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Simple MNIST · Flux
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Recognising MNIST Digits
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< p >
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This walkthrough example will take you through writing a multi-layer perceptron that classifies MNIST digits with high accuracy.
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First, we load the data using the MNIST package:
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< pre > < code class = "language-julia" > using Flux, MNIST
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using Flux: accuracy
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data = [(trainfeatures(i), onehot(trainlabel(i), 0:9)) for i = 1:60_000]
train = data[1:50_000]
test = data[50_001:60_000]< / code > < / pre >
< p >
The only Flux-specific function here is
< code > onehot< / code >
, which takes a class label and turns it into a one-hot-encoded vector that we can use for training. For example:
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< pre > < code class = "language-julia" > julia> onehot(:b, [:a, :b, :c])
3-element Array{Int64,1}:
0
1
0< / code > < / pre >
< p >
Otherwise, the format of the data is simple enough, it' s just a list of tuples from input to output. For example:
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< pre > < code class = "language-julia" > julia> data[1]
([0.0,0.0,0.0, … 0.0,0.0,0.0],[0,0,0,0,0,1,0,0,0,0])< / code > < / pre >
< p >
< code > data[1][1]< / code >
is a
< code > 28*28 == 784< / code >
length vector (mostly zeros due to the black background) and
< code > data[1][2]< / code >
is its classification.
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< p >
Now we define our model, which will simply be a function from one to the other.
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< pre > < code class = "language-julia" > m = @Chain(
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Input(784),
Affine(128), relu,
Affine( 64), relu,
Affine( 10), softmax)
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model = mxnet(m) # Convert to MXNet< / code > < / pre >
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< p >
We can try this out on our data already:
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< pre > < code class = "language-julia" > julia> model(tobatch(data[1][1]))
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10-element Array{Float64,1}:
0.10614
0.0850447
0.101474
...< / code > < / pre >
< p >
The model gives a probability of about 0.1 to each class – " I have no idea" . This isn' t too surprising as we haven' t shown it any data yet. This is easy to fix:
< / p >
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< pre > < code class = "language-julia" > Flux.train!(model, train, η = 1e-3,
cb = [()-> @show accuracy(m, test)])< / code > < / pre >
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< p >
The training step takes about 5 minutes (to make it faster we can do smarter things like batching). If you run this code in Juno, you' ll see a progress meter, which you can hover over to see the remaining computation time.
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< p >
Towards the end of the training process, Flux will have reported that the accuracy of the model is now about 90%. We can try it on our data again:
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< pre > < code class = "language-julia" > 10-element Array{Float32,1}:
...
5.11423f-7
0.9354
3.1033f-5
0.000127077
...< / code > < / pre >
< p >
Notice the class at 93%, suggesting our model is very confident about this image. We can use
< code > onecold< / code >
to compare the true and predicted classes:
< / p >
< pre > < code class = "language-julia" > julia> onecold(data[1][2], 0:9)
5
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julia> onecold(model(tobatch(data[1][1])), 0:9)
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5< / code > < / pre >
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Success!
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