TensorSlow

Machine learning library implementing neural networks from scratch for a school project.

How to build ?

You can build the library with the following make commands :

Command	Effect	Output folder
make / make all	Build library, tests, benchmarks and examples	lib && bin
make lib	Build library	lib
make tests	Build tests	bin
make perf	Build benchmarks	bin
make examples	Build examples	bin

The library's .o and .so files are built in the lib directory. Other executables are built in the bin directory with an adequate suffix.

Note : Executables in the bin directory must be run from the root of the project to be able to find libtensorslow.so, or sometimes the examples data sets. As a general rule, always run binaries and scripts with the following command :

./bin/SOME_FILE

In order to clean the build directories, you can use the following phonies :

Command	Effect	Target folder
make clean	Clean everything	lib && bin
make clean_lib	Clean everything in lib	lib
make clean_o	Clean .o files in lib	lib
make clean_so	Clean .so files in lib	lib
make clean_bin	Clean everything in bin	bin
make clean_tests	Clean tests in bin	bin
make clean_perf	Clean benchmarks in bin	bin
make clean_examples	Clean examples in bin	bin

Examples

• Before running the MNIST example, you must download the MNIST dataset. You can do this by running the provided script (once again, from the root of this repository) :

  bash examples/get-mnist.sh

  This will create an examples/mnist folder containing the uncompressed dataset for both training and testing phases.

• The same goes for the CIFAR example :

  bash examples/get-cifar.sh

• Finally, the R-CNN example uses CIFAR for training, and Traffic-Net for predictions. To download this dataset :

  bash examples/get-trafficnet.sh

You can use the following arguments on all examples running a training phase :

./bin/your_example [N EPOCHS] [N BATCHES] [BATCH SIZE]

Architecture

To get an overview of the library's high level architecture, and a basic understanding of it, read the ARCHITECTURE.md file.

Documentation

The doc folder now contains the academic report and presentation of the project (in French).

Requirements

Some of these are not required by the core of the library, and are therefore optional :

• Eigen : your compiler should have access to the Eigen directory
• OpenMP : used to take advantage of Eigen's multi-threading
• GoogleTest : required to build the tests
• Benchmark : Google's benchmark library, required to build the benchmarks
• CImg : to read and write JPEGs in the prediction phase of the R-CNN example