First, install pytorch.
If no GPU is available, just run
python3 run_fashion.py --model=cnn --batch-size=256 --epochs=100 --lr=0.001 --step-size=25 --no-cuda --cpu-binarization=1.
For faster binarization, specialized CUDA kernel support is needed. To enable it, install pybind11 and CUDA toolkit.
Then, to install CUDA-kernels for fast binarization, go to folder code/cuda/binarizationPM1 and run
python3 setup.py install --user
After successful installation, run the GPU-based training with
python3 run_fashion.py --model=cnn --batch-size=256 --epochs=10 --lr=0.001 --step-size=25.
A small convolutional neural network (CNN) is used by default. To try the fully connected case, use --model=fc instead of --model=cnn in the arguments.
The code is based on the MNIST example in https://github.com/pytorch/examples/tree/master/mnist.
Please contact me if you have any questions: mikail.yayla@tu-dortmund.de.