-training # An example prediction post-training
model(data[1][1]) model(data[1][1])

3
src/backend/tensorflow/model.jl
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@ -80,7 +80,8 @@ function Flux.train!(m::Model, train, test=[]; epoch = 1, η = 0.1,
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.output, Loss, minimize_op), y, cur_loss, _ = run(m.session, vcat(m.output, Loss, minimize_op),
Dict(m.inputs[1]=>batchone(x), Y=>batchone(y))) Dict(m.inputs[1] => batchone(convertel(Float32, x)),
Y => batchone(convertel(Float32, y))))
if i % 5000 == 0 if i % 5000 == 0
@show y @show y
@show accuracy(m, test) @show accuracy(m, test)

2
src/utils.jl
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@ -2,7 +2,7 @@ export AArray
const AArray = AbstractArray const AArray = AbstractArray
initn(dims...) = randn(dims...)/10 initn(dims...) = randn(dims...)/100
function train!(m, train, test = []; epoch = 1, batch = 10, η = 0.1) function train!(m, train, test = []; epoch = 1, batch = 10, η = 0.1)
i = 0 i = 0