[GitHub] aaronmarkham commented on a change in pull request #10149: import Julia binding

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aaronmarkham commented on a change in pull request #10149: import Julia binding
URL: https://github.com/apache/incubator-mxnet/pull/10149#discussion_r218895341
 
 

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 File path: julia/README.md
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+# MXNet
+
+[![MXNet](http://pkg.julialang.org/badges/MXNet_0.6.svg)](http://pkg.julialang.org/?pkg=MXNet)
+
+
+MXNet.jl is the [dmlc/mxnet](https://github.com/dmlc/mxnet) [Julia](http://julialang.org/) package. MXNet.jl brings flexible and efficient GPU computing and state-of-art deep learning to Julia. Some highlight of its features include:
+
+* Efficient tensor/matrix computation across multiple devices, including multiple CPUs, GPUs and distributed server nodes.
+* Flexible symbolic manipulation to composite and construct state-of-the-art deep learning models.
+
+Here is an example of how training a simple 3-layer MLP on MNIST looks like:
+
+```julia
+using MXNet
+
+mlp = @mx.chain mx.Variable(:data)             =>
+  mx.FullyConnected(name=:fc1, num_hidden=128) =>
+  mx.Activation(name=:relu1, act_type=:relu)   =>
+  mx.FullyConnected(name=:fc2, num_hidden=64)  =>
+  mx.Activation(name=:relu2, act_type=:relu)   =>
+  mx.FullyConnected(name=:fc3, num_hidden=10)  =>
+  mx.SoftmaxOutput(name=:softmax)
+
+# data provider
+batch_size = 100
+include(Pkg.dir("MXNet", "examples", "mnist", "mnist-data.jl"))
+train_provider, eval_provider = get_mnist_providers(batch_size)
+
+# setup model
+model = mx.FeedForward(mlp, context=mx.cpu())
+
+# optimization algorithm
+# where η is learning rate and μ is momentum
+optimizer = mx.SGD(η=0.1, μ=0.9)
+
+# fit parameters
+mx.fit(model, optimizer, train_provider, n_epoch=20, eval_data=eval_provider)
+```
+
+You can also predict using the `model` in the following way:
+
+```julia
+probs = mx.predict(model, eval_provider)
+
+# collect all labels from eval data
+labels = reduce(
+  vcat,
+  copy(mx.get(eval_provider, batch, :softmax_label)) for batch ∈ eval_provider)
+# labels are 0...9
+labels .= labels .+ 1
+
+# Now we use compute the accuracy
+pred = map(i -> indmax(probs[1:10, i]), 1:size(probs, 2))
+correct = sum(pred .== labels)
+accuracy = 100correct/length(labels)
+@printf "Accuracy on eval set: %.2f%%\n" accuracy
+```
+
+For more details, please refer to the
+[documentation](https://dmlc.github.io/MXNet.jl/latest) and [examples](examples).
 
 Review comment:
   I don't really see anything for Julia in the examples folder. One search comes up but when you look at the article it doesn't mention Julia at all. 
   
   There are two tutorials on mxnet.jl:
   * http://dmlc.ml/MXNet.jl/latest/tutorial/mnist/
   * http://dmlc.ml/MXNet.jl/latest/tutorial/char-lstm/
   
   If these are tested and good to go, can you add them to the tutorials index page? Or I can. Just LMK if they're both working. I'd switch the link to examples here, and then point to the Julia section (that will exist once added) on the tutorials index page.

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