aaronmarkham commented on a change in pull request #12737: [MXNET-1017] Updating the readme file for cpp-package and adding readme file for example directory. URL: https://github.com/apache/incubator-mxnet/pull/12737#discussion_r223780224
########## File path: cpp-package/example/README.md ########## @@ -0,0 +1,112 @@ +# MXNet C++ Package Examples + +## Building C++ examples + +The examples are built while building the MXNet library and cpp-package from source . However, they can be built manually as follows + +From cpp-package/examples directory + +- Build all examples in release mode: **make all** +- Build all examples in debug mode: **make debug** + +By default, the examples are build to be run on GPU. To build examples to run on CPU: + +- Release: **make all MXNET\_USE\_CPU=1** +- Debug: **make debug MXNET\_USE\_CPU=1** + +The examples that are build to be run on GPU may not work on the non-GPU machines. +The makefile will also download the necessary data files and store in data folder. (The download will take couple of minutes, but will be done only once on a fresh installation.) + + +## Examples + +This directory contains following examples. In order to run the examples, ensure that the path to the MXNet shared library is added to the OS specific environment variable such as _LD\_LIBRARY\_PATH_ . + +### [alexnet.cpp](<https://github.com/apache/incubator-mxnet/blob/master/cpp-package/example/alexnet.cpp>) + +The example implements C++ version of AlexNet. The networks trains the MNIST data. The number of epochs can be specified as command line arguement. For example: + ``` + ./alexnet 10 + ``` + +### [charRNN.cpp](<https://github.com/apache/incubator-mxnet/blob/master/cpp-package/example/chaRNN.cpp>) + +The code implements C++ version charRNN for mxnet\example\rnn\char-rnn.ipynb with MXNet.cpp API. The generated params file is compatiable with python version. The train() and predict() has been verified with original data samples. + +The example expects arguments as follows: + +``` + ./charRNN train [BuildIn\ [TImeMajor] {corpus file} { batch size} { max epoch} [{starting epoch}] + ./charRNN predict [BuildIn\ [TImeMajor] {param file} { batch size} { max epoch} [{starting epoch}] +``` + +### [googlenet.cpp](<https://github.com/apache/incubator-mxnet/blob/master/cpp-package/example/googlenet.cpp>) + +The code implements GoogLeNet/Inception network using C++ API. The example uses MNIST data to train the network. The number of epochs can be specified in the command line as follows. If not specified, the model trains for 100 epochs. + +``` +./googlenet 10 +``` + +### [mlp.cpp](<https://github.com/apache/incubator-mxnet/blob/master/cpp-package/example/mlp.cpp>) + +The code implements multilayer perceptron from scratch. The example creates its own dummy data to train the model. The example does not require command line parameters. It trains the model for 20000 iterations. + +``` +./mlp +``` + +### [mlp_cpu.cpp](<https://github.com/apache/incubator-mxnet/blob/master/cpp-package/example/mlp_cpu.cpp>) + +The code implements multilayer perceptron to train the MNIST data. The code demonstrates the use of "SimpleBind" C++ API and MNISTIter. The example is designed to work on CPU. The example does not require command line parameters. + +``` +./mlp_cpu +``` + +### [mlp_gpu.cpp](<https://github.com/apache/incubator-mxnet/blob/master/cpp-package/example/mlp_gpu.cpp>) +The code implements multilayer perceptron to train the MNIST data. The code demonstrates the use of "SimpleBind" C++ API and MNISTIter. The example is designed to work on GPU. The example does not require command line paratmeters. + +``` +./mlp_gpu +``` + +### [mlp_csv.cpp](<https://github.com/apache/incubator-mxnet/blob/master/cpp-package/example/mlp_csv.cpp>) +The code implements multilayer perceptron to train the MNIST data. The code demonstrates the use of "SimpleBind" C++ API and CSVIter. The CSVIter can iterate data that is in CSV format. The example can be run on CPU or GPU. The example usage is as follows: Review comment: implements a multilayer... of the "SimpleBind" ---------------------------------------------------------------- This is an automated message from the Apache Git Service. To respond to the message, please log on GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: [email protected] With regards, Apache Git Services
