TastyPancakes/Flux.jl
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Float32 by default

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This commit is contained in:
Mike J Innes 2016-10-04 22:36:56 +01:00
parent c646ba4483
commit bf04b70ad1
5 changed files with 7 additions and 7 deletions
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2
examples/MNIST.jl
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@ -1,6 +1,6 @@
using Flux, MNIST using Flux, MNIST
data = [(trainfeatures(i), Vector{Float64}(onehot(trainlabel(i), 0:9))) for i = 1:60_000] data = [(trainfeatures(i), Vector{Float32}(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]

4
examples/mnist-conv.jl
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@ -40,8 +40,8 @@ using TensorFlow
sess = Session(Graph()) sess = Session(Graph())
x = placeholder(Float64) x = placeholder(Float32)
y′ = placeholder(Float64) y′ = placeholder(Float32)
y = Tensor(lenet, x) y = Tensor(lenet, x)

4
src/backend/tensorflow/tensorflow.jl
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@ -65,7 +65,7 @@ Media.render(::Juno.Clipboard, ::Model) = "Flux.TF.Model()"
function tf(model) function tf(model)
sess = Session(Graph()) sess = Session(Graph())
input = placeholder(Float64) input = placeholder(Float32)
g = graph(model, input) g = graph(model, input)
run(sess, initialize_all_variables()) run(sess, initialize_all_variables())
Model(sess, [input], g, gradients(g, input)) Model(sess, [input], g, gradients(g, input))
@ -90,7 +90,7 @@ function Flux.train!(m::Model, train, test=[]; epoch = 1, η = 0.1,
loss = (y, y′) -> reduce_sum((y - y′).^2)/2, loss = (y, y′) -> reduce_sum((y - y′).^2)/2,
opt = TensorFlow.train.GradientDescentOptimizer(η)) opt = TensorFlow.train.GradientDescentOptimizer(η))
i = 0 i = 0
Y = placeholder(Float64) Y = placeholder(Float32)
Loss = loss(m.graph, Y) Loss = loss(m.graph, Y)
minimize_op = TensorFlow.train.minimize(opt, Loss) minimize_op = TensorFlow.train.minimize(opt, Loss)
for e in 1:epoch for e in 1:epoch

2
src/layers/shims.jl
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@ -1,7 +1,7 @@
export Conv2D, MaxPool export Conv2D, MaxPool
type Conv2D <: Model type Conv2D <: Model
filter::Param{Array{Float64,4}} # [height, width, outchans, inchans] filter::Param{Array{Float32,4}} # [height, width, outchans, inchans]
stride::Dims{2} stride::Dims{2}
end end

2
src/utils.jl
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@ -5,7 +5,7 @@ const AArray = AbstractArray
onehot(label, labels) = [i == label for i in labels] onehot(label, labels) = [i == label for i in labels]
onecold(pred, labels = 1:length(pred)) = labels[findfirst(pred, maximum(pred))] onecold(pred, labels = 1:length(pred)) = labels[findfirst(pred, maximum(pred))]
initn(dims...) = randn(dims...)/1000 initn(dims...) = randn(Float32, dims...)/1000
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