Hi, as I am just in the process of wrapping caffe, this looks really exiciting! I will definitely try this out in the coming days.
Are there any specific areas where you would like testing / feedback for now? Do you have an approximate feeling how the performance compares to caffe? Cheers, Rene Am 21.11.2014 um 03:00 schrieb Chiyuan Zhang <plus...@gmail.com>: > https://github.com/pluskid/Mocha.jl > Mocha is a Deep Learning framework for Julia, inspired by the C++ Deep > Learning framework Caffe. Since this is the first time I post announcement > here, change logs of the last two releases are listed: > > v0.0.2 2014.11.20 > > • Infrastructure > • Ability to import caffe trained model > • Properly release all the allocated resources upon backend > shutdown > • Network > • Sigmoid activation function > • Power, Split, Element-wise layers > • Local Response Normalization layer > • Channel Pooling layer > • Dropout Layer > • Documentation > • Complete MNIST demo > • Complete CIFAR-10 demo > • Major part of User's Guide > v0.0.1 2014.11.13 > > • Backend > • Pure Julia CPU > • Julia + C++ Extension CPU > • CUDA + cuDNN GPU > • Infrastructure > • Evaluate on validation set during training > • Automaticly saving and recovering from snapshots > • Network > • Convolution layer, mean and max pooling layer, fully > connected layer, softmax loss layer > • ReLU activation function > • L2 Regularization > • Solver > • SGD with momentum > • Documentation > • Demo code of LeNet on MNIST > • Tutorial document on the MNIST demo (half finished) > > > Below is a copy of the README file: > ---------------------------------------------------------------- > > Mocha is a Deep Learning framework for Julia, inspired by the C++ Deep > Learning framework Caffe. Mocha support multiple backends: > > • Pure Julia CPU Backend: Implemented in pure Julia; Runs out of the > box without any external dependency; Reasonably fast on small models thanks > to Julia's LLVM-based just-in-time (JIT) compiler and Performance Annotations > that eliminate unnecessary bound checkings. > • CPU Backend with Native Extension: Some bottleneck computations > (Convolution and Pooling) have C++ implementations. When compiled and > enabled, could be faster than the pure Julia backend. > • CUDA + cuDNN: An interface to NVidia® cuDNN GPU accelerated deep > learning library. When run with CUDA GPU devices, could be much faster > depending on the size of the problem (e.g. on MNIST CUDA backend is roughly > 20 times faster than the pure Julia backend). > Installation > > To install the release version, simply run > > Pkg.add("Mocha") > > in Julia console. To install the latest development version, run the > following command instead: > > Pkg.clone("https://github.com/pluskid/Mocha.jl.git > ") > > Then you can run the built-in unit tests with > > Pkg.test("Mocha") > > to verify that everything is functioning properly on your machine. > > Hello World > > Please refer to the MNIST tutorial on how prepare the MNIST dataset for the > following example. The complete code for this example is located at > examples/mnist/mnist.jl. See below for detailed documentation of other > tutorials and user's guide. > > using > Mocha > > data > = HDF5DataLayer(name="train-data",source="train-data-list.txt",batch_size=64 > ) > conv > = > ConvolutionLayer(name="conv1",n_filter=20,kernel=(5,5),bottoms=[:data],tops=[:conv > ]) > pool > = > PoolingLayer(name="pool1",kernel=(2,2),stride=(2,2),bottoms=[:conv],tops=[:pool > ]) > conv2 > = > ConvolutionLayer(name="conv2",n_filter=50,kernel=(5,5),bottoms=[:pool],tops=[:conv2 > ]) > pool2 > = > PoolingLayer(name="pool2",kernel=(2,2),stride=(2,2),bottoms=[:conv2],tops=[:pool2] > ) > fc1 > = > InnerProductLayer(name="ip1",output_dim=500,neuron=Neurons.ReLU(),bottoms=[:pool2 > ], > tops > =[:ip1 > ]) > fc2 > = InnerProductLayer(name="ip2",output_dim=10,bottoms=[:ip1],tops=[:ip2 > ]) > loss > = SoftmaxLossLayer(name="loss",bottoms=[:ip2,:label > ]) > > sys > = System(CuDNNBackend > ()) > > init > (sys) > > common_layers > = > [conv, pool, conv2, pool2, fc1, fc2] > net > = Net("MNIST-train", sys, [data, common_layers... > , loss]) > > params > = SolverParameters(max_iter=10000, regu_coef=0.0005, momentum=0.9 > , > lr_policy > =LRPolicy.Inv(0.01, 0.0001, 0.75 > )) > solver > = SGD > (params) > > > # report training progress every 100 iterations > add_coffee_break(solver, TrainingSummary(), every_n_iter=100 > ) > > > # save snapshots every 5000 iterations > add_coffee_break(solver, Snapshot("snapshots", auto_load=true > ), > every_n_iter > =5000 > ) > > > # show performance on test data every 1000 iterations > > data_test > = HDF5DataLayer(name="test-data",source="test-data-list.txt",batch_size=100 > ) > accuracy > = AccuracyLayer(name="test-accuracy",bottoms=[:ip2, :label > ]) > test_net > = Net("MNIST-test", sys, [data_test, common_layers... > , accuracy]) > > add_coffee_break(solver, ValidationPerformance(test_net), every_n_iter=1000 > ) > > > solve > (solver, net) > > > destroy > (net) > > destroy > (test_net) > > shutdown(sys) > Documentation > > The Mocha documentation is hosted on readthedocs.org.
