[ANN] MXNet.jl - Flexible and Efficient Deep Learning for Julia

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[ANN] MXNet.jl - Flexible and Efficient Deep Learning for Julia

Chiyuan Zhang

MXNet.jl is the dmlc/mxnet Julia package. MXNet.jl brings flexible and efficient GPU computing and state-of-art deep learning to Julia. Some highlight of 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 exmple of how training a simple 3-layer MLP on MNIST looks like:

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)  =>

# data provider
batch_size = 100
include(joinpath(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())

# optimizer
optimizer = mx.SGD(lr=0.1, momentum=0.9, weight_decay=0.00001)

# fit parameters
mx.fit(model, optimizer, train_provider, n_epoch=20, eval_data=eval_provider)

For more details, please refer to the document and examples.


- pluskid

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