MXNet.jl <https://github.com/dmlc/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 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) =>
mx.Softmax(name=:softmax)
# data provider
batch_size = 100include(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
<http://mxnetjl.readthedocs.org/> and examples
<https://github.com/dmlc/MXNet.jl/blob/master/examples>.
Enjoy!
- pluskid
