How does this compare to Mocha.jl?
Il giorno lunedì 26 ottobre 2015 04:27:31 UTC+1, Chiyuan Zhang ha scritto: > > 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 >
