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 # Logistic Regression with MNIST
 
-[WIP]
+This walkthrough example will take you through writing a multi-layer perceptron that classifies MNIST digits with high accuracy.
+
+First, we load the data using the MNIST package:
+
+```julia
+using Flux, MNIST
+
+data = [(trainfeatures(i), onehot(trainlabel(i), 0:9)) for i = 1:60_000]
+train = data[1:50_000]
+test = data[50_001:60_000]
+```
+
+The only Flux-specific function here is `onehot`, which takes a class label and turns it into a one-hot-encoded vector that we can use for training. For example:
+
+```julia
+julia> onehot(:b, [:a, :b, :c])
+3-element Array{Int64,1}:
+ 0
+ 1
+ 0
+```
+
+Otherwise, the format of the data is simple enough, it's just a list of tuples from input to output. For example:
+
+```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])
+```
+
+`data[1][1]` is a `28*28 == 784` length vector (mostly zeros due to the black background) and `data[1][2]` is its classification.
+
+Now we define our model, which will simply be a function from one to the other.
+
+```julia
+m = Chain(
+  Input(784),
+  Affine(128), relu,
+  Affine( 64), relu,
+  Affine( 10), softmax)
+
+model = tf(model)
+```
+
+We can try this out on our data already:
+
+```julia
+julia> model(data[1][1])
+10-element Array{Float64,1}:
+ 0.10614  
+ 0.0850447
+ 0.101474
+ ...
+```
+
+The model gives a probability of about 0.1 to each class – which is a way of saying, "I have no idea". This isn't too surprising as we haven't shown it any data yet. This is easy to fix:
+
+```julia
+Flux.train!(model, train, test, η = 1e-4)
+```
+
+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.
+
+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:
+
+```julia
+10-element Array{Float32,1}:
+ ...
+ 5.11423f-7
+ 0.9354     
+ 3.1033f-5  
+ 0.000127077
+ ...
+```
+
+Notice the class at 93%, suggesting our model is very confident about this image. We can use `onecold` to compare the true and predicted classes:
+
+```julia
+julia> onecold(data[1][2], 0:9)
+5
+
+julia> onecold(model(data[1][1]), 0:9)
+5
+```
+
+Success!